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435 results on '"Stanzione, Arnaldo"'

6. The prostate-gland asymmetry affects the 3- and 12-month continence recovery after RARP in patients with small prostate glands: a single center study

Author

Di Bello, Francesco, Morra, Simone, Fraia, Agostino, Pezone, Gabriele, Polverino, Federico, Granata, Giuliano, Collà Ruvolo, Claudia, Napolitano, Luigi, Ponsiglione, Andrea, Stanzione, Arnaldo, La Rocca, Roberto, Balsamo, Raffaele, Creta, Massimiliano, Imbriaco, Massimo, Imbimbo, Ciro, Longo, Nicola, and Califano, Gianluigi

Published
2024
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8. METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII

Author

Kocak, Burak, Akinci D’Antonoli, Tugba, Mercaldo, Nathaniel, Alberich-Bayarri, Angel, Baessler, Bettina, Ambrosini, Ilaria, Andreychenko, Anna E., Bakas, Spyridon, Beets-Tan, Regina G. H., Bressem, Keno, Buvat, Irene, Cannella, Roberto, Cappellini, Luca Alessandro, Cavallo, Armando Ugo, Chepelev, Leonid L., Chu, Linda Chi Hang, Demircioglu, Aydin, deSouza, Nandita M., Dietzel, Matthias, Fanni, Salvatore Claudio, Fedorov, Andrey, Fournier, Laure S., Giannini, Valentina, Girometti, Rossano, Groot Lipman, Kevin B. W., Kalarakis, Georgios, Kelly, Brendan S., Klontzas, Michail E., Koh, Dow-Mu, Kotter, Elmar, Lee, Ho Yun, Maas, Mario, Marti-Bonmati, Luis, Müller, Henning, Obuchowski, Nancy, Orlhac, Fanny, Papanikolaou, Nikolaos, Petrash, Ekaterina, Pfaehler, Elisabeth, Pinto dos Santos, Daniel, Ponsiglione, Andrea, Sabater, Sebastià, Sardanelli, Francesco, Seeböck, Philipp, Sijtsema, Nanna M., Stanzione, Arnaldo, Traverso, Alberto, Ugga, Lorenzo, Vallières, Martin, van Dijk, Lisanne V., van Griethuysen, Joost J. M., van Hamersvelt, Robbert W., van Ooijen, Peter, Vernuccio, Federica, Wang, Alan, Williams, Stuart, Witowski, Jan, Zhang, Zhongyi, Zwanenburg, Alex, and Cuocolo, Renato

Published
2024
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11. Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study

Author

Saha, Anindo, Bosma, Joeran S., Twilt, Jasper J., van Ginneken, Bram, Noordman, Constant R., Slootweg, Ivan, Roest, Christian, Fransen, Stefan J., Sunoqrot, Mohammed R.S., Bathen, Tone F., Rouw, Dennis, Immerzeel, Jos, Geerdink, Jeroen, van Run, Chris, Groeneveld, Miriam, Meakin, James, Karagöz, Ahmet, Bône, Alexandre, Routier, Alexandre, Marcoux, Arnaud, Abi-Nader, Clément, Li, Cynthia Xinran, Feng, Dagan, Alis, Deniz, Karaarslan, Ercan, Ahn, Euijoon, Nicolas, François, Sonn, Geoffrey A., Bhattacharya, Indrani, Kim, Jinman, Shi, Jun, Jahanandish, Hassan, An, Hong, Kan, Hongyu, Oksuz, Ilkay, Qiao, Liang, Rohé, Marc-Michel, Yergin, Mert, Khadra, Mohamed, Şeker, Mustafa E., Kartal, Mustafa S., Debs, Noëlie, Fan, Richard E., Saunders, Sara, Soerensen, Simon J.C., Moroianu, Stefania, Vesal, Sulaiman, Yuan, Yuan, Malakoti-Fard, Afsoun, Mačiūnien, Agnė, Kawashima, Akira, de Sousa Machadov, Ana M.M. de M.G., Moreira, Ana Sofia L., Ponsiglione, Andrea, Rappaport, Annelies, Stanzione, Arnaldo, Ciuvasovas, Arturas, Turkbey, Baris, de Keyzer, Bart, Pedersen, Bodil G., Eijlers, Bram, Chen, Christine, Riccardo, Ciabattoni, Courrech Staal, Ewout F.W., Jäderling, Fredrik, Langkilde, Fredrik, Aringhieri, Giacomo, Brembilla, Giorgio, Son, Hannah, Vanderlelij, Hans, Raat, Henricus P.J., Pikūnienė, Ingrida, Macova, Iva, Schoots, Ivo, Caglic, Iztok, Zawaideh, Jeries P., Wallström, Jonas, Bittencourt, Leonardo K., Khurram, Misbah, Choi, Moon H., Takahashi, Naoki, Tan, Nelly, Franco, Paolo N., Gutierrez, Patricia A., Thimansson, Per Erik, Hanus, Pieter, Puech, Philippe, Rau, Philipp R., de Visschere, Pieter, Guillaume, Ramette, Cuocolo, Renato, Falcão, Ricardo O., van Stiphout, Rogier S.A., Girometti, Rossano, Briediene, Ruta, Grigienė, Rūta, Gitau, Samuel, Withey, Samuel, Ghai, Sangeet, Penzkofer, Tobias, Barrett, Tristan, Tammisetti, Varaha S., Løgager, Vibeke B., Černý, Vladimír, Venderink, Wulphert, Law, Yan M., Lee, Young J., Bjartell, Anders, Padhani, Anwar R., Bonekamp, David, Villeirs, Geert, Salomon, Georg, Giannarini, Gianluca, Kalpathy-Cramer, Jayashree, Barentsz, Jelle, Maier-Hein, Klaus H., Rusu, Mirabela, Obuchowski, Nancy A., Rouvière, Olivier, van den Bergh, Roderick, Panebianco, Valeria, Kasivisvanathan, Veeru, Yakar, Derya, Elschot, Mattijs, Veltman, Jeroen, Fütterer, Jurgen J., de Rooij, Maarten, Huisman, Henkjan, Bosma, Joeran S, Twilt, Jasper J, Padhani, Anwar R, Maier-Hein, Klaus H, Obuchowski, Nancy A, and Fütterer, Jurgen J

Published
2024
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19. Prediction of placenta accreta spectrum in patients with placenta previa using a clinical, US and MRI combined model: A retrospective study with external validation

Author

Maurea, Simone, Verde, Francesco, Romeo, Valeria, Stanzione, Arnaldo, Mainenti, Pier Paolo, Raia, Giorgio, Barbuto, Luigi, Iacobellis, Francesca, Santangelo, Fabrizia, Sarno, Laura, Migliorini, Sonia, Petretta, Mario, D'Armiento, Maria, De Dominicis, Gianfranco, Santangelo, Claudio, Guida, Maurizio, Romano, Luigia, and Brunetti, Arturo

Published
2023
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26. Clinical indications and acquisition protocol for the use of dynamic contrast-enhanced MRI in head and neck cancer squamous cell carcinoma: recommendations from an expert panel

Author

Romeo, Valeria, Stanzione, Arnaldo, Ugga, Lorenzo, Cuocolo, Renato, Cocozza, Sirio, Quarantelli, Mario, Chawla, Sanjeev, Farina, Davide, Golay, Xavier, Parker, Geoff, Shukla-Dave, Amita, Thoeny, Harriet, Vidiri, Antonello, Brunetti, Arturo, Surlan-Popovic, Katarina, and Bisdas, Sotirios

Published
2022
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27. Resolution Resampling of Ultrasound Images in Placenta Previa Patients: Influence on Radiomics Data Reliability and Usefulness for Machine Learning

Author

Ricciardi, Carlo, Cuocolo, Renato, Verde, Francesco, Improta, Giovanni, Stanzione, Arnaldo, Romeo, Valeria, Maurea, Simone, D’Armiento, Maria, Sarno, Laura, Guida, Maurizio, Cesarelli, Mario, Magjarevic, Ratko, Series Editor, Ładyżyński, Piotr, Associate Editor, Ibrahim, Fatimah, Associate Editor, Lackovic, Igor, Associate Editor, Rock, Emilio Sacristan, Associate Editor, Jarm, Tomaz, editor, Cvetkoska, Aleksandra, editor, Mahnič-Kalamiza, Samo, editor, and Miklavcic, Damijan, editor

Published
2021
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29. Clinical value of radiomics and machine learning in breast ultrasound: a multicenter study for differential diagnosis of benign and malignant lesions

Author

Romeo, Valeria, Cuocolo, Renato, Apolito, Roberta, Stanzione, Arnaldo, Ventimiglia, Antonio, Vitale, Annalisa, Verde, Francesco, Accurso, Antonello, Amitrano, Michele, Insabato, Luigi, Gencarelli, Annarita, Buonocore, Roberta, Argenzio, Maria Rosaria, Cascone, Anna Maria, Imbriaco, Massimo, Maurea, Simone, and Brunetti, Arturo

Published
2021
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45. Using a Recurrent Neural Network To Inform the Use of Prostate-specific Antigen (PSA) and PSA Density for Dynamic Monitoring of the Risk of Prostate Cancer Progression on Active Surveillance

Author

Sushentsev, Nikita, Abrego, Luis, Colarieti, Anna, Sanmugalingam, Nimalan, Stanzione, Arnaldo, Zawaideh, Jeries Paolo, Caglic, Iztok, Zaikin, Alexey, Blyuss, Oleg, Barrett, Tristan, Barrett, Tristan [0000-0002-1180-1474], and Apollo - University of Cambridge Repository

Subjects
Artificial intelligence, Prostate cancer, Recurrent neural networks, Longitudinal data, Urology, Predictive modelling, Active surveillance, Prostate-specific antigen
Abstract

UNLABELLED: The global uptake of prostate cancer (PCa) active surveillance (AS) is steadily increasing. While prostate-specific antigen density (PSAD) is an important baseline predictor of PCa progression on AS, there is a scarcity of recommendations on its use in follow-up. In particular, the best way of measuring PSAD is unclear. One approach would be to use the baseline gland volume (BGV) as a denominator in all calculations throughout AS (nonadaptive PSAD, PSADNA), while another would be to remeasure gland volume at each new magnetic resonance imaging scan (adaptive PSAD, PSADA). In addition, little is known about the predictive value of serial PSAD in comparison to PSA. We applied a long short-term memory recurrent neural network to an AS cohort of 332 patients and found that serial PSADNA significantly outperformed both PSADA and PSA for follow-up prediction of PCa progression because of its high sensitivity. Importantly, while PSADNA was superior in patients with smaller glands (BGV ≤55 ml), serial PSA was better in men with larger prostates of >55 ml. PATIENT SUMMARY: Repeat measurements of prostate-specific antigen (PSA) and PSA density (PSAD) are the mainstay of active surveillance in prostate cancer. Our study suggests that in patients with a prostate gland of 55 ml or smaller, PSAD measurements are a better predictor of tumour progression, whereas men with a larger gland may benefit more from PSA monitoring.

Published
2023

46. Resolution Resampling of Ultrasound Images in Placenta Previa Patients: Influence on Radiomics Data Reliability and Usefulness for Machine Learning

Author

Ricciardi, Carlo, primary, Cuocolo, Renato, additional, Verde, Francesco, additional, Improta, Giovanni, additional, Stanzione, Arnaldo, additional, Romeo, Valeria, additional, Maurea, Simone, additional, D’Armiento, Maria, additional, Sarno, Laura, additional, Guida, Maurizio, additional, and Cesarelli, Mario, additional

Published
2020
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48. Large language models in radiology: fundamentals, applications, ethical considerations, risks, and future directions.

Author

D'Antonoli, Tugba Akinci, Stanzione, Arnaldo, Bluethgen, Christian, Vernuccio, Federica, Ugga, Lorenzo, Klontzas, Michail E., Cuocolo, Renato, Cannella, Roberto, and Koçak, Burak

Subjects
LANGUAGE models, ARTIFICIAL intelligence in medicine, RADIOLOGISTS, CHATGPT, NATURAL language processing
Abstract

With the advent of large language models (LLMs), the artificial intelligence revolution in medicine and radiology is now more tangible than ever. Every day, an increasingly large number of articles are published that utilize LLMs in radiology. To adopt and safely implement this new technology in the field, radiologists should be familiar with its key concepts, understand at least the technical basics, and be aware of the potential risks and ethical considerations that come with it. In this review article, the authors provide an overview of the LLMs that might be relevant to the radiology community and include a brief discussion of their short history, technical basics, ChatGPT, prompt engineering, potential applications in medicine and radiology, advantages, disadvantages and risks, ethical and regulatory considerations, and future directions. [ABSTRACT FROM AUTHOR]

Published
2024
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49. Artificial Intelligence and Radiomics for Endometrial Cancer MRI: Exploring the Whats, Whys and Hows.

Author

Leo, Elisabetta, Stanzione, Arnaldo, Miele, Mariaelena, Cuocolo, Renato, Sica, Giacomo, Scaglione, Mariano, Camera, Luigi, Maurea, Simone, and Mainenti, Pier Paolo

Subjects
*RADIOMICS, *ENDOMETRIAL cancer, *ARTIFICIAL intelligence, *MAGNETIC resonance imaging, *PROGNOSIS
Abstract

Endometrial cancer (EC) is intricately linked to obesity and diabetes, which are widespread risk factors. Medical imaging, especially magnetic resonance imaging (MRI), plays a major role in EC assessment, particularly for disease staging. However, the diagnostic performance of MRI exhibits variability in the detection of clinically relevant prognostic factors (e.g., deep myometrial invasion and metastatic lymph nodes assessment). To address these challenges and enhance the value of MRI, radiomics and artificial intelligence (AI) algorithms emerge as promising tools with a potential to impact EC risk assessment, treatment planning, and prognosis prediction. These advanced post-processing techniques allow us to quantitatively analyse medical images, providing novel insights into cancer characteristics beyond conventional qualitative image evaluation. However, despite the growing interest and research efforts, the integration of radiomics and AI to EC management is still far from clinical practice and represents a possible perspective rather than an actual reality. This review focuses on the state of radiomics and AI in EC MRI, emphasizing risk stratification and prognostic factor prediction, aiming to illuminate potential advancements and address existing challenges in the field. [ABSTRACT FROM AUTHOR]

Published
2024
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50. Development and Validation of an Explainable Radiomics Model to Predict High-Aggressive Prostate Cancer: A Multicenter Radiomics Study Based on Biparametric MRI.

Author

Nicoletti, Giulia, Mazzetti, Simone, Maimone, Giovanni, Cignini, Valentina, Cuocolo, Renato, Faletti, Riccardo, Gatti, Marco, Imbriaco, Massimo, Longo, Nicola, Ponsiglione, Andrea, Russo, Filippo, Serafini, Alessandro, Stanzione, Arnaldo, Regge, Daniele, and Giannini, Valentina

Subjects
RESEARCH, CLINICAL decision support systems, MAGNETIC resonance imaging, RETROSPECTIVE studies, DESCRIPTIVE statistics, RESEARCH funding, PREDICTION models, RECEIVER operating characteristic curves, PROSTATE tumors
Abstract

Simple Summary: Prostate cancer (PCa) is one of the leading causes of mortality for men worldwide. PCa aggressiveness affects the patient's prognosis, with less aggressive tumors, i.e., Grade Group (GG) 1 and 2, having lower mortality and better outcomes. For this reason, the aim of this study is to distinguish between GG ≤ 2 and ≥3 PCa using an automatic and noninvasive approach based on artificial intelligence methods. The results obtained are promising, as the system achieved robust results on a multicenter external dataset. If further validated, this approach, combined with the expert knowledge of urologists, could help identify PCa patients who have a better prognosis and may benefit from less invasive treatments. In the last years, several studies demonstrated that low-aggressive (Grade Group (GG) ≤ 2) and high-aggressive (GG ≥ 3) prostate cancers (PCas) have different prognoses and mortality. Therefore, the aim of this study was to develop and externally validate a radiomic model to noninvasively classify low-aggressive and high-aggressive PCas based on biparametric magnetic resonance imaging (bpMRI). To this end, 283 patients were retrospectively enrolled from four centers. Features were extracted from apparent diffusion coefficient (ADC) maps and T2-weighted (T2w) sequences. A cross-validation (CV) strategy was adopted to assess the robustness of several classifiers using two out of the four centers. Then, the best classifier was externally validated using the other two centers. An explanation for the final radiomics signature was provided through Shapley additive explanation (SHAP) values and partial dependence plots (PDP). The best combination was a naïve Bayes classifier trained with ten features that reached promising results, i.e., an area under the receiver operating characteristic (ROC) curve (AUC) of 0.75 and 0.73 in the construction and external validation set, respectively. The findings of our work suggest that our radiomics model could help distinguish between low- and high-aggressive PCa. This noninvasive approach, if further validated and integrated into a clinical decision support system able to automatically detect PCa, could help clinicians managing men with suspicion of PCa. [ABSTRACT FROM AUTHOR]

Published
2024
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