106 results on '"Danti, G"'
Search Results
2. Are all people with diabetes and cardiovascular risk factors or microvascular complications at very high risk? Findings from the Risk and Prevention Study
- Author
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Marzona, Irene, Avanzini, Fausto, Lucisano, Giuseppe, Tettamanti, Mauro, Baviera, Marta, Nicolucci, Antonio, Roncaglioni, Maria Carla, Tombesi, M., Tognoni, G., Massa, E., Marrocco, W., Micalella, M., Caimi, V., Longoni, P., Avanzini, F., Franzosi, M. G., Roncaglioni, M. C., Marzona, I., Baviera, M., Monesi, L., Pangrazzi, I., Barlera, S., Milani, V., Nicolis, E., Casola, C., Clerici, F., Palumbo, A., Sgaroni, G., Marchioli, R., Silletta, M. G., Pioggiarella, R., Scarano, M., Marfisi, R. M., Flamminio, A., Macino, L., Ferri, B., Pera, C., Polidoro, A., Abbatino, D., Acquati, M., Addorisio, G., Adinolfi, D., Adreani, L., Agistri, M. R., Agneta, A., Agnolio, M. L., Agostini, N., Agostino, G., Airò, A., Alaimo, N., Albano, M., Albano, N., Alecci, G., Alemanno, S., Alexanian, A., Alfarano, M., Alfè, L., Alonzo, N., Alvino, S., Ancora, A., Andiloro, S., Andreatta, E., Angeli, S., Angiari, F., Angilletti, V., Annicchiarico, C., Anzivino, M., Aprea, R., Aprile, A., Aprile, E., Aprile, I., Aprile, L., Armellani, V., Arnetoli, M., Aronica, A., Autiero, V., Bacca, G., Baccalaro, A. M., Bacci, M., Baglio, G., Bagnani, M., Baiano, A., Baldari, A., Ballarini, L., Banchi, G., Bandera, R., Bandini, F., Baratella, M., Barbieri, A., Barbieri Vita, A., Bardi, M., Barlocchi, M., Baron, P., Bartoli, M., Basile, A., Basile, F., Basile, S., Battaggia, A., Battaglia, A., Baù, A., Beconcini, G., Beggio, R., Belfiore, P. A., Belicchi, M., Bellamoli, S., Bellini, C., Bellomo, M., Benetollo, C., Benetti, R., Beretta, E., Bertalero, P., Bertaso, F. G., Bertolani, U., Bettelli, G., Biagiotti, G., Bianchi, S., Bianco, G., Biccari, F., Bigioli, F., Bindi, M., Bisanti, G., Bitetti, E. M., Blasetti, M. P., Blesi, F., Boato, V., Boga, S., Boidi, E., Boldrin, G., Bollati, A., Bolzan, L., Bolzonella, S., Bonardi, P., Bonato, G. B., Bonci, M., Bonfitto, G., Bonincontro, E., Boninsegna, F., Bonissone, D., Bono, L., Bonollo, E., Borghi, M., Borioli, N., Borsatto, M., Bosco, T., Bosisio Pioltelli, M., Botarelli, C., Botassis, S., Bottini, F., Bottos, C., Bova, G., Bova, V., Bozzani, A., Bozzetto, R. M., Braga, V. T., Braglia, M., Bramati, E., Brazzoli, C., Breglia, G., Brescia, A., Briganti, D., Brigato, G., Brocchi, A., Brosio, F. A., Bruni, E., Buscaglia, E., Bussini, M. D., Bussotti, A., Buzzaccarini, F., Buzzatti, A., Caccamo, G., Cacciavillani, C., Caggiano, G., Caimi, V., Calciano, F. P., Calderisi, M., Calienno, S., Caltagirone, P., Calzolari, I., Cammisa, M., Campanaro, M., Campanella, G. B., Campese, F., Canali, G., Candiani, D. E. L., Canepa, R., Canini, D., Canino, A., Cantoro, E. A., Capilupi, V., Capotosto, P., Cappelli, B., Capraro, G., Carafa, F. A., Carano, Q., Carcaterra, V., Carriero, D., Carrozzo, G., Cartanese, M., Casalena, M., Casarola, M., Caso, C., Casotto, M., Castaldi, F., Castegnaro, R., Castellani, G., Castri, S., Catalano, E., Catinello, N., Caturano, G., Cavallaro, R., Cavallo, A. M., Cavallo, G., Cavion, M. T., Cavirani, G., Cazzaniga, F., Cazzetta, D., Cecconi, V., Cefalo, A., Celebrano, M., Celora, A., Centonze, P., Cerati, D., Cesaretti, D., Checchia, G., Checchin, A., Cherubini, M., Chianese, L., Chiappa, A., Chiappa, M. V., Chiariello, G., Chiavini, G., Chicco, M., Chiumeo, F., Ciacciarelli, A., Ciaci, D., Ciancaglini, R., Cicale, C., Cicale, S., Cipolla, A., Ciruolo, A., Citeri, A. L., Citterio, G., Clerici, M., Coazzoli, E., Collecchia, G., Colletta, F., Colombo, I., Colorio, P., Coluccia, S., Comerio, M., Comoretto, P., Compagni, M., Conte, O., Contri, S., Contrisciani, A., Coppetti, T., Corasaniti, F., Corradi, M. T., Corsano, A., Corsini, A., Corti, N., Costantini, G., Costantino, A., Cotroneo, S., Cozzi, D., Cravello, M. G., Cristiano, E., Cucchi, R., Cusmai, L., D’Errico, G. B., D’Agostino, P., Dal Bianco, L., Dal Mutto, U., Dal Pozzo, G., Dallapiccola, P., Dallatorre, G., Dalle Molle, G., Dalloni, E., D’Aloiso, A., D’Amicis, G., Danese, R., Danieli, D., Danisi, G., D’Anna, M. A., Danti, G., D’Ascanio, S., Davidde, G., De Angeli, D., De Bastiani, R., De Battisti, A., De Bellis, A., De Berardinis, G., De Carlo, F., De Giorgi, D., De Gobbi, R., De Lorenzis, E., De Luca, P., De Martini, G., De Marzi, M., De Matteis, D., De Padova, S., De Polo, P., De Sabato, N., De Stefano, T., De Vita, M. T., De Vito, U., De Zolt, V., Debernardi, F., Del Carlo, A., Del Re, G., Del Zotti, F., D’Elia, R., Della Giovanna, P., Dell’Acqua, L., Dell’Orco, R. L., Demaria, G., Di Benedetto, M. G., Di Chiara, G., Di Corcia, V., Di Domizio, O., Di Donato, P., Di Donato, S., Di Fermo, G., Di Franco, M., Di Giovannantonio, G., Di Lascio, G., Di Lecce, G., Di Lorenzo, N., Di Maro, T., Di Mattia, Q., Di Michele, E., Di Modica, R. S., Di Murro, D., Di Noi, M. C., Di Paoli, V., Di Santi, M., Di Sanzo, A., Di Turi, C., Diazzi, A., Dileo, I., D’Ingianna, A. P., Dolci, A., Donà, G., Donato, C., Donato, P., Donini, A., Donna, M. E., Donvito, T. V., Esposito, L., Esposito, N., Evangelista, M., Faita, G., Falco, M., Falcone, D. A., Falorni, F., Fanciullacci, A., Fanton, L., Fasolo, L., Fassina, R., Fassone, A., Fatarella, P., Fedele, F., Fera, I., Fera, L., Ferioli, S., Ferlini, M. G., Ferlino, R., Ferrante, G., Ferrara, F. N., Ferrarese, M. F., Ferrari, G., Ferrari, O., Ferreri, A., Ferroni, M., Fezzi, G., Figaroli, C., Fina, M. G., Fioretta, A., Fiorucci, C., Firrincieli, R., Fischetti, M., Fischietti, G., Fiume, D. C., Flecchia, G., Forastiere, G., Fossati, B., Franceschi, P. L., Franchi, L., Franzoso, F., Frapporti, G., Frasca, G., Frisotti, A., Fumagalli, G., Fusco, D., Gabriele, P., Gabrieli, A., Gagliano, D., Galimberti, G., Galli, A., Gallicchio, N., Gallio, F., Gallipoli, T., Gallo, P., Galopin, T., Gambarelli, L., Garbin, A., Garozzo, G. M., Gasparri, R., Gastaldo, M., Gatti, E., Gazzaniga, P., Gennachi, N., Gentile, R. V., Germani, P., Gesualdi, F., Gherardi, E., Ghezzi, C., Ghidini, M. G., Ghionda, F., Giacci, L., Gialdini, D., Giampaolo, C., Giancane, R., Giannanti, A., Giannese, S., Giannini, L., Giaretta, M., Giaretta, R., Giavardi, L., Giordano, P., Giordano, E., Giordano, B., Gioria, G. M., Giugliano, R., Grassi, E. A., Greco, A., Greco, L., Grilletti, N., Grimaldi, N., Grisetti, G., Groppelli, G., Gualtieri, L., Guarducci, M., Guastella, G., Guerra, M., Guerrini, F., Guglielmini, A., Guido, A., Gulotta, P., Iacono, E., Iadarola, G., Ianiro, G., Iarussi, V., Ieluzzi, M. L., Ierardi, C., Ingaldi, F., Interlandi, S., Iocca, M., Iorno, A., Ioverno, E., Iurato, R., La Pace, L., La Piscopia, C., La Selva, R., Lafratta, M., Lamparelli, M., Lanaro, G., Lancerotto, R., Larcher, M., Lassandro, M., Lattuada, G., Laurino, P., Lefons, C., Legrottaglie, F., Lemma, A., Leone, D., Leone, F., Leso, A., Leuzzi, G., Levato, G., Libardi, L., Libralesso, N., Licini, P. I., Licursi, G., Lidonnici, F., Lillo, C., Liveri, L., Livio, A., Loiero, R. A., Loison, M., Lombardo, G., Lombardo, T., Lomunno, V., Lomuscio, S., Lonedo, A., Longo, E., Longoni, P., Lora, L., Lotterio, A., Lucatello, L., Luongo, A., Lupoli, M., Macchia, C., Macri, G., Mafessanti, M., Maggialetti, V., Maggioni, A., Magnani, M., Maiellaro, G., Mancuso, A., Maniglio, A. R., Mannari, G. L., Manni, A., Manocchio, B., Mao, M., Maranò, A., Maraone, E., Marascio, D., Marcheselli, P., Marchetto, B., Marchetto, S., Marchi, A., Marchi, G. L., Mariano, C., Marinacci, S., Marinelli, S., Marini, G., Marra, V. C., Marrali, F., Marseglia, C., Martello, G., Martino, C., Martino, G., Martino, M., Marulli, C. F., Maruzzi, G., Marzotti, A., Mascheroni, G., Mascolo, P., Masoch, G., Masone, R., Massa, E., Massa, L., Massafra, M., Massi, M., Massignani, D. M., Matarese, A. M., Matini, G., Mauro, R., Mazzi, M., Mazzillo, A., Mazzocato, E., Mazzoleni, N. S., Mazzone, A., Melacci, A., Mele, E., Meliota, P., Menaspà, S., Meneghello, F., Merola, G., Merone, L., Metrucci, A., Mezzina, V., Micchi, A., Michielon, A., Migliore, N., Minero, G., Minotta, F., Mirandola, C., Mistrorigo, S., Modafferi, L., Moitre, R., Mola, E., Monachese, C., Mongiardini, C., Montagna, F., Montani, M., Montemurno, I., Montolli, R., Montorsi, S., Montresor, M., Monzani, M. G., Morabito, F., Mori, G., Moro, A., Mosca, M. F., Motti, F., Muddolon, L., Mugnai, M., Muscas, F., Naimoli, F., Nanci, G., Nargi, E., Nasorri, R., Nastrini, G., Negossi, M., Negrini, A., Negroni, A., Neola, V., Niccolini, F., Niro, C. M., Nosengo, C., Novella, G., Nuti, C., Obici, F., Olita, C., Oliverio, S. S., Olivieri, I., Oriente, S., Orlando, G., Paci, C., Pagano, G., Pagliara, C., Paita, G., Paladini, G., Paladino, G., Palano, T., Palatella, A., Palermo, P., Palmisano, M., Pando, P., Panessa, P., Panigo, F., Panozzo, G., Panvini, F., Panzieri, F., Panzino, A., Panzitta, F., Paoli, N., Papagna, R., Papaleo, M. G., Papalia, G., Parisi, R., Parotti, N., Parravicini, D., Passarella, P., Pastore, G. A., Patafio, M., Pavone, P., Pedroli, W., Pedroni, M., Pelligra, G., Pellizzari, M., Penati, A., Perlot, M., Perrone, A., Perrone, G., Peruzzi, P., Peselli, C., Petracchini, L., Petrera, L., Petrone, S., Peverelli, C., Pianorsi, F., Piazza, G. P., Piazzolla, G., Picci, A., Pienabarca, G., Pietronigro, T. P., Pignocchino, P., Pilone, R., Pinto, D., Pirovano, E., Pirrotta, D., Pisante, V., Pitotto, P., Pittari, L., Piva, A., Pizzoglio, A., Plantera, O. R., Plebani, W., Plessi, S., Podrecca, D., Poerio, V., Poggiani, F., Pogliani, W., Poli, L., Poloni, F. G., Porcelli, R., Porto, S., Pranzo, L., Prevedello, C., Profeta, C., Profico, D., Punzi, A., Quaglia, G. M., Racano, M., Raccone, A., Radice, F., Raho, C. A., Raimondi, R., Rainò, M., Ramponi, R., Ramunni, A., Ramunni, A. L., Ravasio, F., Ravera, M., Re Sartò, G., Rebustello, G., Regazzoli, S., Restelli, C., Rezzonico, M., Ricchiuto, F., Rigo, S., Rigon, G., Rigon, R., Rinaldi, O. V., Rinaldi, M., Risplendente, P. G., Rispoli, M., Riundi, R., Riva, M. G., Rizzi, A. L., Rizzi, D., Rizzo, L. D., Rocchi, L., Rondinone, B., Rosa, B., Rosati, F., Roselli, F., Rossetti, A., Rossetti, C., Rossi, R., Rossi, P. R., Rossi, A., Rossi, C. L., Rossitto, A., Ruffini, R., Ruffo, A., Ruggio, S., Ruo, M., Russo, B., Russo, L., Russo, R., Russo, S., Russo, U., Russo, V., Ruta, G., Sacchi, F., Sacco Botto, F., Saia, A., Salladini, G., Salmoiraghi, S., Saluzzo, F., Salvatore, C., Salvatori, E., Salvio, G., Sandri, P., Sandrini, T., Sangermano, V., Santoni, N., Saracino, A. D., Saracino, A., Sarasin, P., Sardo Infirri, C., Sarrì, B., Sartori, G., Sartori, N., Sauro, C., Scaglioni, M., Scalfi, C., Scamardella, A. M., Scandale, G., Scandone, L., Scannavini, G., Scarati, R., Scardi, A., Scarpa, F. M., Scazzi, P., Schifone, A., Schiroso, G., Scigliano, G., Scilla, A., Sciortino, M., Scolaro, G., Scollo, E., Scorretti, G., Sellitti, R., Selmo, A., Selvaggio, G., Sempio, A., Seren, F., Serio, L., Serra, C., Serra, L., Siciliano, D., Sideri, A., Sighele, M., Signore, R., Siliberto, F., Silvestro, M., Simioni, G., Simmini, G., Simonato, L., Sinchetto, F., Sizzano, E., Smajato, G., Smaldone, M., Sola, G., Sordillo, L., Sovran, C. S., Spagnul, P., Spanò, F., Sproviero, S., Squintani, A., Stella, L., Stilo, V., Stocchiero, B., Stornello, M. C., Stracka, G., Strada, S., Stranieri, G., Stucci, N., Stufano, N., Suppa, A., Susca, V. G., Sutti, M., Taddei, M., Tagliabue, E., Tagliente, G., Talato, F., Talerico, P., Talia, R., Taranto, R., Tartaglia, M., Tauro, N., Tedesco, A., Tieri, P., Tirelli, M., Tocci, L., Todesco, P., Tognolo, M., Tomba, A., Tonello, P., Tonon, R., Toscano, L., Tosi, A., Tosi, G., Toso, S., Travaglio, P., Tremul, L., Tresso, C., Triacchini, P., Triggiano, L., Trigilio, A., Trimeloni, J., Tripicchio, G., Tritto, G. S., Trono, F., Trotta, E., Trotta, G., Tubertini, A., Turri, C., Turri, L., Tuttolani, M. P., Urago, M., Ursini, G., Valcanover, F., Valente, L., Valenti, M., Valentini, F., Vallone, G., Valz, P., Valzano, L., Vanin, V., Vatteroni, M., Vegetti, L., Vendrame, D., Veramonti, I., Veronelli, G., Vesco, A., Vicariotto, G., Vignale, G., Villa, P. L., Vinciguerra, R., Visco, A., Visentin, G., Visonà, E., Vitali, E., Vitali, S., Vitti, F., Volpone, D. A., Zambon, N., Zammarrelli, A., Zanaboni, A., Zane, D., Zanetti, B., Zanibellato, R., Zappetti, M., Zappone, P., Zerilli, G., Zirino, V., Zoccali, R., Zuin, F., Altomonte, M., Anelli, N., Angiò, F., Annale, P., Antonacci, S., Anzilotta, R., Bano, F., Basadonna, O., Beduschi, L., Becagli, P., Bellotti, G., Blotta, C., Bruno, G., Cappuccini, A., Caramatti, S., Cariolato, M. P., Castellana, M., Castellani, L., Catania, R., Chielli, A., Chinellato, A., Ciaccia, A., Clerici, E., Cocci, A., Costanzo, G., D’Ercole, F., De Stefano, G., Decè, F., Di Cicco, N., Di Marco, A., Donati Sarti, C., Draghi, E., Dusi, G., Esposito, V., Ferraro, L., Ferretti, A., Ferri, E., Foggetti, L., Foglia, A., Fonzi, E., Frau, G., Fuoco, M. R., Furci, G., Gallo, L., Garra, V., Giannini, A., Gris, A., Iacovino, R., Interrigi, R., Joppi, R., Laner, B., La Fortezza, G., La Padula, A., Lista, M. R., Lupi, G., Maffei, D., Maggioni, G., Magnani, L., Marrazzo, E., Marcon, L., Marinò, V., Maroni, A., Martinelli, C., Mastandrea, E., Mastropierro, F., Meo, A. T., Mero, P., Minesso, E., Moschetta, V., Mosele, E., Nanni, C., Negretti, A., Nisticò, C., Orsini, A., Osti, M., Pacilli, M. C., Pennestre, C., Picerno, G., Piol, K., Pivano, L., Pizzuti, E., Poggi, L., Poidomani, I., Pozzetto, M., Presti, M. L., Ravani, R., Recalenda, V., Romagnuolo, F., Rossignoli, S., Rossin, E., Sabatella, C., Sacco, F., Sanità, F., Sansone, E., Servadei, F., Sisto, M. T., Sorio, A., Sorrentino, A., Spinelli, E., Spolaor, A., Squillacioti, A., Stella, P., Talerico, A., Todisco, C., Vadino, M., Zuliani, C., and Risk & Prevention Collaborative Group
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- 2017
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3. Thoracic non-traumatic cardiovascular diseases: current perspective and multi-detectors Computed Tomography protocols optimization in the emergency setting
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Palumbo, P, Cannizzaro, E, Bracci, A, Bruno, F, Arrigoni, F, Splendiani, A, Danti, G, Cozzi, D, Pradella, S, Grassi, F, Grassi, R, Dell'Aversana, F, Brunese, M C, Cutolo, C, Ravo, L, Fusco, R, Galdiero, R, Granata, V, Masciocchi, C, Di Cesare, E, Palumbo, P, Cannizzaro, E, Bracci, A, Bruno, F, Arrigoni, F, Splendiani, A, Danti, G, Cozzi, D, Pradella, S, Grassi, F, Grassi, R, Dell'Aversana, F, Brunese, M C, Cutolo, C, Ravo, L, Fusco, R, Galdiero, R, Granata, V, Masciocchi, C, and Di Cesare, E
- Subjects
Pulmonary embolism ,Non-traumatic ,CCTA ,Heart ,Humans ,Review Literature as Topic ,Tomography, X-Ray Computed ,Cardiovascular Diseases ,Cardiovascular System ,Thoracic Diseases ,Cardiovascular disease ,X-Ray Computed ,Thoracic emergency ,Acute aortic syndrome ,Triple-rule-out ,CTPA ,Acute coronary syndrome ,Tomography ,CT - Abstract
Cardiovascular diseases (CVDs) are among the most common causes of access to the Emergency Department and among the leading causes of death worldwide. Accurate diagnostic algorithms are mandatory to ensure a rapid life-saving treatment. However, non-specific clinical presentation and unnecessary referrals to other subspecialties may lead to misinterpretation of the diagnosis and delays. In recent years, the development of imaging technologies has allowed Computed Tomography (CT) to play a prominent role in the concepts of CVD rule-in and rule-out. An optimization strategy for CT protocols is needed to reduce variability and improve image quality. A correct diagnostic suspicion is crucial, as different districts (i.e., heart, aorta and pulmonary circulation) may require different investigation techniques. Additionally, the CVD pre-test probability assessment is highly correlated with CT accuracy. The purpose of this narrative review is to analyze the current role of CT in the approach to the CVDs in the ED, and to analyze the main strategies of CT optimization.
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- 2022
4. Optimization of CT protocol in polytrauma patients: an update
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Flammia, F, Chiti, G, Trinci, M, Danti, G, Cozzi, D, Grassi, R, Palumbo, P, Bruno, F, Agostini, A, Fusco, R, Granata, V, Giovagnoni, A, Miele, V, Flammia, F, Chiti, G, Trinci, M, Danti, G, Cozzi, D, Grassi, R, Palumbo, P, Bruno, F, Agostini, A, Fusco, R, Granata, V, Giovagnoni, A, and Miele, V
- Subjects
Review Literature as Topic ,Multiple Trauma ,Dual energy CT ,Radiologists ,Humans ,Polytrauma ,Tomography, X-Ray Computed ,Computed tomography ,Whole body CT - Abstract
Radiologists play a key role in the management of trauma patients. With the improvement of computed tomography (CT), radiologist makes an important contribution to the timely diagnosis of trauma-related findings and the choice of the most suitable treatment, improving patient outcomes. It is important to select the most appropriate imaging technique, which in the trauma patient is CT, and especially the most appropriate CT protocol, to correctly characterize trauma injuries. Currently, there is no agreement on what the optimal protocol is, acquisition times and number of contrast enhanced phases are not standardized. This is a review of the most recent literature on optimizing the CT protocol in polytrauma, with the intent of giving a useful tool for radiologists in the management of trauma patients.
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- 2022
5. Diagnostic protocols in oncology: workup and treatment planning. Part 1: the optimitation of CT protocol
- Author
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Granata, V., Fusco, R., Bicchierai, G., Cozzi, D., Grazzini, G., Danti, G., DE MUZIO, F., Maggialetti, N., Smorchkova, O., D'Elia, M., Brunese, M. C., Grassi, R., Giacobbe, G., Bruno, F., Palumbo, P., Lacasella, G. V., Brunese, L., Miele, V., Barile, A., Granata, V., Fusco, R., Bicchierai, G., Cozzi, D., Grazzini, G., Danti, G., DE MUZIO, F., Maggialetti, N., Smorchkova, O., D'Elia, M., Brunese, M. C., Grassi, R., Giacobbe, G., Bruno, F., Palumbo, P., Lacasella, G. V., Brunese, L., Miele, V., and Barile, A.
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Abbreviated protocol ,Computed tomography ,Magnetic resonance study ,Oncologic imaging ,Radiation exposure ,Humans ,Medical Oncology ,Neoplasms ,Tomography, X-Ray Computed ,Tomography ,X-Ray Computed - Abstract
The increase in oncology knowledge and the possibility of creating personalized medicine by selecting a more suitable therapy related to tumor subtypes, as well as the patient's management with cancer within a multidisciplinary team has improved the clinical outcomes. Early detection of cancer through screening-based imaging is probably the major contributor to a reduction in mortality for certain cancers. Nowadays, imaging can also characterize several lesions and predict their histopathological features and can predict tumor behaviour and prognosis. CT is the main diagnostic tool in oncologic imaging and is widely used for the tumors detection, staging, and follow-up. Moreover, since CT accounts for 49-66% of overall patient radiation exposure, the constant reduction, optimization, dose inter- and intraindividual consistency are major goals in radiological field. In the recent years, numerous dose reduction techniques have been established and created voltage modulation keeping a satisfactory image quality. The introduction of CT dual- layer detector technology enabled the acquisition of spectral data without additional CT x-ray tube or additional acquisitions. In addition, since MRI does not expose the body to radiation, it has become a mainstay of non-invasive diagnostic radiology modality since the 1980s.
- Published
- 2021
6. OC.01.6 HIGHER VOLUME GROWTH RATE IS ASSOCIATED WITH DEVELOPMENT OF WORRISOME FEATURES IN PATIENTS WITH BRANCH DUCT-INTRADUCTAL PAPILLARY MUCINOUS NEOPLASMS
- Author
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Innocenti, T., primary, Danti, G., additional, Lynch, E.N., additional, Dragoni, G., additional, Gottin, M., additional, Fedeli, F., additional, Palatresi, D., additional, Biagini, M.R., additional, Milani, S., additional, Miele, V., additional, and Galli, A., additional
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- 2022
- Full Text
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7. Non-traumatic non-cardiovascular thoracic emergencies: role of imaging.
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BICCI, E., GRAZZINI, G., COZZI, D., DANTI, G., PRADELLA, S., PALUMBO, P., BRUNO, F., GRASSI, F., DELL’ AVERSANA, F., DE MUZIO, F., BRUNESE, M. C., CUTOLO, C., FUSCO, R., GRANATA, V., GIOVAGNONI, A., and MIELE, V.
- Abstract
Patients presenting to the emergency with thoracic symptoms could have a wide variety of causes, even if the traumatic and vascular causes are excluded. Therefore, the diagnosis is often a challenge for emergency physicians. Anamnesis, physical examination and laboratory testing need to be integrated with imaging to get a rapid diagnosis and to distinguish among the potential causes. This review discusses the role of diagnostic imaging studies in the emergency setting in patients with non-traumatic non-cardiovascular thoracic symptoms. The use of chest x-ray, bedside lung Ultrasound and Computed Tomography in the diagnosis and care of these patients have been reviewed as well as the common findings on imaging. [ABSTRACT FROM AUTHOR]
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- 2022
8. Radiological assessment of peritoneal carcinomatosis: a primer for resident.
- Author
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GRANATA, V., FUSCO, R., SETOLA, S. VENANZIO, SASSAROLI, C., DE FRANCISIS, S., DELRIO, P., DANTI, G., GRAZZINI, G., FAGGIONI, L., GABELLONI, M., OTTAIANO, A., GREGGI, S., PATRONE, R., PALAIA, R., PETRILLO, A., and IZZO, F.
- Abstract
The imaging has critical responsibility in the assessment of peritoneal lesions along with estimating the overall extent. Valuing disease burden is crucial for selection of combining cytoreductive surgery (CRS) and intraperitoneal hyperthermic chemotherapy (HIPEC) treatment. An approach that combines the strength of several imaging tools and increases diagnostic accuracy, should be chosen, even if the preferred imaging tool in patients with suspected Peritoneal Carcinomatosis (PC) is CT. The outcomes of PC are mainly correlated to tumor spread, localization, and lesion size. Accurate assessment of these features is critical for prognosis and treatment planning. These data can be evaluated by Peritoneal Cancer Index (PCI), a quantitative index suggested by Harman and Sugarbaker. Additionally, precise predictive biomarkers should be established to predict PC in patients at risk. The radiomics analysis could predict PC throughout the evaluation of cancers heterogeneity. [ABSTRACT FROM AUTHOR]
- Published
- 2022
9. CT study protocol optimization in acute non-traumatic abdominal settings.
- Author
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MUZIO, F. DE, CUTOLO, C., GRANATA, V., FUSCO, R., RAVO, L., MAGGIALETTI, N., BRUNESE, M. C., GRASSI, R., GRASSI, F., BRUNO, F., PALUMBO, P., PALATRESI, D., COZZI, D., and DANTI, G.
- Abstract
Abdominal acute pain is a manifestation of heterogeneous medical conditions, with difficult clinical-laboratory assessment. Multi-detector CT (MDCT) is the gold standard imaging technique for evaluating adult patients with acute abdominal pain. Due to its fast execution and the high spatial resolution, CT is fundamental in the diagnostic and therapeutic work-up of patients with time-dependent pathology that could require surgical treatment, reducing mortality and morbidity. However, the radiological risk connected to the ionizing radiation use should not be underestimated, especially in young patients. The aim of this study is to identify optimized CT protocols to apply in the management of non-traumatic acute abdomen. In particular, this review is focused on the main emergency settings: acute pancreatitis, small bowel obstruction, acute appendicitis and acute diverticulitis. This survey would not be complete without mentioning Dual-Energy CT (DECT) technique, one of the last frontiers in CT, achieving encouraging results also in acute abdominal conditions. [ABSTRACT FROM AUTHOR]
- Published
- 2022
10. Diagnostic protocols in oncology: workup and treatment planning. Part 2: Abbreviated MR protocol.
- Author
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GRANATA, V., BICCHIERAI, G., FUSCO, R., COZZI, D., GRAZZINI, G., DANTI, G., DE MUZIO, F., MAGGIALETTI, N., SMORCHKOVA, O., D'ELIA, M., BRUNESE, M. C., GRASSI, R., GIACOBBE, G., BRUNO, F., PALUMBO, P., GRASSI, F., BRUNESE, L., MIELE, V., and BARILE, A.
- Abstract
Magnetic resonance imaging (MRI) is a non-invasive imaging technique (non-ionizing radiation) with superior soft tissue contrasts and potential morphological and functional applications. However, long examination and interpretation times, as well as higher costs, still represent barriers to MRI use in clinical routine. Abbreviated MRI protocols have emerged as an alternative to standard MRI protocols. Abbreviated MRI protocols eliminate redundant sequences that negatively affect cost, acquisition time, patient comfort. However, the diagnostic information is generally not compromised. Abbreviated MRI protocols have already been utilized for hepatocellular carcinoma, for prostate cancer detection, and for nonalcoholic fatty liver disease screening. [ABSTRACT FROM AUTHOR]
- Published
- 2021
11. Diagnostic imaging of gastrointestinal neuroendocrine tumours (GI-NETs): relationship between MDCT features and 2010 WHO classification. Grazzini G, Danti G, Cozzi D, Lanzetta MM, Addeo G, Falchini M, Masserelli A, Pradella S, Miele V. Radiol Med. Feb;124(2):94-102. doi: 10.1007/s11547-018-0946-8. Epub 2018 Sep 25
- Author
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Danti, G, Cozzi, D, Lanzetta, Mm, Addeo, Gloria, Falchini, M, Masserelli, A, Pradella, S, and Miele, V.
- Subjects
Extra-intestinal signs ,Gastrointestinal neuroendocrine tumours ,Intestinal signs ,Multi-detector computed tomography ,Pathological classification - Published
- 2019
12. Are all people with diabetes and cardiovascular risk factors or microvascular complications at very high risk? Findings from the Risk and Prevention Study
- Author
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Marzona, I., Avanzini, F., Lucisano, G., Tettamanti, M., Baviera, M., Nicolucci, A., Roncaglioni, M. C., Tombesi, M., Tognoni, G., Massa, E., Marrocco, W., Micalella, M., Caimi, V., Longoni, P., Franzosi, M. G., Monesi, L., Pangrazzi, I., Barlera, S., Milani, V., Nicolis, E., Casola, C., Clerici, F., Palumbo, A., Sgaroni, G., Marchioli, R., Silletta, M. G., Pioggiarella, R., Scarano, M., Marfisi, R. M., Flamminio, A., Macino, L., Ferri, B., Pera, C., Polidoro, A., Abbatino, D., Acquati, M., Addorisio, G., Adinolfi, D., Adreani, L., Agistri, M. R., Agneta, A., Agnolio, M. L., Agostini, N., Agostino, G., Airo, A., Alaimo, N., Albano, M., Albano, N., Alecci, G., Alemanno, S., Alexanian, A., Alfarano, M., Alfe, L., Alonzo, N., Alvino, S., Ancora, A., Andiloro, S., Andreatta, E., Angeli, S., Angiari, F., Angilletti, V., Annicchiarico, C., Anzivino, M., Aprea, R., Aprile, A., Aprile, E., Aprile, I., Aprile, L., Armellani, V., Arnetoli, M., Aronica, A., Autiero, V., Bacca, G., Baccalaro, A. M., Bacci, M., Baglio, G., Bagnani, M., Baiano, A., Baldari, A., Ballarini, L., Banchi, G., Bandera, R., Bandini, F., Baratella, M., Barbieri, A., Barbieri Vita, A., Bardi, M., Barlocchi, M., Baron, P., Bartoli, M., Basile, A., Basile, F., Basile, S., Battaggia, A., Battaglia, A., Bau, A., Beconcini, G., Beggio, R., Belfiore, P. A., Belicchi, M., Bellamoli, S., Bellini, C., Bellomo, M., Benetollo, C., Benetti, R., Beretta, E., Bertalero, P., Bertaso, F. G., Bertolani, U., Bettelli, G., Biagiotti, G., Bianchi, S., Bianco, G., Biccari, F., Bigioli, F., Bindi, M., Bisanti, G., Bitetti, E. M., Blasetti, M. P., Blesi, F., Boato, V., Boga, S., Boidi, E., Boldrin, G., Bollati, A., Bolzan, L., Bolzonella, S., Bonardi, P., Bonato, G. B., Bonci, M., Bonfitto, G., Bonincontro, E., Boninsegna, F., Bonissone, D., Bono, L., Bonollo, E., Borghi, M., Borioli, N., Borsatto, M., Bosco, T., Bosisio Pioltelli, M., Botarelli, C., Botassis, S., Bottini, F., Bottos, C., Bova, G., Bova, V., Bozzani, A., Bozzetto, R. M., Braga, V. T., Braglia, M., Bramati, E., Brazzoli, C., Breglia, G., Brescia, A., Briganti, D., Brigato, G., Brocchi, A., Brosio, F. A., Bruni, E., Buscaglia, E., Bussini, M. D., Bussotti, A., Buzzaccarini, F., Buzzatti, A., Caccamo, G., Cacciavillani, C., Caggiano, G., Calciano, F. P., Calderisi, M., Calienno, S., Caltagirone, P., Calzolari, I., Cammisa, M., Campanaro, M., Campanella, G. B., Campese, F., Canali, G., Candiani, D. E. L., Canepa, R., Canini, D., Canino, A., Cantoro, E. A., Capilupi, V., Capotosto, P., Cappelli, B., Capraro, G., Carafa, F. A., Carano, Q., Carcaterra, V., Carriero, D., Carrozzo, G., Cartanese, M., Casalena, M., Casarola, M., Caso, C., Casotto, M., Castaldi, F., Castegnaro, R., Castellani, G., Castri, S., Catalano, E., Catinello, N., Caturano, G., Cavallaro, R., Cavallo, A. M., Cavallo, G., Cavion, M. T., Cavirani, G., Cazzaniga, F., Cazzetta, D., Cecconi, V., Cefalo, A., Celebrano, M., Celora, A., Centonze, P., Cerati, D., Cesaretti, D., Checchia, G., Checchin, A., Cherubini, M., Chianese, L., Chiappa, A., Chiappa, M. V., Chiariello, G., Chiavini, G., Chicco, M., Chiumeo, F., Ciacciarelli, A., Ciaci, D., Ciancaglini, R., Cicale, C., Cicale, S., Cipolla, A., Ciruolo, A., Citeri, A. L., Citterio, G., Clerici, M., Coazzoli, E., Collecchia, G., Colletta, F., Colombo, I., Colorio, P., Coluccia, S., Comerio, M., Comoretto, P., Compagni, M., Conte, O., Contri, S., Contrisciani, A., Coppetti, T., Corasaniti, F., Corradi, M. T., Corsano, A., Corsini, A., Corti, N., Costantini, G., Costantino, A., Cotroneo, S., Cozzi, D., Cravello, M. G., Cristiano, E., Cucchi, R., Cusmai, L., D'Errico, G. B., D'Agostino, P., Dal Bianco, L., Dal Mutto, U., Dal Pozzo, G., Dallapiccola, P., Dallatorre, G., Dalle Molle, G., Dalloni, E., D'Aloiso, A., D'Amicis, G., Danese, R., Danieli, D., Danisi, G., D'Anna, M. A., Danti, G., D'Ascanio, S., Davidde, G., De Angeli, D., De Bastiani, R., De Battisti, A., De Bellis, A., De Berardinis, G., De Carlo, F., De Giorgi, D., De Gobbi, R., De Lorenzis, E., De Luca, P., De Martini, G., De Marzi, M., De Matteis, D., De Padova, S., De Polo, P., De Sabato, N., De Stefano, T., De Vita, M. T., De Vito, U., De Zolt, V., Debernardi, F., Del Carlo, A., Del Re, G., Del Zotti, F., D'Elia, R., Della Giovanna, P., Dell'Acqua, L., Dell'Orco, R. L., Demaria, G., Di Benedetto, M. G., Di Chiara, G., Di Corcia, V., Di Domizio, O., Di Donato, P., Di Donato, S., Di Fermo, G., Di Franco, M., Di Giovannantonio, G., Di Lascio, G., Di Lecce, G., Di Lorenzo, N., Di Maro, T., Di Mattia, Q., Di Michele, E., Di Modica, R. S., Di Murro, D., Di Noi, M. C., Di Paoli, V., Di Santi, M., Di Sanzo, A., Di Turi, C., Diazzi, A., Dileo, I., D'Ingianna, A. P., Dolci, A., Dona, G., Donato, C., Donato, P., Donini, A., Donna, M. E., Donvito, T. V., Esposito, L., Esposito, N., Evangelista, M., Faita, G., Falco, M., Falcone, D. A., Falorni, F., Fanciullacci, A., Fanton, L., Fasolo, L., Fassina, R., Fassone, A., Fatarella, P., Fedele, F., Fera, I., Fera, L., Ferioli, S., Ferlini, M. G., Ferlino, R., Ferrante, G., Ferrara, F. N., Ferrarese, M. F., Ferrari, G., Ferrari, O., Ferreri, A., Ferroni, M., Fezzi, G., Figaroli, C., Fina, M. G., Fioretta, A., Fiorucci, C., Firrincieli, R., Fischetti, M., Fischietti, G., Fiume, D. C., Flecchia, G., Forastiere, G., Fossati, B., Franceschi, P. L., Franchi, L., Franzoso, F., Frapporti, G., Frasca, G., Frisotti, A., Fumagalli, G., Fusco, D., Gabriele, P., Gabrieli, A., Gagliano, D., Galimberti, G., Galli, A., Gallicchio, N., Gallio, F., Gallipoli, T., Gallo, P., Galopin, T., Gambarelli, L., Garbin, A., Garozzo, G. M., Gasparri, R., Gastaldo, M., Gatti, E., Gazzaniga, P., Gennachi, N., Gentile, R. V., Germani, P., Gesualdi, F., Gherardi, E., Ghezzi, C., Ghidini, M. G., Ghionda, F., Giacci, L., Gialdini, D., Giampaolo, C., Giancane, R., Giannanti, A., Giannese, S., Giannini, L., Giaretta, M., Giaretta, R., Giavardi, L., Giordano, P., Giordano, E., Giordano, B., Gioria, G. M., Giugliano, R., Grassi, E. A., Greco, A., Greco, L., Grilletti, N., Grimaldi, N., Grisetti, G., Groppelli, G., Gualtieri, L., Guarducci, M., Guastella, G., Guerra, M., Guerrini, F., Guglielmini, A., Guido, A., Gulotta, P., Iacono, E., Iadarola, G., Ianiro, G., Iarussi, V., Ieluzzi, M. L., Ierardi, C., Ingaldi, F., Interlandi, S., Iocca, M., Iorno, A., Ioverno, E., Iurato, R., La Pace, L., La Piscopia, C., La Selva, R., Lafratta, M., Lamparelli, M., Lanaro, G., Lancerotto, R., Larcher, M., Lassandro, M., Lattuada, G., Laurino, P., Lefons, C., Legrottaglie, F., Lemma, A., Leone, D., Leone, F., Leso, A., Leuzzi, G., Levato, G., Libardi, L., Libralesso, N., Licini, P. I., Licursi, G., Lidonnici, F., Lillo, C., Liveri, L., Livio, A., Loiero, R. A., Loison, M., Lombardo, G., Lombardo, T., Lomunno, V., Lomuscio, S., Lonedo, A., Longo, E., Lora, L., Lotterio, A., Lucatello, L., Luongo, A., Lupoli, M., Macchia, C., Macri, G., Mafessanti, M., Maggialetti, V., Maggioni, A., Magnani, M., Maiellaro, G., Mancuso, A., Maniglio, A. R., Mannari, G. L., Manni, A., Manocchio, B., Mao, M., Marano, A., Maraone, E., Marascio, D., Marcheselli, P., Marchetto, B., Marchetto, S., Marchi, A., Marchi, G. L., Mariano, C., Marinacci, S., Marinelli, S., Marini, G., Marra, V. C., Marrali, F., Marseglia, C., Martello, G., Martino, C., Martino, G., Martino, M., Marulli, C. F., Maruzzi, G., Marzotti, A., Mascheroni, G., Mascolo, P., Masoch, G., Masone, R., Massa, L., Massafra, M., Massi, M., Massignani, D. M., Matarese, A. M., Matini, G., Mauro, R., Mazzi, M., Mazzillo, A., Mazzocato, E., Mazzoleni, N. S., Mazzone, A., Melacci, A., Mele, E., Meliota, P., Menaspa, S., Meneghello, F., Merola, G., Merone, L., Metrucci, A., Mezzina, V., Micchi, A., Michielon, A., Migliore, N., Minero, G., Minotta, F., Mirandola, C., Mistrorigo, S., Modafferi, L., Moitre, R., Mola, E., Monachese, C., Mongiardini, C., Montagna, F., Montani, M., Montemurno, I., Montolli, R., Montorsi, S., Montresor, M., Monzani, M. G., Morabito, F., Mori, G., Moro, A., Mosca, M. F., Motti, F., Muddolon, L., Mugnai, M., Muscas, F., Naimoli, F., Nanci, G., Nargi, E., Nasorri, R., Nastrini, G., Negossi, M., Negrini, A., Negroni, A., Neola, V., Niccolini, F., Niro, C. M., Nosengo, C., Novella, G., Nuti, C., Obici, F., Olita, C., Oliverio, S. S., Olivieri, I., Oriente, S., Orlando, G., Paci, C., Pagano, G., Pagliara, C., Paita, G., Paladini, G., Paladino, G., Palano, T., Palatella, A., Palermo, P., Palmisano, M., Pando, P., Panessa, P., Panigo, F., Panozzo, G., Panvini, F., Panzieri, F., Panzino, A., Panzitta, F., Paoli, N., Papagna, R., Papaleo, M. G., Papalia, G., Parisi, R., Parotti, N., Parravicini, D., Passarella, P., Pastore, G. A., Patafio, M., Pavone, P., Pedroli, W., Pedroni, M., Pelligra, G., Pellizzari, M., Penati, A., Perlot, M., Perrone, A., Perrone, G., Peruzzi, P., Peselli, C., Petracchini, L., Petrera, L., Petrone, S., Peverelli, C., Pianorsi, F., Piazza, G. P., Piazzolla, G., Picci, A., Pienabarca, G., Pietronigro, T. P., Pignocchino, P., Pilone, R., Pinto, D., Pirovano, E., Pirrotta, D., Pisante, V., Pitotto, P., Pittari, L., Piva, A., Pizzoglio, A., Plantera, O. R., Plebani, W., Plessi, S., Podrecca, D., Poerio, V., Poggiani, F., Pogliani, W., Poli, L., Poloni, F. G., Porcelli, R., Porto, S., Pranzo, L., Prevedello, C., Profeta, C., Profico, D., Punzi, A., Quaglia, G. M., Racano, M., Raccone, A., Radice, F., Raho, C. A., Raimondi, R., Raino, M., Ramponi, R., Ramunni, A., Ramunni, A. L., Ravasio, F., Ravera, M., Re Sarto, G., Rebustello, G., Regazzoli, S., Restelli, C., Rezzonico, M., Ricchiuto, F., Rigo, S., Rigon, G., Rigon, R., Rinaldi, O. V., Rinaldi, M., Risplendente, P. G., Rispoli, M., Riundi, R., Riva, M. G., Rizzi, A. L., Rizzi, D., Rizzo, L. D., Rocchi, L., Rondinone, B., Rosa, B., Rosati, F., Roselli, F., Rossetti, A., Rossetti, C., Rossi, R., Rossi, P. R., Rossi, A., Rossi, C. L., Rossitto, A., Ruffini, R., Ruffo, A., Ruggio, S., Ruo, M., Russo, B., Russo, L., Russo, R., Russo, S., Russo, U., Russo, V., Ruta, G., Sacchi, F., Sacco Botto, F., Saia, A., Salladini, G., Salmoiraghi, S., Saluzzo, F., Salvatore, C., Salvatori, E., Salvio, G., Sandri, P., Sandrini, T., Sangermano, V., Santoni, N., Saracino, A. D., Saracino, A., Sarasin, P., Sardo Infirri, C., Sarri, B., Sartori, G., Sartori, N., Sauro, C., Scaglioni, M., Scalfi, C., Scamardella, A. M., Scandale, G., Scandone, L., Scannavini, G., Scarati, R., Scardi, A., Scarpa, F. M., Scazzi, P., Schifone, A., Schiroso, G., Scigliano, G., Scilla, A., Sciortino, M., Scolaro, G., Scollo, E., Scorretti, G., Sellitti, R., Selmo, A., Selvaggio, G., Sempio, A., Seren, F., Serio, L., Serra, C., Serra, L., Siciliano, D., Sideri, A., Sighele, M., Signore, R., Siliberto, F., Silvestro, M., Simioni, G., Simmini, G., Simonato, L., Sinchetto, F., Sizzano, E., Smajato, G., Smaldone, M., Sola, G., Sordillo, L., Sovran, C. S., Spagnul, P., Spano, F., Sproviero, S., Squintani, A., Stella, L., Stilo, V., Stocchiero, B., Stornello, M. C., Stracka, G., Strada, S., Stranieri, G., Stucci, N., Stufano, N., Suppa, A., Susca, V. G., Sutti, M., Taddei, M., Tagliabue, E., Tagliente, G., Talato, F., Talerico, P., Talia, R., Taranto, R., Tartaglia, M., Tauro, N., Tedesco, A., Tieri, P., Tirelli, M., Tocci, L., Todesco, P., Tognolo, M., Tomba, A., Tonello, P., Tonon, R., Toscano, L., Tosi, A., Tosi, G., Toso, S., Travaglio, P., Tremul, L., Tresso, C., Triacchini, P., Triggiano, L., Trigilio, A., Trimeloni, J., Tripicchio, G., Tritto, G. S., Trono, F., Trotta, E., Trotta, G., Tubertini, A., Turri, C., Turri, L., Tuttolani, M. P., Urago, M., Ursini, G., Valcanover, F., Valente, L., Valenti, M., Valentini, F., Vallone, G., Valz, P., Valzano, L., Vanin, V., Vatteroni, M., Vegetti, L., Vendrame, D., Veramonti, I., Veronelli, G., Vesco, A., Vicariotto, G., Vignale, G., Villa, P. L., Vinciguerra, R., Visco, A., Visentin, G., Visona, E., Vitali, E., Vitali, S., Vitti, F., Volpone, D. A., Zambon, N., Zammarrelli, A., Zanaboni, A., Zane, D., Zanetti, B., Zanibellato, R., Zappetti, M., Zappone, P., Zerilli, G., Zirino, V., Zoccali, R., Zuin, F., Altomonte, M., Anelli, N., Angio, F., Annale, P., Antonacci, S., Anzilotta, R., Bano, F., Basadonna, O., Beduschi, L., Becagli, P., Bellotti, G., Blotta, C., Bruno, G., Cappuccini, A., Caramatti, S., Cariolato, M. P., Castellana, M., Castellani, L., Catania, R., Chielli, A., Chinellato, A., Ciaccia, A., Clerici, E., Cocci, A., Costanzo, G., D'Ercole, F., De Stefano, G., Dece, F., Di Cicco, N., Di Marco, A., Donati Sarti, C., Draghi, E., Dusi, G., Esposito, V., Ferraro, L., Ferretti, A., Ferri, E., Foggetti, L., Foglia, A., Fonzi, E., Frau, G., Fuoco, M. R., Furci, G., Gallo, L., Garra, V., Giannini, A., Gris, A., Iacovino, R., Interrigi, R., Joppi, R., Laner, B., La Fortezza, G., La Padula, A., Lista, M. R., Lupi, G., Maffei, D., Maggioni, G., Magnani, L., Marrazzo, E., Marcon, L., Marino, V., Maroni, A., Martinelli, C., Mastandrea, E., Mastropierro, F., Meo, A. T., Mero, P., Minesso, E., Moschetta, V., Mosele, E., Nanni, C., Negretti, A., Nistico, C., Orsini, A., Osti, M., Pacilli, M. C., Pennestre, C., Picerno, G., Piol, K., Pivano, L., Pizzuti, E., Poggi, L., Poidomani, I., Pozzetto, M., Presti, M. L., Ravani, R., Recalenda, V., Romagnuolo, F., Rossignoli, S., Rossin, E., Sabatella, C., Sacco, F., Sanita, F., Sansone, E., Servadei, F., Sisto, M. T., Sorio, A., Sorrentino, A., Spinelli, E., Spolaor, A., Squillacioti, A., Stella, P., Talerico, A., Todisco, C., Vadino, M., and Zuliani, C.
- Subjects
Male ,medicine.medical_specialty ,Endocrinology, Diabetes and Metabolism ,Population ,030209 endocrinology & metabolism ,030204 cardiovascular system & hematology ,Overweight ,03 medical and health sciences ,0302 clinical medicine ,Endocrinology ,Prediction model ,Internal medicine ,Diabetes mellitus ,Internal Medicine ,Diabetes Mellitus ,Medicine ,Humans ,Multicenter Studies as Topic ,Myocardial infarction ,Risk factor ,education ,Stroke ,Aged ,Randomized Controlled Trials as Topic ,education.field_of_study ,Lifestyle habits ,business.industry ,Major cardiovascular events ,Atrial fibrillation ,General Medicine ,Middle Aged ,medicine.disease ,Cardiovascular Diseases ,Heart failure ,Physical therapy ,Female ,medicine.symptom ,business ,Diabetic Angiopathies - Abstract
To verify whether it is possible, in people with diabetes mellitus (DM) considered at very high cardiovascular (CV) risk, stratify this risk better and identify significant modifiable risk factor (including lifestyle habits) to help patients and clinicians improve CV prevention. People with DM and microvascular diseases or one or more CV risk factors (hypertension, hyperlipidemia, smoking, poor dietary habits, overweight, physical inactivity) included in the Risk and Prevention study were selected. We considered the combined endpoint of non-fatal acute myocardial infarction and stroke and CV death. A multivariate Cox proportional analysis was carried out to identify relevant predictors. We also used the RECPAM method to identify subgroups of patients at higher risk. In our study, the rate of major CV events was lower than expected (5 % in 5 years). Predictors of CV events were age, male, sex, heart failure, previous atherosclerotic disease, atrial fibrillation, insulin treatment, high HbA1c, heart rate and other CV diseases while being physically active was protective. RECPAM analysis indicated that history of atherosclerotic diseases and a low BMI defined worse prognosis (HR 4.51 95 % CI 3.04–6.69). Among subjects with no previous atherosclerotic disease, men with HbA1c more than 8 % were at higher CV risk (HR 2.77; 95 % CI 1.86–4.14) with respect to women. In this population, the rate of major CV events was lower than expected. This prediction model could help clinicians identify people with DM at higher CV risk and support them in achieving goals of physical activity and HbA1c.
- Published
- 2016
13. N-3 fatty acids in patients with multiple cardiovascular risk factors
- Author
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Roncaglioni, Maria Carla, Avanzini, Fausto, Barlera, Simona, Marzona, Irene, Milani, Valentina, Tombesi, Massimo, Caimi, Vittorio, Longoni, Paolo, Silletta, Maria Giuseppina, Tognoni, Gianni, Marchioli, Avanzini F, Roberto., Caimi, V, Longoni, P, Marchioli, R, Roncaglioni, Mc, Silletta, Mg, Tognoni, G, Tombesi, M, Barlera, S, Milani, V, Nicolis, Eb, Casola, C, Marzona, I, Massa, E, Marrocco, W, Micalella, M, Avanzini, F, Franzosi, Mg, Geraci, E, Giansiracusa, N, Rocchetti, L, Decarli, A, Satolli, R, Alli, C, Beghi, E, Bertele', V, Volpi, A, Baviera, M, Monesi, L, Pangrazzi, I, Nicolis, E, Clerici, F, Palumbo, A, Sgaroni, G, Pioggiarella, R, Scarano, M, Marfisi, Rm, Flamminio, A, Macino, L, Ferri, B, Pera, C, Polidoro, A, Abbatino, D, Acquati, M, Addorisio, G, Adinolfi, D, Adreani, L, Agistri, Mr, Agneta, A, Agnolio, Ml, Agostini, N, Agostino, G, Airò, A, Alaimo, N, Albano, M, Albano, N, Alecci, G, Alemanno, S, Alexanian, A, Alfarano, M, Alfè, L, Alonzo, N, Alvino, S, Ancora, A, Andiloro, S, Andreatta, E, Angeli, S, Angiari, F, Angilletti, V, Annicchiarico, C, Anzivino, M, Aprea, R, Aprile, A, Aprile, E, Aprile, I, Aprile, L, Armellani, V, Arnetoli, M, Aronica, A, Autiero, V, Bacca, G, Baccalaro, Am, Bacci, M, Baglio, G, Bagnani, M, Baiano, A, Baldari, A, Ballarini, L, Banchi, G, Bandera, R, Bandini, F, Baratella, M, Barbieri, A, Barbieri Vita, A, Bardi, M, Barlocchi, M, Baron, P, Bartoli, M, Basile, A, Basile, F, Basile, S, Battaggia, A, Battaglia, A, Baù, A, Beconcini, G, Beggio, R, Belfiore, Pa, Belicchi, M, Bellamoli, S, Bellini, C, Bellomo, M, Benetollo, C, Benetti, R, Beretta, E, Bertalero, P, Bertaso, Fg, Bertolani, U, Bettelli, G, Biagiotti, G, Bianchi, S, Bianco, G, Biccari, F, Bigioli, F, Bindi, M, Bisanti, G, Bitetti, Em, Blasetti, Mp, Blesi, F, Boato, V, Boga, S, Boidi, E, Boldrin, G, Bollati, A, Bolzan, L, Bolzonella, S, Bonardi, P, Bonato, Gb, Bonci, M, Bonfitto, G, Bonincontro, E, Boninsegna, F, Bonissone, D, Bono, L, Bonollo, E, Borghi, M, Borioli, N, Borsatto, M, Bosco, T, Bosisio Pioltelli, M, Botarelli, C, Botassis, S, Bottini, F, Bottos, C, Bova, G, Bova, V, Bozzani, A, Bozzetto, Rm, Braga, Vt, Braglia, M, Bramati, E, Brazzoli, C, Breglia, G, Brescia, A, Briganti, D, Brigato, G, Brocchi, A, Brosio, Fa, Bruni, E, Buscaglia, E, Bussini, Md, Bussotti, A, Buzzaccarini, F, Buzzatti, A, Caccamo, G, Cacciavillani, C, Caggiano, G, Calciano, Fp, Calderisi, M, Calienno, S, Caltagirone, P, Calzolari, I, Cammisa, M, Campanaro, M, Campanella, Gb, Campese, F, Canali, G, Candiani, De, Canepa, R, Canini, D, Canino, A, Cantoro, Ea, Capilupi, V, Capotosto, P, Cappelli, B, Capraro, G, Carafa, Fa, Carano, Q, Carcaterra, V, Carriero, D, Carrozzo, G, Cartanese, M, Casalena, M, Casarola, M, Caso, C, Casotto, M, Castaldi, F, Castegnaro, R, Castellani, G, Castri, S, Catalano, E, Catinello, N, Caturano, G, Cavallaro, R, Cavallo, Am, Cavallo, G, Cavion, Mt, Cavirani, G, Cazzaniga, F, Cazzetta, D, Cecconi, V, Cefalo, A, Celebrano, M, Celora, A, Centonze, P, Cerati, D, Cesaretti, D, Checchia, G, Checchin, A, Cherubini, M, Chianese, L, Chiappa, A, Chiappa, Mv, Chiariello, G, Chiavini, G, Chicco, M, Chiumeo, F, Ciacciarelli, A, Ciaci, D, Ciancaglini, R, Cicale, C, Cicale, S, Cipolla, A, Ciruolo, A, Citeri, Al, Citterio, G, Clerici, M, Coazzoli, E, Collecchia, G, Colletta, F, Colombo, I, Colorio, P, Coluccia, S, Comerio, M, Comoretto, P, Compagni, M, Conte, O, Contri, S, Contrisciani, A, Coppetti, T, Corasaniti, F, Corradi, Mt, Corsano, A, Corsini, A, Corti, N, Costantini, G, Costantino, A, Cotroneo, S, Cozzi, D, Cravello, Mg, Cristiano, E, Cucchi, R, Cusmai, L, D' Errico GB, D'Agostino, P, Dal Bianco, L, Dal Mutto, U, Dal Pozzo, G, Dallapiccola, P, Dallatorre, G, Dalle Molle, G, Dalloni, E, D'Aloiso, A, D'Amicis, G, Danese, R, Danieli, D, Danisi, G, D'Anna, Ma, Danti, G, D'Ascanio, S, Davidde, G, De Angeli, D, De Bastiani, R, De Battisti, A, De Bellis, A, De Berardinis, G, De Carlo, F, De Giorgi, D, De Gobbi, R, De Lorenzis, E, De Luca, P, De Martini, G, De Marzi, M, De Matteis, D, De Padova, S, De Polo, P, De Sabato, N, De Stefano, T, De Vita MT, De Vita, U, De Zolt, V, Debernardi, F, Del Carlo, A, Del Re, G, Del Zotti, F, D'Elia, R, Della Giovanna, P, Dell'Acqua, L, Dell'Orco, Rl, Demaria, G, Di Benedetto MG, Di Chiara, G, Di Corcia, V, Di Domizio, O, Di Donato, P, Di Donato, S, Di Fermo, G, Di Franco, M, Di Giovannantonio, G, Di Lascio, G, Di Lecce, G, Di Lorenzo, N, Di Maro, T, Di Mattia, Q, Di Michele, E, Di Modica RS, Di Murro, D, Di Noi MC, Di Paoli, V, Di Santi, M, Di Sanzo, A, Di Turi, C, Diazzi, A, Dileo, I, D'Ingianna, Ap, Dolci, A, Donà, G, Donato, C, Donato, P, Donini, A, Donna, Me, Donvito, Tv, Esposito, L, Esposito, N, Evangelista, M, Faita, G, Falco, M, Falcone, Da, Falorni, F, Fanciullacci, A, Fanton, L, Fasolo, L, Fassina, R, Fassone, A, Fatarella, P, Fedele, F, Fera, I, Fera, L, Ferioli, S, Ferlini, Mg, Ferlino, R, Ferrante, G, Ferrara, Fn, Ferrarese, Mf, Ferrari, G, Ferrari, O, Ferreri, A, Ferroni, M, Fezzi, G, Figaroli, C, Fina, Mg, Fioretta, A, Fiorucci, C, Firrincieli, R, Fischetti, M, Fischietti, G, Fiume, Dc, Flecchia, G, Forastiere, G, Fossati, B, Franceschi, Pl, Franchi, L, Franzoso, F, Frapporti, G, Frasca, G, Frisotti, A, Fumagalli, G, Fusco, D, Gabriele, P, Gabrieli, A, Gagliano, D, Galimberti, G, Galli, A, Gallicchio, N, Gallio, F, Gallipoli, T, Gallo, P, Galopin, T, Gambarelli, L, Garbin, A, Garozzo, Gm, Gasparri, R, Gastaldo, M, Gatti, E, Gazzaniga, P, Gennachi, N, Gentile, Rv, Germani, P, Gesualdi, F, Gherardi, E, Ghezzi, C, Ghidini, Mg, Ghionda, F, Giacci, L, Gialdini, D, Giampaolo, C, Giancane, R, Giannanti, A, Giannese, S, Giannini, L, Giaretta, M, Giaretta, R, Giavardi, L, Giordano, P, Giordano, E, Giordano, B, Gioria, Gm, Giugliano, R, Grassi, Ea, Greco, A, Greco, L, Grilletti, N, Grimaldi, N, Grisetti, G, Groppelli, G, Gualtieri, L, Guarducci, M, Guastella, G, Guerra, M, Guerrini, F, Guglielmini, A, Guido, A, Gulotta, P, Iacono, E, Iadarola, G, Ianiro, G, Iarussi, V, Ieluzzi, Ml, Ierardi, C, Ingaldi, F, Interlandi, S, Iocca, M, Iorno, A, Ioverno, E, Iurato, R, La Pace, L, La Piscopia, C, La Selva, R, Lafratta, M, Lamparelli, M, Lanaro, G, Lancerotto, R, Larcher, M, Lassandro, M, Lattuada, G, Laurino, P, Lefons, C, Legrottaglie, F, Lemma, A, Leone, D, Leone, F, Leso, A, Leuzzi, G, Levato, G, Libardi, L, Libralesso, N, Licini, Pi, Licursi, G, Lidonnici, F, Lillo, C, Liveri, L, Livio, A, Loiero, Ra, Loison, M, Lombardo, G, Lombardo, T, Lomunno, V, Lomuscio, S, Lonedo, A, Longo, E, Lora, L, Lotterio, A, Lucatello, L, Luongo, A, Lupoli, M, Macchia, C, Macri, G, Mafessanti, M, Maggialetti, V, Maggioni, A, Magnani, M, Maiellaro, G, Mancuso, A, Maniglio, Ar, Mannari, Gl, Manni, A, Manocchio, B, Mao, M, Maranò, A, Maraone, E, Marascio, D, Marcheselli, P, Marchetto, B, Marchetto, S, Marchi, A, Marchi, Gl, Mariano, C, Marinacci, S, Marinelli, S, Marini, G, Marra, Vc, Marrali, F, Marseglia, C, Martello, G, Martino, C, Martino, G, Martino, M, Marulli, Cf, Maruzzi, G, Marzotti, A, Mascheroni, G, Mascolo, P, Masoch, G, Masone, R, Massa, L, Massafra, M, Massi, M, Massignani, Dm, Matarese, Am, Matini, G, Mauro, R, Mazzi, M, Mazzillo, A, Mazzocato, E, Mazzoleni, Ns, Mazzone, A, Melacci, A, Mele, E, Meliota, P, Menaspà, S, Meneghello, F, Merola, G, Merone, L, Metrucci, A, Mezzina, V, Micchi, A, Michielon, A, Migliore, N, Minero, G, Minotta, F, Mirandola, C, Mistrorigo, S, Modafferi, L, Moitre, R, Mola, E, Monachese, C, Mongiardini, C, Montagna, F, Montani, M, Montemurno, I, Montolli, R, Montorsi, S, Montresor, M, Monzani, Mg, Morabito, F, Mori, G, Moro, A, Mosca, Mf, Motti, F, Muddolon, L, Mugnai, M, Muscas, F, Naimoli, F, Nanci, G, Nargi, E, Nasorri, R, Nastrini, G, Negossi, M, Negrini, A, Negroni, A, Neola, V, Niccolini, F, Niro, Cm, Nosengo, C, Novella, G, Nuti, C, Obici, F, Olita, C, Oliverio, Ss, Olivieri, I, Oriente, S, Orlando, G, Paci, C, Pagano, G, Pagliara, C, Paita, G, Paladini, G, Paladino, G, Palano, T, Palatella, A, Palermo, P, Palmisano, M, Pando, P, Panessa, P, Panigo, F, Panozzo, G, Panvini, F, Panzieri, F, Panzino, A, Panzitta, F, Paoli, N, Papagna, R, Papaleo, Mg, Papalia, G, Parisi, R, Parotti, N, Parravicini, D, Passarella, P, Pastore, Ga, Patafio, M, Pavone, P, Pedroli, W, Pedroni, M, Pelligra, G, Pellizzari, M, Penati, A, Perlot, M, Perrone, A, Perrone, G, Peruzzi, P, Peselli, C, Petracchini, L, Petrera, L, Petrone, S, Peverelli, C, Pianorsi, F, Piazza, Gp, Piazzolla, G, Picci, A, Pienabarca, G, Pietronigro, Tp, Pignocchino, P, Pilone, R, Pinto, D, Pirovano, E, Pirrotta, D, Pisante, V, Pitotto, P, Pittari, L, Piva, A, Pizzoglio, A, Plantera, Or, Plebani, W, Plessi, S, Podrecca, D, Poerio, V, Poggiani, F, Pogliani, W, Poli, L, Poloni, Fg, Porcelli, R, Porto, S, Pranzo, L, Prevedello, C, Profeta, C, Profico, D, Punzi, A, Quaglia, Gm, Racano, M, Raccone, A, Radice, F, Raho, Ca, Raimondi, R, Rainò, M, Ramponi, R, Ramunni, A, Ramunni, Al, Ravasio, F, Ravera, M, Re Sartò, G, Rebustello, G, Regazzoli, S, Restelli, C, Rezzonico, M, Ricchiuto, F, Rigo, S, Rigon, G, Rigon, R, Rinaldi, Ov, Rinaldi, M, Risplendente, Pg, Rispoli, M, Riundi, R, Riva, Mg, Rizzi, Al, Rizzi, D, Rizzo, Ld, Rocchi, L, Rondinone, B, Rosa, B, Rosati, F, Roselli, F, Rossetti, A, Rossetti, C, Rossi, R, Rossi, Pr, Rossi, A, Rossi, Cl, Rossitto, A, Ruffini, R, Ruffo, A, Ruggio, S, Ruo, M, Russo, B, Russo, L, Russo, R, Russo, S, Russo, U, Russo, V, Ruta, G, Sacchi, F, Sacco Botto, F, Saia, A, Salladini, G, Salmoiraghi, S, Saluzzo, F, Salvatore, C, Salvatori, E, Salvio, G, Sandri, P, Sandrini, T, Sangermano, V, Santoni, N, Saracino, Ad, Saracino, A, Sarasin, P, Sardo Infirri, C, Sarrì, B, Sartori, G, Sartori, N, Sauro, C, Scaglioni, M, Scalfi, C, Scamardella, Am, Scandale, G, Scandone, L, Scannavini, G, Scarati, R, Scardi, A, Scarpa, Fm, Scazzi, P, Schifone, A, Schirosa, G, Scigliano, G, Scilla, A, Sciortino, M, Scolaro, G, Scollo, E, Scorretti, G, Sellitti, R, Selmo, A, Selvaggio, G, Sempio, A, Seren, F, Serio, L, Serra, C, Serra, L, Siciliano, D, Sideri, A, Sighele, M, Signore, R, Siliberto, F, Silvestro, M, Simioni, G, Simmini, G, Simonato, L, Sinchetto, F, Sizzano, E, Smajato, G, Smaldone, M, Sola, G, Sordillo, L, Sovran, Cs, Spagnul, P, Spanò, F, Sproviero, S, Squintani, A, Stella, L, Stilo, V, Stocchiero, B, Stornello, Mc, Stracka, G, Strada, S, Stranieri, G, Stucci, N, Stufano, N, Suppa, A, Susca, Vg, Sutti, M, Taddei, M, Tagliabue, E, Tagliente, G, Talato, F, Talerico, P, Talia, R, Taranto, R, Tartaglia, M, Tauro, N, Tedesco, A, Tieri, P, Tirelli, M, Tocci, L, Todesco, P, Tognolo, M, Tomba, A, Tonello, P, Tonon, R, Toscano, L, Tosi, A, Tosi, G, Toso, S, Travaglio, P, Tremul, L, Tresso, C, Triacchini, P, Triggiano, L, Trigilio, A, Trimeloni, J, Tripicchio, G, Tritto, Gs, Trono, F, Trotta, E, Trotta, G, Tubertini, A, Turri, C, Turri, L, Tuttolani, Mp, Urago, M, Ursini, G, Valcanover, F, Valente, L, Valenti, M, Valentini, F, Vallone, G, Valz, P, Valzano, L, Vanin, V, Vatteroni, M, Vegetti, L, Vendrame, D, Veramonti, I, Veronelli, G, Vesco, A, Vicariotto, G, Vignale, G, Villa, Pl, Vinciguerra, R, Visco, A, Visentin, G, Visonà, E, Vitali, E, Vitali, S, Vitti, F, Volpone, Da, Zambon, N, Zammarrelli, A, Zanaboni, A, Zane, D, Zanetti, B, Zanibellato, R, Zappetti, M, Zappone, P, Zerilli, G, Zirino, V, Zoccali, R, Zuin, F, Altomonte, M, Anelli, N, Angiò, F, Annale, P, Antonacci, S, Anzilotta, R, Bano, F, Basadonna, O, Beduschi, L, Becagli, P, Bellotti, G, Blotta, C, Bruno, G, Cappuccini, A, Caramatti, S, Cariolato, Mp, Castellana, M, Castellani, L, Catania, R, Chielli, A, Chinellato, A, Ciaccia, A, Clerici, E, Cocci, A, Costanzo, G, D'Ercole, F, De Stefano, G, Decè, F, Di Cicco, N, Di Marco, A, Donati Sarti, C, Draghi, E, Dusi, G, Esposito, V, Ferraro, L, Ferretti, A, Ferri, E, Foggetti, L, Foglia, A, Fonzi, E, Frau, G, Fuoco, Mr, Furci, G, Gallo, L, Garra, V, Giannini, A, Gris, A, Iacovino, R, Interrigi, R, Joppi, R, Laner, B, La Fortezza, G, La Padula, A, Lista, Mr, Lupi, G, Maffei, D, Maggioni, G, Magnani, L, Marrazzo, E, Marcon, L, Marinò, V, Maroni, A, Martinelli, C, Mastandrea, E, Mastropierro, F, Meo, At, Mero, P, Minesso, E, Moschetta, V, Mosele, E, Nanni, C, Negretti, A, Nisticò, C, Orsini, A, Osti, M, Pacilli, Mc, Pennestre, C, Picerno, G, Piol, K, Pivano, L, Pizzuti, E, Poggi, L, Poidomani, I, Pozzetto, M, Presti, Ml, Ravani, R, Recalenda, V, Romagnuolo, F, Rossignoli, S, Rossin, E, Sabatella, C, Sacco, F, Sanità, F, Sansone, E, Servadei, F, Sisto, Mt, Sorio, A, Sorrentino, A, Spinelli, E, Spolaor, A, Squillacioti, A, Stella, P, Talerico, A, Todisco, C, Vadino, M, and Zuliani, C.
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Male ,medicine.medical_specialty ,General Practice ,Kaplan-Meier Estimate ,Placebo ,Double-Blind Method ,Risk Factors ,Internal medicine ,Fatty Acids, Omega-3 ,Clinical endpoint ,medicine ,Humans ,Myocardial infarction ,Treatment Failure ,Aged ,Proportional Hazards Models ,chemistry.chemical_classification ,Omega-3 ,business.industry ,Proportional hazards model ,Medicine (all) ,Hazard ratio ,Fatty Acids ,General Medicine ,Middle Aged ,medicine.disease ,Hospitalization ,Primary Prevention ,chemistry ,Cardiovascular Diseases ,Heart failure ,Cohort ,Female ,business ,Polyunsaturated fatty acid - Abstract
Background Trials have shown a beneficial effect of n-3 polyunsaturated fatty acids in patients with a previous myocardial infarction or heart failure. We evaluated the potential benefit of such therapy in patients with multiple cardiovascular risk factors or atherosclerotic vascular disease who had not had a myocardial infarction. Methods In this double-blind, placebo-controlled clinical trial, we enrolled a cohort of patients who were followed by a network of 860 general practitioners in Italy. Eligible patients were men and women with multiple cardiovascular risk factors or atherosclerotic vascular disease but not myocardial infarction. Patients were randomly assigned to n-3 fatty acids (1 g daily) or placebo (olive oil). The initially specified primary end point was the cumulative rate of death, nonfatal myocardial infarction, and nonfatal stroke. At 1 year, after the event rate was found to be lower than anticipated, the primary end point was revised as time to death from cardiovascular causes or admission to the hospital for cardiovascular causes. Results Of the 12,513 patients enrolled, 6244 were randomly assigned to n-3 fatty acids and 6269 to placebo. With a median of 5 years of follow-up, the primary end point occurred in 1478 of 12,505 patients included in the analysis (11.8%), of whom 733 of 6239 (11.7%) had received n-3 fatty acids and 745 of 6266 (11.9%) had received placebo (adjusted hazard ratio with n-3 fatty acids, 0.97; 95% confidence interval, 0.88 to 1.08; P=0.58). The same null results were observed for all the secondary end points. Conclusions In a large general-practice cohort of patients with multiple cardiovascular risk factors, daily treatment with n-3 fatty acids did not reduce cardiovascular mortality and morbidity. (Funded by Societa Prodotti Antibiotici and others; ClinicalTrials.gov number, NCT00317707.).
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- 2013
14. Prevalence of Dementia and Apolipoprotein E Genotype Distribution in the Elderly of Buttapietra, Verona Province, Italy
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Benedetti, M.D., primary, Salviati, A., additional, Filipponi, S., additional, Manfredi, M., additional, De Togni, L., additional, Gomez Lira, M., additional, Stenta, G., additional, Fincati, E., additional, Pampanin, M., additional, Rizzuto, N., additional, and Danti, G., additional
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- 2002
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15. An evaluation of tienilic acid, a new diuretic uricosuric agent, in the therapy of arterial hypertension
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Riva Ad, G. Covi, Danti G, Pedrolli E, Alessandro Lechi, and Pomari S
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Adult ,Blood Glucose ,Male ,Uricosuric ,Adolescent ,medicine.medical_treatment ,Posture ,Ticrynafen ,Renal function ,Blood Pressure ,Pharmacology ,chemistry.chemical_compound ,Electrolytes ,Hydrochlorothiazide ,Double-Blind Method ,Uricosuric Agent ,Heart Rate ,medicine ,Humans ,Triglycerides ,Aged ,Creatinine ,Clinical Trials as Topic ,business.industry ,General Medicine ,Middle Aged ,Glycolates ,Uric Acid ,Cholesterol ,chemistry ,Tienilic acid ,Hypertension ,Uric acid ,Female ,Diuretic ,business ,medicine.drug - Abstract
1. Tienilic acid and hydrochlorothiazide were evaluated in a double-blind trial in order to investigate their antihypertensive and metabolic effects. 2. After 5 weeks, the decreases in blood pressure and the changes in plasma or serum electrolytes, urea, creatinine, glucose, cholesterol and triglycerides, and in creatinine clearance, did not differ in the two groups of patients. 3. In patients taking tienilic acid a significant decrease in serum uric acid and an increase in urate clearance was observed, whereas in patients receiving hydrochlorothiazide a slight increase in serum uric acid, with no modification of urate clearance, occurred. 4. The diuretic and antihypertensive actions of tienilic acid and hydrochlorothiazide are very similar. The uricosuric/hypouricaemic effect of tienilic acid could assume clinical relevance in long-term therapy of hypertensive patients.
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- 1979
16. [Relation between plasma renin activity and cardiovascular complications in essential arterial hypertension]
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Covi, G., Danti, G., Lechi, Alessandro, Corgnati, A., Santonastaso, Clara, and Scuro, L. A.
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- 1977
17. Italian Study on Depression (ISD) in general practice,Studio Italiano sulla Depressione in Medicina Generale (Italian Study on Depression - ISD)
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Danti, G., Del Zotti, F., Bonati, M., Font, M., Miselli, M., Romero, M., Tognoni, G., Pietraru, C., Spolaor, A., Castellani, L., Joppi, R., Mezzalira, L., Marta Baviera, Roni, C., Anecchino, C., Di Biagio, K., and Sasso, E.
18. Cross-sectional survey of the cardiovascular risk profile evaluation in the population of subjects for healthcare assistance by general practitioners in the area of ASL 20 of Verona,Indagine trasversale per la valutazione del profilo di rischio cardiovascolare nella popolazione di soggetti assistibili afferenti ai medici di medicina generale nell'area della ASL 20 di Verona
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Bastarolo, D., Battaggia, A., Blengio, G., SILVIA BUSTACCHINI, Celebrano, M., Danti, G., Flor, L., Girotto, S., Joppi, R., Lombardo, G., Mezzalira, L., Mirandola, M., Panfilo, M., Pescarin, G., and Ruffo, P.
19. Evaluating health status in patients with chronic bronchitis: An outcome study in general practice | LA VALUTAZIONE DELLO STATO DI SALUTE IN PAZIENTI AMBULATORIALI CON BRONCHITE CRONICA. UNO STUDIO DI ESITO NELLA MEDICINA GENERALE
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Bosisio, M., Parma, E., Apolone, G., Guido Bertolini, Agnolio, M. L., Benincasa, F., Bertini, L., Bertolissi, S., Bianchetti, F., Cazzaniga, F., Danti, G., Della Vedova, R., Di Giovanbattista, E., Filippone, B., Fossati, B., Gazzetta, F., Lenotti, M. A., Marchetto, M., and Martignoni, A. M.
20. An Evaluation of Tienilic Acid, a New Diuretic Uricosuric Agent, in the Therapy of Arterial Hypertension
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Lechi, A., primary, Covi, G., primary, Danti, G., primary, Riva, A. Dalla, primary, Pedrolli, E., primary, and Pomari, S., primary
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- 1979
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21. Imaging of metabolic and overload disorders in tissues and organs
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Bruno, Federico, Albano, Domenico, Agostini, Andrea, Benenati, Massimo, Cannella, Roberto, Caruso, Damiano, Cellina, Michaela, Cozzi, Diletta, Danti, Ginevra, De Muzio, Federica, Gentili, Francesco, Giacobbe, Giuliana, Gitto, Salvatore, Grazzini, Giulia, Grazzini, Irene, Messina, Carmelo, Palmisano, Anna, Palumbo, Pierpaolo, Bruno, Alessandra, Grassi, Francesca, Grassi, Roberta, Fusco, Roberta, Granata, Vincenza, Giovagnoni, Andrea, Miele, Vittorio, Barile, Antonio, Bruno F., Albano D., Agostini A., Benenati M., Cannella R., Caruso D., Cellina M., Cozzi D., Danti G., De Muzio F., Gentili F., Giacobbe G., Gitto S., Grazzini G., Grazzini I., Messina C., Palmisano A., Palumbo P., Bruno A., Grassi F., Grassi R., Fusco R., Granata V., Giovagnoni A., Miele V., and Barile A.
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Fabry disease ,Hemochromatosi ,Radiology, Nuclear Medicine and imaging ,Overload disorder ,Metabolic disorder ,CT ,MRI - Abstract
Metabolic and overload disorders are a heterogeneous group of relatively uncommon but important diseases. While imaging plays a key role in the early detection and accurate diagnosis in specific organs with a pivotal role in several metabolic pathways, most of these diseases affect different tissues as part of a systemic syndromes. Moreover, since the symptoms are often vague and phenotypes similar, imaging alterations can present as incidental findings, which must be recognized and interpreted in the light of further biochemical and histological investigations. Among imaging modalities, MRI allows, thanks to its multiparametric properties, to obtain numerous information on tissue composition, but many metabolic and accumulation alterations require a multimodal evaluation, possibly using advanced imaging techniques and sequences, not only for the detection but also for accurate characterization and quantification. The purpose of this review is to describe the different alterations resulting from metabolic and overload pathologies in organs and tissues throughout the body, with particular reference to imaging findings.
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- 2023
22. Ability of Delta Radiomics to Predict a Complete Pathological Response in Patients with Loco-Regional Rectal Cancer Addressed to Neoadjuvant Chemo-Radiation and Surgery
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Valerio Nardone, Alfonso Reginelli, Roberta Grassi, Giovanna Vacca, Giuliana Giacobbe, Antonio Angrisani, Alfredo Clemente, Ginevra Danti, Pierpaolo Correale, Salvatore Francesco Carbone, Luigi Pirtoli, Lorenzo Bianchi, Angelo Vanzulli, Cesare Guida, Roberto Grassi, Salvatore Cappabianca, Nardone, V., Reginelli, A., Grassi, R., Vacca, G., Giacobbe, G., Angrisani, A., Clemente, A., Danti, G., Correale, P., Carbone, S. F., Pirtoli, L., Bianchi, L., Vanzulli, A., Guida, C., and Cappabianca, S.
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rectal cancer ,neoadjuvant chemo-radiation ,MRI ,texture analysis ,Cancer Research ,Oncology - Abstract
We performed a pilot study to evaluate the use of MRI delta texture analysis (D-TA) as a methodological item able to predict the frequency of complete pathological responses and, consequently, the outcome of patients with locally advanced rectal cancer addressed to neoadjuvant chemoradiotherapy (C-RT) and subsequently, to radical surgery. In particular, we carried out a retrospective analysis including 100 patients with locally advanced rectal adenocarcinoma who received C-RT and then radical surgery in three different oncological institutions between January 2013 and December 2019. Our experimental design was focused on the evaluation of the gross tumor volume (GTV) at baseline and after C-RT by means of MRI, which was contoured on T2, DWI, and ADC sequences. Multiple texture parameters were extracted by using a LifeX Software, while D-TA was calculated as percentage of variations in the two time points. Both univariate and multivariate analysis (logistic regression) were, therefore, carried out in order to correlate the above-mentioned TA parameters with the frequency of pathological responses in the examined patients’ population focusing on the detection of complete pathological response (pCR, with no viable cancer cells: TRG 1) as main statistical endpoint. ROC curves were performed on three different datasets considering that on the 21 patients, only 21% achieved an actual pCR. In our training dataset series, pCR frequency significantly correlated with ADC GLCM-Entropy only, when univariate and binary logistic analysis were performed (AUC for pCR was 0.87). A confirmative binary logistic regression analysis was then repeated in the two remaining validation datasets (AUC for pCR was 0.92 and 0.88, respectively). Overall, these results support the hypothesis that D-TA may have a significant predictive value in detecting the occurrence of pCR in our patient series. If confirmed in prospective and multicenter trials, these results may have a critical role in the selection of patients with locally advanced rectal cancer who may benefit form radical surgery after neoadjuvant chemoradiotherapy.
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- 2022
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23. Structured reporting of computed tomography and magnetic resonance in the staging of pancreatic adenocarcinoma: A delphi consensus proposal
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Mirko D'Onofrio, Nicola Maggialetti, Salvatore Cappabianca, Lorenzo Faggioni, Giulia Grazzini, Federica De Muzio, Alfonso Reginelli, Roberto Grassi, Francesca Coppola, Ginevra Danti, Eleonora Ciaghi, Francesca Grassi, Marco Montella, Carmelo Barresi, Roberta Fusco, Duccio Buccicardi, Vincenza Granata, Fabrizio Urraro, Giovanni Morana, Marco Rengo, Emanuele Neri, Chandra Bortolotto, Vittorio Miele, Francesco Bellifemine, Giorgia Viola La Casella, Antonio Barile, Luca Brunese, Granata, V., Morana, G., D'Onofrio, M., Fusco, R., Coppola, F., Grassi, F., Cappabianca, S., Reginelli, A., Maggialetti, N., Buccicardi, D., Barile, A., Rengo, M., Bortolotto, C., Urraro, F., La Casella, G. V., Montella, M., Ciaghi, E., Bellifemine, F., De Muzio, F., Danti, G., Grazzini, G., Barresi, C., Brunese, L., Neri, E., Grassi, R., Miele, V., and Faggioni, L.
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Structured report ,Medicine (General) ,medicine.medical_specialty ,radiology report ,structured report ,pancreatic adenocarcinoma ,computed tomography ,magnetic resonance imaging ,Correlation coefficient ,Clinical Biochemistry ,Section (typography) ,Article ,R5-920 ,Magnetic resonance imaging ,Cronbach's alpha ,Structured reporting ,medicine ,Medical physics ,Computed tomography ,computer.programming_language ,Protocol (science) ,medicine.diagnostic_test ,Interventional radiology ,Radiology report ,Pancreatic adenocarcinoma ,Psychology ,computer ,Delphi - Abstract
Background: Structured reporting (SR) in radiology has been recognized recently by major scientific societies. This study aims to build structured computed tomography (CT) and magnetic resonance (MR)-based reports in pancreatic adenocarcinoma during the staging phase in order to improve communication between the radiologist and members of multidisciplinary teams. Materials and Methods: A panel of expert radiologists, members of the Italian Society of Medical and Interventional Radiology, was established. A modified Delphi process was used to develop the CT-SR and MRI-SR, assessing a level of agreement for all report sections. Cronbach’s alpha (Cα) correlation coefficient was used to assess internal consistency for each section and to measure quality analysis according to the average inter-item correlation. Results: The final CT-SR version was built by including n = 16 items in the “Patient Clinical Data” section, n = 11 items in the “Clinical Evaluation” section, n = 7 items in the “Imaging Protocol” section, and n = 18 items in the “Report” section. Overall, 52 items were included in the final version of the CT-SR. The final MRI-SR version was built by including n = 16 items in the “Patient Clinical Data” section, n = 11 items in the “Clinical Evaluation” section, n = 8 items in the “Imaging Protocol” section, and n = 14 items in the “Report” section. Overall, 49 items were included in the final version of the MRI-SR. In the first round for CT-SR, all sections received more than a good rating. The overall mean score of the experts was 4.85. The Cα correlation coefficient was 0.85. In the second round, the overall mean score of the experts was 4.87, and the Cα correlation coefficient was 0.94. In the first round, for MRI-SR, all sections received more than a good rating. The overall mean score of the experts was 4.73. The Cα correlation coefficient was 0.82. In the second round, the overall mean score of the experts was 4.91, and the Cα correlation coefficient was 0.93. Conclusions: The CT-SR and MRI-SR are based on a multi-round consensus-building Delphi exercise derived from the multidisciplinary agreement of expert radiologists in order to obtain more appropriate communication tools for referring physicians.
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- 2021
24. Dynamic contrast-enhanced (DCE) imaging: state of the art and applications in whole-body imaging
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Domenico, Albano, Federico, Bruno, Andrea, Agostini, Salvatore Alessio, Angileri, Massimo, Benenati, Giulia, Bicchierai, Michaela, Cellina, Vito, Chianca, Diletta, Cozzi, Ginevra, Danti, Federica, De Muzio, Letizia, Di Meglio, Francesco, Gentili, Giuliana, Giacobbe, Giulia, Grazzini, Irene, Grazzini, Pasquale, Guerriero, Carmelo, Messina, Giuseppe, Micci, Pierpaolo, Palumbo, Maria Paola, Rocco, Roberto, Grassi, Vittorio, Miele, Antonio, Barile, Albano, D., Bruno, F., Agostini, A., Angileri, S. A., Benenati, M., Bicchierai, G., Cellina, M., Chianca, V., Cozzi, D., Danti, G., De Muzio, F., Di Meglio, L., Gentili, F., Giacobbe, G., Grazzini, G., Grazzini, I., Guerriero, P., Messina, C., Micci, G., Palumbo, P., Rocco, M. P., Grassi, R., Miele, V., and Barile, A.
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Radiomics ,Oncology ,Artificial Intelligence ,Neoplasms ,Contrast Media ,Humans ,Whole Body Imaging ,Radiology, Nuclear Medicine and imaging ,DCE ,Magnetic Resonance Imaging ,MRI - Abstract
Dynamic contrast-enhanced (DCE) imaging is a non-invasive technique used for the evaluation of tissue vascularity features through imaging series acquisition after contrast medium administration. Over the years, the study technique and protocols have evolved, seeing a growing application of this method across different imaging modalities for the study of almost all body districts. The main and most consolidated current applications concern MRI imaging for the study of tumors, but an increasing number of studies are evaluating the use of this technique also for inflammatory pathologies and functional studies. Furthermore, the recent advent of artificial intelligence techniques is opening up a vast scenario for the analysis of quantitative information deriving from DCE. The purpose of this article is to provide a comprehensive update on the techniques, protocols, and clinical applications - both established and emerging - of DCE in whole-body imaging.
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- 2021
25. Structured Reporting of Computed Tomography in the Staging of Neuroendocrine Neoplasms: A Delphi Consensus Proposal
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Vincenza Granata, Francesca Coppola, Roberta Grassi, Roberta Fusco, Salvatore Tafuto, Francesco Izzo, Alfonso Reginelli, Nicola Maggialetti, Duccio Buccicardi, Barbara Frittoli, Marco Rengo, Chandra Bortolotto, Roberto Prost, Giorgia Viola Lacasella, Marco Montella, Eleonora Ciaghi, Francesco Bellifemine, Federica De Muzio, Ginevra Danti, Giulia Grazzini, Massimo De Filippo, Salvatore Cappabianca, Carmelo Barresi, Franco Iafrate, Luca Pio Stoppino, Andrea Laghi, Roberto Grassi, Luca Brunese, Emanuele Neri, Vittorio Miele, Lorenzo Faggioni, Granata, V., Coppola, F., Grassi, R., Fusco, R., Tafuto, S., Izzo, F., Reginelli, A., Maggialetti, N., Buccicardi, D., Frittoli, B., Rengo, M., Bortolotto, C., Prost, R., Lacasella, G. V., Montella, M., Ciaghi, E., Bellifemine, F., De Muzio, F., Danti, G., Grazzini, G., De Filippo, M., Cappabianca, S., Barresi, C., Iafrate, F., Stoppino, L. P., Laghi, A., Brunese, L., Neri, E., Miele, V., and Faggioni, L.
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Adult ,medicine.medical_specialty ,Consensus ,Delphi Technique ,Correlation coefficient ,Endocrinology, Diabetes and Metabolism ,Computed tomography ,computed tomography ,neuroendocrine neoplasm ,radiology report ,staging ,structured report ,Humans ,Neoplasm Staging ,Neuroendocrine Tumors ,Tomography, X-Ray Computed ,Diseases of the endocrine glands. Clinical endocrinology ,Standard deviation ,Endocrinology ,Cronbach's alpha ,Structured reporting ,Medicine ,Medical physics ,Tomography ,Original Research ,computer.programming_language ,Protocol (science) ,medicine.diagnostic_test ,business.industry ,Interventional radiology ,RC648-665 ,X-Ray Computed ,business ,computer ,Delphi - Abstract
BackgroundStructured reporting (SR) in radiology is becoming increasingly necessary and has been recognized recently by major scientific societies. This study aims to build structured CT-based reports in Neuroendocrine Neoplasms during the staging phase in order to improve communication between the radiologist and members of multidisciplinary teams.Materials and MethodsA panel of expert radiologists, members of the Italian Society of Medical and Interventional Radiology, was established. A Modified Delphi process was used to develop the SR and to assess a level of agreement for all report sections. Cronbach’s alpha (Cα) correlation coefficient was used to assess internal consistency for each section and to measure quality analysis according to the average inter-item correlation.ResultsThe final SR version was built by including n=16 items in the “Patient Clinical Data” section, n=13 items in the “Clinical Evaluation” section, n=8 items in the “Imaging Protocol” section, and n=17 items in the “Report” section. Overall, 54 items were included in the final version of the SR. Both in the first and second round, all sections received more than a good rating: a mean value of 4.7 and range of 4.2-5.0 in the first round and a mean value 4.9 and range of 4.9-5 in the second round. In the first round, the Cα correlation coefficient was a poor 0.57: the overall mean score of the experts and the sum of scores for the structured report were 4.7 (range 1-5) and 728 (mean value 52.00 and standard deviation 2.83), respectively. In the second round, the Cα correlation coefficient was a good 0.82: the overall mean score of the experts and the sum of scores for the structured report were 4.9 (range 4-5) and 760 (mean value 54.29 and standard deviation 1.64), respectively.ConclusionsThe present SR, based on a multi-round consensus-building Delphi exercise following in-depth discussion between expert radiologists in gastro-enteric and oncological imaging, derived from a multidisciplinary agreement between a radiologist, medical oncologist and surgeon in order to obtain the most appropriate communication tool for referring physicians.
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- 2021
26. Diagnostic protocols in oncology: Workup and treatment planning. Part 2: Abbreviated MR protocol
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V, Granata, G, Bicchierai, R, Fusco, D, Cozzi, G, Grazzini, G, Danti, F, De Muzio, N, Maggialetti, O, Smorchkova, M, D'Elia, M C, Brunese, R, Grassi, G, Giacobbe, F, Bruno, P, Palumbo, F, Grassi, L, Brunese, V, Miele, A, Barile, Granata, V., Bicchierai, G., Fusco, R., Cozzi, D., Grazzini, G., Danti, G., Muzio, F. D. E., Maggialetti, N., Smorchkova, O., D'Elia, M., Brunese, M. C., Grassi, R., Giacobbe, G., Bruno, F., Palumbo, P., Grassi, F., Brunese, L., Miele, V., and Barile, A.
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Abbreviated protocol ,Magnetic resonance study ,Computed tomography ,Oncologic imaging ,Radiation exposure ,Artificial Intelligence ,Humans ,Medical Oncology ,Neoplasms ,Magnetic Resonance Imaging - Abstract
Magnetic resonance imaging (MRI) is a non-invasive imaging technique (non-ionizing radiation) with superior soft tissue contrasts and potential morphological and functional applications. However, long examination and interpretation times, as well as higher costs, still represent barriers to MRI use in clinical routine. Abbreviated MRI protocols have emerged as an alternative to standard MRI protocols. Abbreviated MRI protocols eliminate redundant sequences that negatively affect cost, acquisition time, patient comfort. However, the diagnostic information is generally not compromised. Abbreviated MRI protocols have already been utilized for hepatocellular carcinoma, for prostate cancer detection, and for nonalcoholic fatty liver disease screening.
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- 2021
27. Laser Flare Photometry to Monitor Childhood Chronic Uveitis: A Preliminary Report of a Monocentric Italian Experience.
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Maccora I, De Libero C, Peri M, Danti G, Rossi A, Marrani E, Pasqualetti R, Pagnini I, Mastrolia MV, and Simonini G
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Background : Childhood chronic non-infectious uveitis (cNIU) is a challenging disease that needs close monitoring. Slit lamp evaluation (SLE) is the cornerstone of ophthalmological evaluation for uveitis, but it is affected by interobserver variability and may be problematic in children. Laser flare photometry (LFP), a novel and objective technique, might be used in children with uveitis. Aim : The aim of this study was to attempt the use of LFP in cNIU clinical practice. Methods : Children, attending the Rheumatology Unit and who were scheduled to receive ophthalmological evaluation, were prospectively enrolled to concomitantly receive SLE and LFP. SLE was performed blind to LFP measure. Demographic, laboratory, clinical, and ophthalmology data were collected. Results : A total of 29 children (58 eyes) were enrolled, including 3 with juvenile idiopathic arthritis without uveitis (JIA-no-U), 15 with JIA-associated uveitis (JIA-U), and 11 with idiopathic chronic uveitis (ICU). We observed significantly higher LFP values in the eyes of children with uveitis compared to the others (10.1 IQR 7.1-13.6 versus 6.2 IQR 5.8-6.9, p = 0.007). Accordance between the SLE and LFP measures, at baseline (ρ.498, p < 0.001) and during the follow-up (LFP II ρ 0.460, p < 0.001, LFP III ρ 0.631, p < 0.001, LFP IV ρ 0.547, p = 0.006, LFP V ρ 0.767, p = 0.001), was detected. We evaluated significant correlation between LFP values and the presence of complications (ρ 0.538, p < 0.001), especially with cataract formation (ρ 0.542, p < 0.001). Conclusions : In this cohort, LFP measurements showed a good correlation with SLE. LFP values showed a positive correlation with the presence of complications. LFP might be considered as a reliable objective modality to monitor intraocular inflammation in cNIU.
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- 2023
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28. Radiomics in gastrointestinal stromal tumours: an up-to-date review.
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Galluzzo A, Boccioli S, Danti G, De Muzio F, Gabelloni M, Fusco R, Borgheresi A, Granata V, Giovagnoni A, Gandolfo N, and Miele V
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- Humans, Prognosis, Tomography, X-Ray Computed, Magnetic Resonance Imaging, Endoscopy, Gastrointestinal Stromal Tumors diagnostic imaging
- Abstract
Gastrointestinal stromal tumours are rare mesenchymal neoplasms originating from the Cajal cells and represent the most common sarcomas in the gastroenteric tract. Symptoms may be absent or non-specific, ranging from fatigue and weight loss to acute abdomen. Nowadays endoscopy, echoendoscopy, contrast-enhanced computed tomography, magnetic resonance imaging and positron emission tomography are the main methods for diagnosis. Because of their rarity, these neoplasms may not be included immediately in the differential diagnosis of a solitary abdominal mass. Radiomics is an emerging technique that can extract medical imaging information, not visible to the human eye, transforming it into quantitative data. The purpose of this review is to demonstrate how radiomics can improve the already known imaging techniques by providing useful tools for the diagnosis, treatment, and prognosis of these tumours., (© 2023. The Author(s) under exclusive licence to Japan Radiological Society.)
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- 2023
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29. An Updated Review on Imaging and Staging of Anal Cancer-Not Just Rectal Cancer.
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Congedo A, Mallardi D, Danti G, De Muzio F, Granata V, and Miele V
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- Humans, Lymphatic Metastasis diagnostic imaging, Positron Emission Tomography Computed Tomography, Rare Diseases, Anus Neoplasms diagnostic imaging, Anus Neoplasms therapy, Rectal Neoplasms diagnostic imaging, Rectal Neoplasms therapy, Carcinoma, Squamous Cell diagnostic imaging, Carcinoma, Squamous Cell therapy, Adenocarcinoma diagnostic imaging, Adenocarcinoma therapy
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Anal cancer is a rare disease, but its incidence has been increasing steadily. Primary staging and assessment after chemoradiation therapy are commonly performed using MRI, which is considered to be the preferred imaging modality. CT and PET/CT are useful in evaluating lymph node metastases and distant metastatic disease. Anal squamous-cell carcinoma (ASCC) and rectal adenocarcinoma are typically indistinguishable on MRI, and a biopsy prior to imaging is necessary to accurately stage the tumor and determine the treatment approach. This review discusses the histology, MR technique, diagnosis, staging, and treatment of anal cancer, with a particular focus on the differences in TNM staging between anal and rectal carcinomas., Purpose: This review discusses the histology, MR technique, diagnosis, staging, and treatment of anal cancer, with a particular focus on the differences in TNM staging between anal squamous-cell carcinoma (ASCC) and rectal adenocarcinoma., Methods and Materials: To conduct this updated review, a comprehensive literature search was performed using prominent medical databases, including PubMed and Embase. The search was limited to articles published within the last 10 years (2013-2023) to ensure their relevance to the current state of knowledge., Inclusion Criteria: (1) articles that provided substantial information on the diagnostic techniques used for ASCC, mainly focusing on imaging, were included; (2) studies reporting on emerging technologies; (3) English-language articles., Exclusion Criteria: articles that did not meet the inclusion criteria, case reports, or articles with insufficient data. The primary outcome of this review is to assess the accuracy and efficacy of different diagnostic modalities, including CT, MRI, and PET, in diagnosing ASCC. The secondary outcomes are as follows: (1) to identify any advancements or innovations in diagnostic techniques for ASCC over the past decade; (2) to highlight the challenges and limitations of the diagnostic process., Results: ASCC is a rare disease; however, its incidence has been steadily increasing. Primary staging and assessment after chemoradiation therapy are commonly performed using MRI, which is considered to be the preferred imaging modality. CT and PET/CT are useful in evaluating lymph node metastases and distant metastatic disease., Conclusion: ASCC and rectal adenocarcinoma are the most common histological subtypes and are typically indistinguishable on MRI; therefore, a biopsy prior to imaging is necessary to stage the tumor accurately and determine the treatment approach.
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- 2023
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30. Focal Lesions of the Liver and Radiomics: What Do We Know?
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Anichini M, Galluzzo A, Danti G, Grazzini G, Pradella S, Treballi F, and Bicci E
- Abstract
Despite differences in pathological analysis, focal liver lesions are not always distinguishable in contrast-enhanced magnetic resonance imaging (MRI), contrast-enhanced computed tomography (CT), and positron emission tomography (PET). This issue can cause problems of differential diagnosis, treatment, and follow-up, especially in patients affected by HBV/HCV chronic liver disease or fatty liver disease. Radiomics is an innovative imaging approach that extracts and analyzes non-visible quantitative imaging features, supporting the radiologist in the most challenging differential diagnosis when the best-known methods are not conclusive. The purpose of this review is to evaluate the most significant CT and MRI texture features, which can discriminate between the main benign and malignant focal liver lesions and can be helpful to predict the response to pharmacological or surgical therapy and the patient's prognosis.
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- 2023
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31. Radiomic Features Are Predictive of Response in Rectal Cancer Undergoing Therapy.
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Santini D, Danti G, Bicci E, Galluzzo A, Bettarini S, Busoni S, Innocenti T, Galli A, and Miele V
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Background: Rectal cancer is a major mortality cause in the United States (US), and its treatment is based on individual risk factors for recurrence in each patient. In patients with rectal cancer, accurate assessment of response to chemoradiotherapy has increased in importance as the variety of treatment options has grown. In this scenario, a controversial non-operative approach may be considered in some patients for whom complete tumor regression is believed to have occurred. The recommended treatment for locally advanced rectal cancer (LARC, T3-4 ± N+) is total mesorectal excision (TME) after neoadjuvant chemoradiotherapy (nCRT). Magnetic resonance imaging (MRI) has become a standard technique for local staging of rectal cancer (tumor, lymph node, and circumferential resection margin [CRM] staging), in both the US and Europe, and it is getting widely used for restaging purposes., Aim: In our study, we aimed to use an MRI radiomic model to identify features linked to the different responses of chemoradiotherapy of rectal cancer before surgery, and whether these features are helpful to understand the effectiveness of the treatments., Methods: We retrospectively evaluated adult patients diagnosed with LARC who were subjected to at least 2 MRI examinations in 10-12 weeks at our hospital, before and after nCRT. The MRI acquisition protocol for the 2 exams included T2 sequence and apparent diffusion coefficient (ADC) map. The patients were divided into 2 groups according to the treatment response: complete or good responders (Group 1) and incomplete or poor responders (Group 2). MRI images were segmented, and quantitative features were extracted and compared between the two groups. Features that showed significant differences (SF) were then included in a LASSO regression method to build a radiomic-based predictive model., Results: We included 38 patients (26 males and 12 females), who are classified from T2 and T4 stages in the rectal cancer TNM. After the nCRT, the patients were divided into Group 1 (13 patients), complete or good responders, and Group 2 (25 patients), incomplete or poor responders. Analysis at baseline generated the following significant features for the Mann-Whitney test (out of a total of 107) for each sequence. Also, the analysis at the end of the follow-up yielded a high number of significant features for the Mann-Whitney test (out of a total of 107) for each image. Features selected by the LASSO regression method for each image analyzed; ROC curves relative to each model are represented., Conclusion: We developed an MRI-based radiomic model that is able to differentiate and predict between responders and non-responders who went through nCRT for rectal cancer. This approach might identify early lesions with high surgical potential from lesions potentially resolving after medical treatment.
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- 2023
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32. Local Recurrences in Rectal Cancer: MRI vs. CT.
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Grazzini G, Danti G, Chiti G, Giannessi C, Pradella S, and Miele V
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Rectal cancers are often considered a distinct disease from colon cancers as their survival and management are different. Particularly, the risk for local recurrence (LR) is greater than in colon cancer. There are many factors predisposing to LR such as postoperative histopathological features or the mesorectal plane of surgical resection. In addition, the pattern of LR in rectal cancer has a prognostic significance and an important role in the choice of operative approach and. Therefore, an optimal follow up based on imaging is critical in rectal cancer. The aim of this review is to analyse the risk and the pattern of local recurrences in rectal cancer and to provide an overview of the role of imaging in early detection of LRs. We performed a literature review of studies published on Web of Science and MEDLINE up to January 2023. We also reviewed the current guidelines of National Comprehensive Cancer Network (NCCN) and the European Society for Medical Oncology (ESMO). Although the timing and the modality of follow-up is not yet established, the guidelines usually recommend a time frame of 5 years post surgical resection of the rectum. Computed Tomography (CT) scans and/or Magnetic Resonance Imaging (MRI) are the main imaging techniques recommended in the follow-up of these patients. PET-CT is not recommended by guidelines during post-operative surveillance and it is generally used for problem solving.
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- 2023
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33. Tips and Tricks in Thoracic Radiology for Beginners: A Findings-Based Approach.
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Borgheresi A, Agostini A, Pierpaoli L, Bruno A, Valeri T, Danti G, Bicci E, Gabelloni M, De Muzio F, Brunese MC, Bruno F, Palumbo P, Fusco R, Granata V, Gandolfo N, Miele V, Barile A, and Giovagnoni A
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- Humans, Tomography, X-Ray Computed methods, Lung diagnostic imaging, Radiography, Thoracic methods, Lung Neoplasms diagnostic imaging, Radiology
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This review has the purpose of illustrating schematically and comprehensively the key concepts for the beginner who approaches chest radiology for the first time. The approach to thoracic imaging may be challenging for the beginner due to the wide spectrum of diseases, their overlap, and the complexity of radiological findings. The first step consists of the proper assessment of the basic imaging findings. This review is divided into three main districts (mediastinum, pleura, focal and diffuse diseases of the lung parenchyma): the main findings will be discussed in a clinical scenario. Radiological tips and tricks, and relative clinical background, will be provided to orient the beginner toward the differential diagnoses of the main thoracic diseases.
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- 2023
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34. Peritoneal Carcinosis: What the Radiologist Needs to Know.
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Reginelli A, Giacobbe G, Del Canto MT, Alessandrella M, Balestrucci G, Urraro F, Russo GM, Gallo L, Danti G, Frittoli B, Stoppino L, Schettini D, Iafrate F, Cappabianca S, Laghi A, Grassi R, Brunese L, Barile A, and Miele V
- Abstract
Peritoneal carcinosis is a condition characterized by the spread of cancer cells to the peritoneum, which is the thin membrane that lines the abdominal cavity. It is a serious condition that can result from many different types of cancer, including ovarian, colon, stomach, pancreatic, and appendix cancer. The diagnosis and quantification of lesions in peritoneal carcinosis are critical in the management of patients with the condition, and imaging plays a central role in this process. Radiologists play a vital role in the multidisciplinary management of patients with peritoneal carcinosis. They need to have a thorough understanding of the pathophysiology of the condition, the underlying neoplasms, and the typical imaging findings. In addition, they need to be aware of the differential diagnoses and the advantages and disadvantages of the various imaging methods available. Imaging plays a central role in the diagnosis and quantification of lesions, and radiologists play a critical role in this process. Ultrasound, computed tomography, magnetic resonance, and PET/CT scans are used to diagnose peritoneal carcinosis. Each imaging procedure has advantages and disadvantages, and particular imaging techniques are recommended based on patient conditions. Our aim is to provide knowledge to radiologists regarding appropriate techniques, imaging findings, differential diagnoses, and treatment options. With the advent of AI in oncology, the future of precision medicine appears promising, and the interconnection between structured reporting and AI is likely to improve diagnostic accuracy and treatment outcomes for patients with peritoneal carcinosis.
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- 2023
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35. Imaging of metabolic and overload disorders in tissues and organs.
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Bruno F, Albano D, Agostini A, Benenati M, Cannella R, Caruso D, Cellina M, Cozzi D, Danti G, De Muzio F, Gentili F, Giacobbe G, Gitto S, Grazzini G, Grazzini I, Messina C, Palmisano A, Palumbo P, Bruno A, Grassi F, Grassi R, Fusco R, Granata V, Giovagnoni A, Miele V, and Barile A
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- Humans, Magnetic Resonance Imaging methods, Hemochromatosis diagnosis, Hemochromatosis genetics, Iron Overload
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Metabolic and overload disorders are a heterogeneous group of relatively uncommon but important diseases. While imaging plays a key role in the early detection and accurate diagnosis in specific organs with a pivotal role in several metabolic pathways, most of these diseases affect different tissues as part of a systemic syndromes. Moreover, since the symptoms are often vague and phenotypes similar, imaging alterations can present as incidental findings, which must be recognized and interpreted in the light of further biochemical and histological investigations. Among imaging modalities, MRI allows, thanks to its multiparametric properties, to obtain numerous information on tissue composition, but many metabolic and accumulation alterations require a multimodal evaluation, possibly using advanced imaging techniques and sequences, not only for the detection but also for accurate characterization and quantification. The purpose of this review is to describe the different alterations resulting from metabolic and overload pathologies in organs and tissues throughout the body, with particular reference to imaging findings., (© 2023. The Author(s) under exclusive licence to Japan Radiological Society.)
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- 2023
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36. The Role of Dual-Energy CT in the Study of Urinary Tract Tumors: Review of Recent Literature.
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Galluzzo A, Danti G, Bicci E, Mastrorosato M, Bertelli E, and Miele V
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- Pregnancy, Humans, Female, Child, Tomography, X-Ray Computed methods, Contrast Media, Urologic Neoplasms diagnostic imaging, Urinary Bladder Neoplasms diagnostic imaging, Urinary Bladder Neoplasms pathology, Carcinoma, Transitional Cell pathology
- Abstract
Urothelial cancers are often detected incidentally because of an exponential growth in medical cross-sectional imaging. Nowadays there is the need for improved lesion characterization to distinguish clinically significant tumors from benign conditions. The gold standard for diagnosis of bladder cancer is cystoscopy, while for upper tract urothelial cancer computed tomographic urography and flexible ureteroscopy are more appropriate modalities. Computed tomography (CT) is the cornerstone in the assessment of locoregional and distant disease, using a protocol with precontrastographic and postcontrastographic phases. In particular, renal pelvis, ureter and bladder lesions can be assessed during the urography phase in the acquisition protocol of the urothelial tumors. Multiphasic CT is associated with overexposure to ionising radiation and repeated infusion of iodinated contrast media, which can be problematic especially in certain types of patients (allergic, nephropathic, pregnant women and in paediatric age). Dual-energy CT can overcome these difficulties with a number of methods, for example, by reconstructing virtual noncontrast images from a single-phase examination with contrast medium. In this review of the recent literature, we would like to highlight the role of Dual-energy CT in the diagnosis of urothelial cancer, its potential in this setting and possible advantages related to it., (Copyright © 2023 Elsevier Inc. All rights reserved.)
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- 2023
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37. Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma.
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Brunese MC, Fantozzi MR, Fusco R, De Muzio F, Gabelloni M, Danti G, Borgheresi A, Palumbo P, Bruno F, Gandolfo N, Giovagnoni A, Miele V, Barile A, and Granata V
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Background: This paper offers an assessment of radiomics tools in the evaluation of intrahepatic cholangiocarcinoma., Methods: The PubMed database was searched for papers published in the English language no earlier than October 2022., Results: We found 236 studies, and 37 satisfied our research criteria. Several studies addressed multidisciplinary topics, especially diagnosis, prognosis, response to therapy, and prediction of staging (TNM) or pathomorphological patterns. In this review, we have covered diagnostic tools developed through machine learning, deep learning, and neural network for the recurrence and prediction of biological characteristics. The majority of the studies were retrospective., Conclusions: It is possible to conclude that many performing models have been developed to make differential diagnosis easier for radiologists to predict recurrence and genomic patterns. However, all the studies were retrospective, lacking further external validation in prospective and multicentric cohorts. Furthermore, the radiomics models and the expression of results should be standardized and automatized to be applicable in clinical practice.
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- 2023
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38. Primary Lymphoproliferative Lung Diseases: Imaging and Multidisciplinary Approach.
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Gozzi L, Cozzi D, Cavigli E, Moroni C, Giannessi C, Zantonelli G, Smorchkova O, Ruzga R, Danti G, Bertelli E, Luzzi V, Pasini V, and Miele V
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Lymphoproliferative lung diseases are a heterogeneous group of disorders characterized by primary or secondary involvement of the lung. Primary pulmonary lymphomas are the most common type, representing 0.5-1% of all primary malignancies of the lung. The radiological presentation is often heterogeneous and non-specific: consolidations, masses, and nodules are the most common findings, followed by ground-glass opacities and interstitial involvement, more common in secondary lung lymphomas. These findings usually show a prevalent perilymphatic spread along bronchovascular bundles, without a prevalence in the upper or lower lung lobes. An ancillary sign, such as a "halo sign", "reverse halo sign", air bronchogram, or CT angiogram sign, may be present and can help rule out a differential diagnosis. Since a wide spectrum of pulmonary parenchymal diseases may mimic lymphoma, a correct clinical evaluation and a multidisciplinary approach are mandatory. In this sense, despite High-Resolution Computer Tomography (HRCT) representing the gold standard, a tissue sample is needed for a certain and definitive diagnosis. Cryobiopsy is a relatively new technique that permits the obtaining of a larger amount of tissue without significant artifacts, and is less invasive and more precise than surgical biopsy.
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- 2023
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39. Branch duct-intraductal papillary mucinous neoplasms (BD-IPMNs): an MRI-based radiomic model to determine the malignant degeneration potential.
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Flammia F, Innocenti T, Galluzzo A, Danti G, Chiti G, Grazzini G, Bettarini S, Tortoli P, Busoni S, Dragoni G, Gottin M, Galli A, and Miele V
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- Adult, Humans, Retrospective Studies, Pancreatic Ducts diagnostic imaging, Pancreatic Ducts pathology, Magnetic Resonance Imaging, Carcinoma, Pancreatic Ductal epidemiology, Carcinoma, Pancreatic Ductal pathology, Pancreatic Neoplasms diagnostic imaging, Neoplasms, Cystic, Mucinous, and Serous pathology
- Abstract
Background: Branch duct-intraductal papillary mucinous neoplasms (BD-IPMNs) are the most common pancreatic cystic tumors and have a low risk of malignant transformation. Features able to early identify high-risk BD-IPMNs are lacking, and guidelines currently rely on the occurrence of worrisome features (WF) and high-risk stigmata (HRS)., Aim: In our study, we aimed to use a magnetic resonance imaging (MRI) radiomic model to identify features linked to a higher risk of malignant degeneration, and whether these appear before the occurrence of WF and HRS., Methods: We retrospectively evaluated adult patients with a known BD-IPMN who had had at least two contrast-enhanced MRI studies at our center and a 24-month minimum follow-up time. MRI acquisition protocol for the two examinations included pre- and post-contrast phases and diffusion-weighted imaging (DWI)/apparent diffusion coefficient (ADC) map. Patients were divided into two groups according to the development of WF or HRS at the end of the follow-up (Group 0 = no WF or HRS; Group 1 = WF or HRS). We segmented the MRI images and quantitative features were extracted and compared between the two groups. Features that showed significant differences (SF) were then included in a LASSO regression method to build a radiomic-based predictive model., Results: We included 50 patients: 31 in Group 0 and 19 in Group 1. No patients in this cohort developed HRS. At baseline, 47, 67, 38, and 68 SF were identified for pre-contrast T1-weighted (T1-W) sequence, post-contrast T1-W sequence, T2-weighted (T2- W) sequence, and ADC map, respectively. At the end of follow-up, we found 69, 78, 53, and 91 SF, respectively. The radiomic-based predictive model identified 16 SF: more particularly, 5 SF for pre-contrast T1-W sequence, 6 for post-contrast T1-W sequence, 3 for T2-W sequence, and 2 for ADC., Conclusion: We identified radiomic features that correlate significantly with WF in patients with BD-IPMNs undergoing contrast-enhanced MRI. Our MRI-based radiomic model can predict the occurrence of WF., (© 2023. Italian Society of Medical Radiology.)
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- 2023
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40. Dual-Energy CT applications in urinary tract cancers: an update.
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Bicci E, Mastrorosato M, Danti G, Lattavo L, Bertelli E, Cozzi D, Pradella S, Agostini S, and Miele V
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- Humans, Hematuria etiology, Tomography, X-Ray Computed methods, Urologic Neoplasms diagnostic imaging, Urinary Bladder Neoplasms diagnostic imaging, Urinary Bladder Neoplasms pathology, Carcinoma, Transitional Cell pathology
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Urothelial tumours are the fourth most common cancer in the world and account for the majority of tumours involving the bladder. The symptom that often leads to diagnosis is the presence of haematuria. Diagnosis is made by cystoscopy, which is currently the gold standard in bladder cancer. Computed tomography (CT) performed with pre- and post-contrastographic phases is essential in order to assess the loco-regional and distant extension of disease. The diagnosis and staging of upper tract urothelial cancer (UTUC) are best done with computed tomography urography and flexible ureteroscopy (URS). In the acquisition protocol of this type of tumour, a urographic phase is mandatory, which allows for an accurate diagnostic assessment of the renal pelvis, ureter and bladder, especially in papillary forms. The use of multiple acquisition phases, especially in this type of patient who will have to perform follow-up CTs, leads to the problem of overexposure to ionising radiation, as well as the frequent administration of iodinated contrast medium. For this reason, in recent year, the focus has been put on advanced technologies such as dual-energy CT (DECT), that is a method that can offer some advantages for both radiologist and patient, in the diagnosis of cancer and, in particular, urinary tract disease.
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- 2023
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41. Dose Reduction Strategies for Pregnant Women in Emergency Settings.
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Picone C, Fusco R, Tonerini M, Fanni SC, Neri E, Brunese MC, Grassi R, Danti G, Petrillo A, Scaglione M, Gandolfo N, Giovagnoni A, Barile A, Miele V, Granata C, and Granata V
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In modern clinical practice, there is an increasing dependence on imaging techniques in several settings, and especially during emergencies. Consequently, there has been an increase in the frequency of imaging examinations and thus also an increased risk of radiation exposure. In this context, a critical phase is a woman's pregnancy management that requires a proper diagnostic assessment to reduce radiation risk to the fetus and mother. The risk is greatest during the first phases of pregnancy at the time of organogenesis. Therefore, the principles of radiation protection should guide the multidisciplinary team. Although diagnostic tools that do not employ ionizing radiation, such as ultrasound (US) and magnetic resonance imaging (MRI) should be preferred, in several settings as polytrauma, computed tomography (CT) nonetheless remains the examination to perform, beyond the fetus risk. In addition, protocol optimization, using dose-limiting protocols and avoiding multiple acquisitions, is a critical point that makes it possible to reduce risks. The purpose of this review is to provide a critical evaluation of emergency conditions, e.g., abdominal pain and trauma, considering the different diagnostic tools that should be used as study protocols in order to control the dose to the pregnant woman and fetus.
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- 2023
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42. Exploring Radiologists' Burnout in the COVID-19 Era: A Narrative Review.
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Gabelloni M, Faggioni L, Fusco R, De Muzio F, Danti G, Grassi F, Grassi R, Palumbo P, Bruno F, Borgheresi A, Bruno A, Catalano O, Gandolfo N, Giovagnoni A, Miele V, Barile A, and Granata V
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- Humans, Pandemics, Radiologists, Diagnostic Imaging adverse effects, COVID-19 epidemiology, Burnout, Professional epidemiology
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Since its beginning in March 2020, the COVID-19 pandemic has claimed an exceptionally high number of victims and brought significant disruption to the personal and professional lives of millions of people worldwide. Among medical specialists, radiologists have found themselves at the forefront of the crisis due to the pivotal role of imaging in the diagnostic and interventional management of COVID-19 pneumonia and its complications. Because of the disruptive changes related to the COVID-19 outbreak, a proportion of radiologists have faced burnout to several degrees, resulting in detrimental effects on their working activities and overall wellbeing. This paper aims to provide an overview of the literature exploring the issue of radiologists' burnout in the COVID-19 era.
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- 2023
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43. Post-Surgical Imaging Assessment in Rectal Cancer: Normal Findings and Complications.
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De Muzio F, Fusco R, Cutolo C, Giacobbe G, Bruno F, Palumbo P, Danti G, Grazzini G, Flammia F, Borgheresi A, Agostini A, Grassi F, Giovagnoni A, Miele V, Barile A, and Granata V
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Rectal cancer (RC) is one of the deadliest malignancies worldwide. Surgery is the most common treatment for RC, performed in 63.2% of patients. The type of surgical approach chosen aims to achieve maximum residual function with the lowest risk of recurrence. The selection is made by a multidisciplinary team that assesses the characteristics of the patient and the tumor. Total mesorectal excision (TME), including both low anterior resection (LAR) and abdominoperineal resection (APR), is still the standard of care for RC. Radical surgery is burdened by a 31% rate of major complications (Clavien-Dindo grade 3-4), such as anastomotic leaks and a risk of a permanent stoma. In recent years, less-invasive techniques, such as local excision, have been tested. These additional procedures could mitigate the morbidity of rectal resection, while providing acceptable oncologic results. The "watch and wait" approach is not a globally accepted model of care but encouraging results on selected groups of patients make it a promising strategy. In this plethora of treatments, the radiologist is called upon to distinguish a physiological from a pathological postoperative finding. The aim of this narrative review is to identify the main post-surgical complications and the most effective imaging techniques.
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- 2023
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44. Radiation Recall Pneumonitis: The Open Challenge in Differential Diagnosis of Pneumonia Induced by Oncological Treatments.
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Grassi F, Granata V, Fusco R, De Muzio F, Cutolo C, Gabelloni M, Borgheresi A, Danti G, Picone C, Giovagnoni A, Miele V, Gandolfo N, Barile A, Nardone V, and Grassi R
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The treatment of primary and secondary lung neoplasms now sees the fundamental role of radiotherapy, associated with surgery and systemic therapies. The improvement in survival outcomes has also increased attention to the quality of life, treatment compliance and the management of side effects. The role of imaging is not only limited to recognizing the efficacy of treatment but also to identifying, as soon as possible, the uncommon effects, especially when more treatments, such as chemotherapy, immunotherapy and radiotherapy, are associated. Radiation recall pneumonitis is an uncommon treatment complication that should be correctly characterized, and it is essential to recognize the mechanisms of radiation recall pneumonitis pathogenesis and diagnostic features in order to promptly identify them and adopt the best therapeutic strategy, with the shortest possible withdrawal of the current oncological drug. In this setting, artificial intelligence could have a critical role, although a larger patient data set is required.
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- 2023
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45. Reproducibility of CT radiomic features in lung neuroendocrine tumours (NETs) patients: analysis in a heterogeneous population.
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Bicci E, Cozzi D, Cavigli E, Ruzga R, Bertelli E, Danti G, Bettarini S, Tortoli P, Mazzoni LN, Busoni S, and Miele V
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- Humans, Ki-67 Antigen, Lung diagnostic imaging, Lung pathology, Lymphatic Metastasis, Reproducibility of Results, Tomography, X-Ray Computed methods, Lung Neoplasms diagnostic imaging, Lung Neoplasms pathology, Neuroendocrine Tumors diagnostic imaging, Neuroendocrine Tumors pathology
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Background: The aim is to find a correlation between texture features extracted from neuroendocrine (NET) lung cancer subtypes, both Ki-67 index and the presence of lymph-nodal mediastinal metastases detected while using different computer tomography (CT) scanners., Methods: Sixty patients with a confirmed pulmonary NET histological diagnosis, a known Ki-67 status and metastases, were included. After subdivision of primary lesions in baseline acquisition and venous phase, 107 radiomic features of first and higher orders were extracted. Spearman's correlation matrix with Ward's hierarchical clustering was applied to confirm the absence of bias due to the database heterogeneity. Nonparametric tests were conducted to identify statistically significant features in the distinction between patient groups (Ki-67 < 3-Group 1; 3 ≤ Ki-67 ≤ 20-Group 2; and Ki-67 > 20-Group 3, and presence of metastases)., Results: No bias arising from sample heterogeneity was found. Regarding Ki-67 groups statistical tests, seven statistically significant features (p value < 0.05) were found in post-contrast enhanced CT; three in baseline acquisitions. In metastasis classes distinction, three features (first-order class) were statistically significant in post-contrast acquisitions and 15 features (second-order class) in baseline acquisitions, including the three features distinguishing between Ki-67 groups in baseline images (MCC, ClusterProminence and Strength)., Conclusions: Some radiomic features can be used as a valid and reproducible tool for predicting Ki-67 class and hence the subtype of lung NET in baseline and post-contrast enhanced CT images. In particular, in baseline examination three features can establish both tumour class and aggressiveness., (© 2023. The Author(s).)
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- 2023
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46. Structured reporting of computed tomography in the polytrauma patient assessment: a Delphi consensus proposal.
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Granata V, Fusco R, Cozzi D, Danti G, Faggioni L, Buccicardi D, Prost R, Ferrari R, Trinci M, Galluzzo M, Iacobellis F, Scaglione M, Tonerini M, Coppola F, Bortolotto C, Caruso D, Ciaghi E, Gabelloni M, Rengo M, Giacobbe G, Grassi F, Romano L, Pinto A, Caranci F, Bertelli E, D'Andrea P, Neri E, Giovagnoni A, Grassi R, and Miele V
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- Humans, Delphi Technique, Consensus, Tomography, X-Ray Computed, Radiology, Multiple Trauma
- Abstract
Objectives: To develop a structured reporting (SR) template for whole-body CT examinations of polytrauma patients, based on the consensus of a panel of emergency radiology experts from the Italian Society of Medical and Interventional Radiology., Methods: A multi-round Delphi method was used to quantify inter-panelist agreement for all SR sections. Internal consistency for each section and quality analysis in terms of average inter-item correlation were evaluated by means of the Cronbach's alpha (Cα) correlation coefficient., Results: The final SR form included 118 items (6 in the "Patient Clinical Data" section, 4 in the "Clinical Evaluation" section, 9 in the "Imaging Protocol" section, and 99 in the "Report" section). The experts' overall mean score and sum of scores were 4.77 (range 1-5) and 257.56 (range 206-270) in the first Delphi round, and 4.96 (range 4-5) and 208.44 (range 200-210) in the second round, respectively. In the second Delphi round, the experts' overall mean score was higher than in the first round, and standard deviation was lower (3.11 in the second round vs 19.71 in the first round), reflecting a higher expert agreement in the second round. Moreover, Cα was higher in the second round than in the first round (0.97 vs 0.87)., Conclusions: Our SR template for whole-body CT examinations of polytrauma patients is based on a strong agreement among panel experts in emergency radiology and could improve communication between radiologists and the trauma team., (© 2023. The Author(s).)
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- 2023
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47. Risk Assessment and Cholangiocarcinoma: Diagnostic Management and Artificial Intelligence.
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Granata V, Fusco R, De Muzio F, Cutolo C, Grassi F, Brunese MC, Simonetti I, Catalano O, Gabelloni M, Pradella S, Danti G, Flammia F, Borgheresi A, Agostini A, Bruno F, Palumbo P, Ottaiano A, Izzo F, Giovagnoni A, Barile A, Gandolfo N, and Miele V
- Abstract
Intrahepatic cholangiocarcinoma (iCCA) is the second most common primary liver tumor, with a median survival of only 13 months. Surgical resection remains the only curative therapy; however, at first detection, only one-third of patients are at an early enough stage for this approach to be effective, thus rendering early diagnosis as an efficient approach to improving survival. Therefore, the identification of higher-risk patients, whose risk is correlated with genetic and pre-cancerous conditions, and the employment of non-invasive-screening modalities would be appropriate. For several at-risk patients, such as those suffering from primary sclerosing cholangitis or fibropolycystic liver disease, the use of periodic (6-12 months) imaging of the liver by ultrasound (US), magnetic Resonance Imaging (MRI)/cholangiopancreatography (MRCP), or computed tomography (CT) in association with serum CA19-9 measurement has been proposed. For liver cirrhosis patients, it has been proposed that at-risk iCCA patients are monitored in a similar fashion to at-risk HCC patients. The possibility of using Artificial Intelligence models to evaluate higher-risk patients could favor the diagnosis of these entities, although more data are needed to support the practical utility of these applications in the field of screening. For these reasons, it would be appropriate to develop screening programs in the research protocols setting. In fact, the success of these programs reauires patient compliance and multidisciplinary cooperation.
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- 2023
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48. Added prognostic value of molecular imaging parameters over proliferation index in typical lung carcinoid: an [18F]FDG PET/CT and SSTR imaging study.
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Linguanti F, Abenavoli EM, Briganti V, Danti G, Lavacchi D, Matteini M, Vaggelli L, Novelli L, Grosso AM, Mungai F, Mini E, Antonuzzo L, Miele V, Sciagrà R, and Berti V
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- Humans, Adult, Middle Aged, Aged, Aged, 80 and over, Positron Emission Tomography Computed Tomography, Fluorodeoxyglucose F18, Prognosis, Receptors, Somatostatin metabolism, Retrospective Studies, Ki-67 Antigen metabolism, Lung metabolism, Molecular Imaging, Cell Proliferation, Tumor Burden, Radiopharmaceuticals, Glycolysis, Lung Neoplasms pathology, Neuroendocrine Tumors, Carcinoma, Neuroendocrine, Carcinoid Tumor diagnostic imaging
- Abstract
Objective: This study was performed to evaluate the prognostic meaning of volumetric and semi-quantitative parameters measured using [18F]FDG PET/CT and somatostatin receptor (SSTR) imaging in patients with typical lung carcinoid (TC), and their relationship with proliferative index (Ki67)., Methods: We retrospectively reviewed 67 patients (38-94 years old, mean: 69.7) with diagnosis of TC who underwent [18F]FDG PET/CT and/or SSTR scintigraphy/SPECT with [111In]DTPA-Octreotide plus contrast-enhanced CT (CECT) at staging evaluation. All patients had Ki67 measured and a follow-up (FU) of at least 1 year. SSTR density (SSTRd) was calculated as the percentage difference of tumor/non-tumor ratio at 4 and 24 h post-injection. At PET/CT, metabolic activity was measured using SUVmax and SUVratio; volumetric parameters included MTV and TLG of the primary tumor, measured using the threshold SUV41%. ROC analysis, discriminant analysis and Kaplan-Meier curves (KM) were performed., Results: 11 patients died during FU. Disease stage (localized versus advanced), SUVratio, SUVmax, Ki67, MTV and TLG were significantly higher in non-survivors than in survivors. ROC curves resulted statistically significant for Ki67, SUVratio, SUVmax, MTV and TLG. On multivariate analysis, stage of disease and TLG were significant independent predictors of overall survival (OS). In KM curves, the combination of disease stage and TLG identified four groups with significantly different outcomes (p < 0.005). Metabolic activity (SUVmax and SUVratio) was confirmed as significant independent prognostic factor for OS also in patients with advanced disease, with the best AUC using SUVmax. In patients with advanced and localized disease, SSTRd proved to be the best imaging prognostic factor for progression and for disease-free survival (DFS), respectively. In localized disease, SSTRd 31.5% identified two subgroups of patients with significant different DFS distribution and in advanced disease, a high cutoff value (58.5%) was a significant predictor of adverse prognosis., Conclusion: Volumetric and semi-quantitative parameters measured using [18F]FDG PET/CT and SSTR imaging combined with Ki67 may provide a reference for prognosis evaluation of patients with TC, to better stratify risk groups with the goal of developing individualized therapeutic strategies., (© 2022. The Author(s).)
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- 2023
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49. Structured Reporting in Radiological Settings: Pitfalls and Perspectives.
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Granata V, De Muzio F, Cutolo C, Dell'Aversana F, Grassi F, Grassi R, Simonetti I, Bruno F, Palumbo P, Chiti G, Danti G, and Fusco R
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Objective: The aim of this manuscript is to give an overview of structured reporting in radiological settings., Materials and Method: This article is a narrative review on structured reporting in radiological settings. Particularly, limitations and future perspectives are analyzed., Results: The radiological report is a communication tool for the referring physician and the patients. It was conceived as a free text report (FTR) to allow radiologists to have their own individuality in the description of the radiological findings. However, this form could suffer from content, style, and presentation discrepancies, with a probability of transferring incorrect radiological data. Quality, datafication/quantification, and accessibility represent the three main goals in moving from FTRs to structured reports (SRs). In fact, the quality is related to standardization, which aims to improve communication and clarification. Moreover, a "structured" checklist, which allows all the fundamental items for a particular radiological study to be reported and permits the connection of the radiological data with clinical features, allowing a personalized medicine. With regard to accessibility, since radiological reports can be considered a source of research data, SR allows data mining to obtain new biomarkers and to help the development of new application domains, especially in the field of radiomics., Conclusions: Structured reporting could eliminate radiologist individuality, allowing a standardized approach.
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- 2022
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50. A Narrative Review on LI-RADS Algorithm in Liver Tumors: Prospects and Pitfalls.
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De Muzio F, Grassi F, Dell'Aversana F, Fusco R, Danti G, Flammia F, Chiti G, Valeri T, Agostini A, Palumbo P, Bruno F, Cutolo C, Grassi R, Simonetti I, Giovagnoni A, Miele V, Barile A, and Granata V
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Liver cancer is the sixth most detected tumor and the third leading cause of tumor death worldwide. Hepatocellular carcinoma (HCC) is the most common primary liver malignancy with specific risk factors and a targeted population. Imaging plays a major role in the management of HCC from screening to post-therapy follow-up. In order to optimize the diagnostic-therapeutic management and using a universal report, which allows more effective communication among the multidisciplinary team, several classification systems have been proposed over time, and LI-RADS is the most utilized. Currently, LI-RADS comprises four algorithms addressing screening and surveillance, diagnosis on computed tomography (CT)/magnetic resonance imaging (MRI), diagnosis on contrast-enhanced ultrasound (CEUS) and treatment response on CT/MRI. The algorithm allows guiding the radiologist through a stepwise process of assigning a category to a liver observation, recognizing both major and ancillary features. This process allows for characterizing liver lesions and assessing treatment. In this review, we highlighted both major and ancillary features that could define HCC. The distinctive dynamic vascular pattern of arterial hyperenhancement followed by washout in the portal-venous phase is the key hallmark of HCC, with a specificity value close to 100%. However, the sensitivity value of these combined criteria is inadequate. Recent evidence has proven that liver-specific contrast could be an important tool not only in increasing sensitivity but also in diagnosis as a major criterion. Although LI-RADS emerges as an essential instrument to support the management of liver tumors, still many improvements are needed to overcome the current limitations. In particular, features that may clearly distinguish HCC from cholangiocarcinoma (CCA) and combined HCC-CCA lesions and the assessment after locoregional radiation-based therapy are still fields of research.
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- 2022
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