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1. Typical values of z-resolution for different Digital Breast Tomosynthesis systems evaluated in a multicenter study

Author

Dalmonte, S., Golinelli, P., Oberhofer, N., Strocchi, S., Rossetti, V., Berta, L., Porzio, M., Angelini, L., Paruccini, N., Villa, R., Bertolini, M., Delle Canne, S., Cavallari, M., D'Ercole, L., Guerra, G., Rosasco, R., Cannillo, B., D'Alessio, A., Di Nicola, E., Origgi, D., De Marco, P., Maldera, A., Scabbio, C., Rottoli, F., Castriconi, R., Lorenzini, E., Pasquali, G., Pietrobon, F., Bregant, P., Giovannini, G., Favuzza, V., Bruschi, A., D'Urso, D., Maestri, D., De Novellis, S., Fracassi, A., Boschiroli, L., Quattrocchi, M., Gilio, M.A., Roberto, E., Altabella, L., Califano, G., Cimmino, M.C., Bortoli, E., Deiana, E., Pagan, L., Berardi, P., Ardu, V., Azzeroni, R., Campoleoni, M., and Ravaglia, V.

Published
2024
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2. MULTICENTER INTERCOMPARISON ASSESSMENT OF QUANTITATIVE DIFFUSION-WEIGHTED MRI-DERIVED RADIOMIC FEATURES: A PHANTOM STUDY

Author

Altabella, L., primary, Fedeli, L., additional, Busoni, S., additional, Belli, G., additional, Ciccarone, A., additional, Coniglio, A., additional, Esposito, M., additional, Nocetti, L., additional, Sghedoni, R., additional, Tarducci, R., additional, Benelli, M., additional, Belligotti, E., additional, Bettarini, S., additional, Betti, M., additional, Caivano, R., additional, Carnì, M., additional, Chiappiniello, A., additional, Cimolai, S., additional, Cretti, F., additional, Fulcheri, C., additional, Gasperi, C., additional, Giacometti, M., additional, Levrero, F., additional, Lizio, D., additional, Maieron, M., additional, Marzi, S., additional, Mascaro, L., additional, Mazzocchi, S., additional, Meliadò, G., additional, Morzenti, S., additional, Niespolo, A., additional, Noferini, L., additional, Oberhofer, N., additional, Orsingher, L., additional, Quattrocchi, M., additional, Ricci, A., additional, Savini, A., additional, Taddeucci, A., additional, Testa, C., additional, Tortoli, P., additional, Gobbi, G., additional, Gori, C., additional, Bernardi, L., additional, Mazzoni, L.N., additional, and Giannelli, M., additional

Published
2023
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3. TYPICAL VALUES FOR Z-RESOLUTION OF DIFFERENT DIGITAL BREAST TOMOSYNTHESIS SYSTEMS EVALUATED IN A MULTICENTER STUDY

Author

Dalmonte, S., primary, Ravaglia, V., additional, Altabella, L., additional, Ardu, V., additional, Azzeroni, R., additional, Berardi, P., additional, Berta, L., additional, Bertolini, M., additional, Bortoli, E., additional, Boschiroli, L., additional, Bregant, P., additional, Bruschi, A., additional, Califano, G., additional, Cannillo, B., additional, Castriconi, R., additional, Cavallari, M., additional, Cimmino, M.C., additional, D’Alessio, A., additional, De Novellis, S., additional, Delle Canne, S., additional, De Marco, P., additional, D’Ercole, L., additional, Di Nicola, E., additional, D’Urso, D., additional, Favuzza, V., additional, Fracassi, A., additional, Gilio, M.A., additional, Giovannini, G., additional, Golinelli, P., additional, Guerra, G., additional, Lorenzini, E., additional, Maestri, D., additional, Maldera, A., additional, Moresco, P., additional, Origgi, D., additional, Pagan, L., additional, Paruccini, N., additional, Pasquali, G., additional, Pietrobon, F., additional, Porzio, M., additional, Quattrocchi, M., additional, Roberto, E., additional, Rosasco, R., additional, Rossetti, V., additional, Strocchi, S., additional, and Villa, R., additional

Published
2023
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5. ASSESSMENT OF VARIABILITY IN PERFORMANCES OF SCANNERS FOR FUNCTIONAL MAGNETIC RESONANCE IMAGING – A LARGE MULTICENTER INTERCOMPARISON PHANTOM STUDY

Author

Belmonte, G., primary, Altabella, L., additional, Balboni, E., additional, Belli, G., additional, Biscione, C., additional, Busoni, S., additional, Caivano, R., additional, Canonico, D., additional, Chiappiniello, A., additional, Ciccarone, A., additional, Cimolai, S., additional, Coniglio, A., additional, Costagli, M., additional, Cretti, F., additional, Esposito, M., additional, Fedeli, L., additional, Fontana, L., additional, Gasperi, C., additional, Giannelli, M., additional, Gobbi, G., additional, Gori, C., additional, Levrero, F., additional, Longo, R., additional, Marzi, S., additional, Mascaro, L., additional, Mazzocchi, S., additional, Mazzoni, L., additional, Nocetti, L., additional, Noferini, L., additional, Quattrocchi, M., additional, Scabbio, C., additional, Sghedoni, R., additional, Solla, I., additional, Stroppi, M., additional, Tarducci, R., additional, Maieron, M., additional, and Bruschi, A., additional

Published
2023
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6. VALIDATION OF A PREDICTIVE MODEL OF COMPLETE RESPONDERS TO NEOADJUVANT CHEMOTHERAPY IN BREAST CANCER ON AN INDEPENDENT SET

Author

Esposito, P.G., primary, Altabella, L., additional, Benetti, G., additional, Cardobi, N., additional, Olivieri, A.M., additional, Bisighin, M.V., additional, Camera, L., additional, Zerbato, C., additional, Caumo, F., additional, Fiorio, E., additional, Zanelli, S., additional, Montemezzi, S., additional, and Cavedon, C., additional

Published
2023
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7. TYPICAL VALUES FOR DIFFERENT DIGITAL BREAST TOMOSYNTHESIS SYSTEMS EVALUATED IN A MULTICENTER STUDY

Author

Ravaglia, V., primary, Altabella, L., additional, Ardu, V., additional, Azzeroni, R., additional, Berardi, P., additional, Bertolini, M., additional, Bortoli, E., additional, Boschiroli, L., additional, Bregant, P., additional, Bruschi, A., additional, Califano, G., additional, Cannillo, B., additional, Castriconi, R., additional, Cavallari, M., additional, Cimmino, M.C., additional, Cimolai, S., additional, Colombo, P.E., additional, D’Alessio, A., additional, D’Ercole, L., additional, De Marco, P., additional, Di Nicola, E., additional, Delle Canne, S., additional, Favuzza, V., additional, Gilio, M.A., additional, Golinelli, P., additional, Guerra, G., additional, Giovannini, G., additional, Lorenzini, E., additional, Maestri, D., additional, Maldera, A., additional, Moresco, P., additional, Origgi, D., additional, Pagan, L., additional, Paruccini, N., additional, Pasquali, G., additional, Pietrobon, F., additional, Porzio, M., additional, Quattrocchi, M., additional, Roberto, E., additional, Rosasco, R., additional, Rossetti, V., additional, Strocchi, S., additional, Turano, P., additional, and Villa, R., additional

Published
2023
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11. MO-07.3 - TYPICAL VALUES FOR DIFFERENT DIGITAL BREAST TOMOSYNTHESIS SYSTEMS EVALUATED IN A MULTICENTER STUDY

Author

Ravaglia, V., Altabella, L., Ardu, V., Azzeroni, R., Berardi, P., Bertolini, M., Bortoli, E., Boschiroli, L., Bregant, P., Bruschi, A., Califano, G., Cannillo, B., Castriconi, R., Cavallari, M., Cimmino, M.C., Cimolai, S., Colombo, P.E., D’Alessio, A., D’Ercole, L., De Marco, P., Di Nicola, E., Delle Canne, S., Favuzza, V., Gilio, M.A., Golinelli, P., Guerra, G., Giovannini, G., Lorenzini, E., Maestri, D., Maldera, A., Moresco, P., Origgi, D., Pagan, L., Paruccini, N., Pasquali, G., Pietrobon, F., Porzio, M., Quattrocchi, M., Roberto, E., Rosasco, R., Rossetti, V., Strocchi, S., Turano, P., and Villa, R.

Published
2023
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12. CO-18.1 - MULTICENTER INTERCOMPARISON ASSESSMENT OF QUANTITATIVE DIFFUSION-WEIGHTED MRI-DERIVED RADIOMIC FEATURES: A PHANTOM STUDY

Author

Altabella, L., Fedeli, L., Busoni, S., Belli, G., Ciccarone, A., Coniglio, A., Esposito, M., Nocetti, L., Sghedoni, R., Tarducci, R., Benelli, M., Belligotti, E., Bettarini, S., Betti, M., Caivano, R., Carnì, M., Chiappiniello, A., Cimolai, S., Cretti, F., Fulcheri, C., Gasperi, C., Giacometti, M., Levrero, F., Lizio, D., Maieron, M., Marzi, S., Mascaro, L., Mazzocchi, S., Meliadò, G., Morzenti, S., Niespolo, A., Noferini, L., Oberhofer, N., Orsingher, L., Quattrocchi, M., Ricci, A., Savini, A., Taddeucci, A., Testa, C., Tortoli, P., Gobbi, G., Gori, C., Bernardi, L., Mazzoni, L.N., and Giannelli, M.

Published
2023
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13. CO-13.1.2 - ASSESSMENT OF VARIABILITY IN PERFORMANCES OF SCANNERS FOR FUNCTIONAL MAGNETIC RESONANCE IMAGING – A LARGE MULTICENTER INTERCOMPARISON PHANTOM STUDY

Author

Belmonte, G., Altabella, L., Balboni, E., Belli, G., Biscione, C., Busoni, S., Caivano, R., Canonico, D., Chiappiniello, A., Ciccarone, A., Cimolai, S., Coniglio, A., Costagli, M., Cretti, F., Esposito, M., Fedeli, L., Fontana, L., Gasperi, C., Giannelli, M., Gobbi, G., Gori, C., Levrero, F., Longo, R., Marzi, S., Mascaro, L., Mazzocchi, S., Mazzoni, L., Nocetti, L., Noferini, L., Quattrocchi, M., Scabbio, C., Sghedoni, R., Solla, I., Stroppi, M., Tarducci, R., Maieron, M., and Bruschi, A.

Published
2023
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14. CO-12.2 - TYPICAL VALUES FOR Z-RESOLUTION OF DIFFERENT DIGITAL BREAST TOMOSYNTHESIS SYSTEMS EVALUATED IN A MULTICENTER STUDY

Author

Dalmonte, S., Ravaglia, V., Altabella, L., Ardu, V., Azzeroni, R., Berardi, P., Berta, L., Bertolini, M., Bortoli, E., Boschiroli, L., Bregant, P., Bruschi, A., Califano, G., Cannillo, B., Castriconi, R., Cavallari, M., Cimmino, M.C., D’Alessio, A., De Novellis, S., Delle Canne, S., De Marco, P., D’Ercole, L., Di Nicola, E., D’Urso, D., Favuzza, V., Fracassi, A., Gilio, M.A., Giovannini, G., Golinelli, P., Guerra, G., Lorenzini, E., Maestri, D., Maldera, A., Moresco, P., Origgi, D., Pagan, L., Paruccini, N., Pasquali, G., Pietrobon, F., Porzio, M., Quattrocchi, M., Roberto, E., Rosasco, R., Rossetti, V., Strocchi, S., and Villa, R.

Published
2023
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15. Reliability of quantitative diffusion weighted imaging: a large multicenter and multiparametric study based on a unsupervised clustering analysis

Author

Mazzoni, L.N., primary, Fedeli, L., additional, Belli, G., additional, Ciccarone, A., additional, Coniglio, A., additional, Esposito, M., additional, Giannelli, M., additional, Sghedoni, R., additional, Tarducci, R., additional, Altabella, L., additional, Belligotti, E., additional, Benelli, M., additional, Bernardi, L., additional, Betti, M., additional, Caivano, R., additional, Carnì, M., additional, Chiappiniello, A., additional, Cimolai, S., additional, Cretti, F., additional, Fulcheri, C., additional, Gasperi, C., additional, Giacometti, M., additional, Levrero, F., additional, Lizio, D., additional, Maieron, M., additional, Marzi, S., additional, Mascaro, L., additional, Mazzocchi, S., additional, Meliadò, G., additional, Morzenti, S., additional, Niespolo, A., additional, Nocetti, L., additional, Noferini, L., additional, Oberhofer, N., additional, Orsingher, L., additional, Quattrocchi, M.G., additional, Ricci, A., additional, Savini, A., additional, Taddeucci, A., additional, Testa, C., additional, Tortoli, P., additional, Luchinat, C., additional, Tenori, L., additional, Gobbi, G., additional, Gori, C., additional, and Busoni, S., additional

Published
2021
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16. Multicenter comparison of MR scanners for quantitative diffusion weighted imaging: apparent diffusion coefficient dependence on acquisition plan and spatial position – preliminary results

Author

Fedeli, L., primary, Belli, G., additional, Ciccarone, A., additional, Coniglio, A., additional, Esposito, M., additional, Giannelli, M., additional, Sghedoni, R., additional, Tarducci, R., additional, Altabella, L., additional, Belligotti, E., additional, Benelli, M., additional, Bernardi, L., additional, Betti, M., additional, Caivano, R., additional, Carni, M., additional, Chiappiniello, A., additional, Cimolai, S., additional, Cretti, F., additional, Fulcheri, C., additional, Gasperi, C., additional, Giacometti, M., additional, Levrero, F., additional, Lizio, D., additional, Maieron, M., additional, Marzi, S., additional, Mascaro, L., additional, Mazzocchi, S., additional, Meliado, G., additional, Morzenti, S., additional, Niespolo, A., additional, Nocetti, L., additional, Noferini, L., additional, Oberhofer, N., additional, Orsingher, L., additional, Quattrocchi, M.G., additional, Ricci, A., additional, Savini, A., additional, Taddeucci, A., additional, Testa, C., additional, Tortoli, P., additional, Luchinat, C., additional, Tenori, L., additional, Gobbi, G., additional, Gori, C., additional, Busoni, S., additional, and Mazzoni, L.N., additional

Published
2021
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20. PO-1572: Use of intravoxel incoherent motion (IVIM) MRI for predicting dysphagia in oropharyngeal carcinoma

Author

Maddalo, M., primary, Altabella, L., additional, Pegurri, L., additional, Guerini, A., additional, Peretto, G., additional, Alghisi, A., additional, Turla, O., additional, Guaineri, A., additional, Costantino, G., additional, Spiazzi, L., additional, Costa, L., additional, Mozzetti, C., additional, Pasinetti, N., additional, and Buglione, M., additional

Published
2020
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21. 20. Diffusion MRI and ADC accuracy at the isocenter: An AIFM multisite comparison study

Author

Coniglio, A., primary, Fedeli, L., additional, Belli, G., additional, Ciccarone, A., additional, Esposito, M., additional, Giannelli, M., additional, Gobbi, G., additional, Gori, C., additional, Mazzoni, L.N., additional, Nocetti, L., additional, Sghedoni, R., additional, Tarducci, R., additional, Altabella, L., additional, Belligotti, E., additional, Benelli, M., additional, Betti, M., additional, Caivano, R., additional, Carnì, M., additional, Chiappiniello, A., additional, Cimolai, S., additional, Cretti, F., additional, Fulcheri, C., additional, Gasperi, C., additional, Giacometti, M., additional, Levrero, F., additional, Lizio, D., additional, Luchinat, C., additional, Maieron, M., additional, Marzi, S., additional, Mascaro, L., additional, Mazzocchi, S., additional, Meliadò, G., additional, Morzenti, S., additional, Noferini, L., additional, Oberhofer, N., additional, Quattrocchi, M.G., additional, Ricci, A., additional, Taddeucci, A., additional, Tenori, L., additional, Torresin, A., additional, and Busoni, S., additional

Published
2018
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23. 45 Threshold dose for focal liver reaction

Author

Spiazzi, L., primary, Toraci, C., additional, Frassine, F., additional, Borghetti, P., additional, Altabella, L., additional, Vitali, P., additional, Polonini, A., additional, and Moretti, R, additional

Published
2018
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24. 18. Quality assurance in proton magnetic resonance spectroscopy: Final results of an intercomparison of 18 clinical scanner systems

Author

Sghedoni, R., primary, Coniglio, A., additional, Mazzoni, L.N., additional, Belli, G., additional, Tarducci, R., additional, Busoni, S., additional, Esposito, M., additional, Nocetti, L., additional, Ciccarone, A., additional, Altabella, L., additional, Levrero, F., additional, Fedeli, L., additional, Caivano, R., additional, Carnì, M., additional, Ricci, A., additional, Binotto, L., additional, Cimolai, S., additional, D’Urso, D., additional, Morzenti, S., additional, Gasperi, C., additional, Mangili, P., additional, Nitrosi, A., additional, Oberhofer, N., additional, Paruccini, N., additional, Toncelli, A., additional, Gobbi, G., additional, Gori, C., additional, and Giannelli Marco, M., additional

Published
2018
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27. 21 Phase encoding direction and position effects on ADC in diffusion MRI: An AFIM multisite comparison study

Author

Fedeli, L, primary, Belli, G., additional, Ciccarone, A., additional, Coniglio, A., additional, Esposito, M., additional, Giannelli, M., additional, Gobbi, G., additional, Gori, C., additional, Mazzoni, L.N., additional, Nocetti, L., additional, Sghedoni, R., additional, Tarducci, R., additional, Altabella, L., additional, Belligotti, E., additional, Benelli, M., additional, Betti, M., additional, Caivano, R., additional, Carnì, M., additional, Chiappiniello, A., additional, Cimolai, S., additional, Cretti, F., additional, Fulcheri, C., additional, Gasperi, C., additional, Giacometti, M., additional, Levrero, F., additional, Lizio, D., additional, Luchinat, C., additional, Maieron, M., additional, Marzi, S., additional, Mascaro, L., additional, Mazzocchi, S., additional, Meliadò, G., additional, Morzenti, S., additional, Noferini, L., additional, Oberhofer, N., additional, Quattrocchi, M.G., additional, Ricci, A., additional, Taddeucci, A., additional, Tenori, L., additional, Torresin, A., additional, and Busoni, S., additional

Published
2018
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28. P170 - Multicenter comparison of MR scanners for quantitative diffusion weighted imaging: apparent diffusion coefficient dependence on acquisition plan and spatial position – preliminary results

Author

Fedeli, L., Belli, G., Ciccarone, A., Coniglio, A., Esposito, M., Giannelli, M., Sghedoni, R., Tarducci, R., Altabella, L., Belligotti, E., Benelli, M., Bernardi, L., Betti, M., Caivano, R., Carni, M., Chiappiniello, A., Cimolai, S., Cretti, F., Fulcheri, C., Gasperi, C., Giacometti, M., Levrero, F., Lizio, D., Maieron, M., Marzi, S., Mascaro, L., Mazzocchi, S., Meliado, G., Morzenti, S., Niespolo, A., Nocetti, L., Noferini, L., Oberhofer, N., Orsingher, L., Quattrocchi, M.G., Ricci, A., Savini, A., Taddeucci, A., Testa, C., Tortoli, P., Luchinat, C., Tenori, L., Gobbi, G., Gori, C., Busoni, S., and Mazzoni, L.N.

Published
2021
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30. OD101 - Reliability of quantitative diffusion weighted imaging: a large multicenter and multiparametric study based on a unsupervised clustering analysis

Author

Mazzoni, L.N., Fedeli, L., Belli, G., Ciccarone, A., Coniglio, A., Esposito, M., Giannelli, M., Sghedoni, R., Tarducci, R., Altabella, L., Belligotti, E., Benelli, M., Bernardi, L., Betti, M., Caivano, R., Carnì, M., Chiappiniello, A., Cimolai, S., Cretti, F., Fulcheri, C., Gasperi, C., Giacometti, M., Levrero, F., Lizio, D., Maieron, M., Marzi, S., Mascaro, L., Mazzocchi, S., Meliadò, G., Morzenti, S., Niespolo, A., Nocetti, L., Noferini, L., Oberhofer, N., Orsingher, L., Quattrocchi, M.G., Ricci, A., Savini, A., Taddeucci, A., Testa, C., Tortoli, P., Luchinat, C., Tenori, L., Gobbi, G., Gori, C., and Busoni, S.

Published
2021
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42. Comparison of T1 mapping and fixed T1 method for dynamic contrast-enhanced MRI perfusion in brain gliomas

Author

Massimo Caulo, Luisa Altabella, Marco Grimaldi, Gian Marco Conte, Valeria Cuccarini, Antonella Castellano, Alberto Bizzi, Andrea Falini, Nicoletta Anzalone, Antonella Costa, Conte, G M, Altabella, L, Castellano, A, Cuccarini, V, Bizzi, A, Grimaldi, M, Costa, A, Caulo, M, Falini, A, and Anzalone, N

Subjects
Adult, Male, medicine.medical_specialty, Wilcoxon signed-rank test, Contrast Media, Perfusion scanning, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, Young Adult, Magnetic resonance imaging, 0302 clinical medicine, Flip angle, Histogram, Glioma, Medicine, Humans, Radiology, Nuclear Medicine and imaging, Aged, Retrospective Studies, Aged, 80 and over, medicine.diagnostic_test, business.industry, Brain Neoplasms, Ultrasound, Brain, General Medicine, Middle Aged, Perfusion imaging, medicine.disease, Magnetic Resonance Imaging, ROC Curve, 030220 oncology & carcinogenesis, Dynamic contrast-enhanced MRI, Female, Radiology, Neoplasm Grading, Nuclear medicine, business
Abstract

To compare dynamic contrast-enhanced MRI (DCE-MRI) data obtained using different prebolus T1 values in glioma grading and molecular profiling. We retrospectively reviewed 83 cases of gliomas: 46 lower-grade gliomas (LGG; grades II and III) and 37 high-grade gliomas (HGG; grade IV). DCE-MRI maps of plasma volume fraction (Vp), extravascular-extracellular volume fraction (Ve), and tracer transfer constant from plasma to tissue (Ktrans) were obtained using a fixed T1 value of 1400 ms and a measured T1 obtained with variable flip angle (VFA). Tumour segmentations were performed and first-order histogram parameters were extracted from volumes of interest (VOIs) after co-registration with the perfusion maps. The two methods were compared using Wilcoxon matched-pairs signed-rank test and Bland-Altman analysis. Diagnostic accuracy was obtained and compared using ROC curve analysis and DeLong’s test. Perfusion parameters obtained with the fixed T1 value were significantly higher than those obtained with the VFA. As regards diagnostic accuracy, there were no significant differences between the two methods both for glioma grading and molecular classification, except for few parameters of both methods. DCE-MRI data obtained with different prebolus T1 are not comparable and the definition of a prebolus T1 by T1 mapping is not mandatory since it does not improve the diagnostic accuracy of DCE-MRI. • DCE-MRI data obtained with different prebolus T1 are significantly different, thus not comparable. • The definition of a prebolus T1 by T1 mapping is not mandatory since it does not improve the diagnostic accuracy of DCE-MRI for glioma grading. • The use of a fixed T1 value represents a valid alternative to T1 mapping for DCE-MRI analysis.

Published
2018

43. T1-Weighted Dynamic Contrast-Enhanced MRI Is a Noninvasive Marker of Epidermal Growth Factor Receptor vIII Status in Cancer Stem Cell–Derived Experimental Glioblastomas

Author

Marco Peviani, Marcello Cadioli, Stefania Mazzoleni, Letterio S. Politi, Luisa Altabella, Andrea Falini, Gianluca Brugnara, Antonella Castellano, Rossella Galli, Politi, L, Brugnara, G, Castellano, Antonella, Cadioli, M, Altabella, L, Peviani, M, Mazzoleni, S, Falini, Andrea, and Galli, R.

Subjects
Adult, Male, 0301 basic medicine, Pathology, medicine.medical_specialty, medicine.disease_cause, Article, Statistics, Nonparametric, 03 medical and health sciences, ErbB Receptors, 0302 clinical medicine, Cancer stem cell, Biomarkers, Tumor, medicine, Humans, Radiology, Nuclear Medicine and imaging, Epidermal growth factor receptor, Mutation, medicine.diagnostic_test, biology, Brain Neoplasms, business.industry, Magnetic resonance imaging, Middle Aged, medicine.disease, Magnetic Resonance Imaging, 030104 developmental biology, ROC Curve, 030220 oncology & carcinogenesis, Dynamic contrast-enhanced MRI, biology.protein, Biomarker (medicine), Female, Neurology (clinical), Glioblastoma, business
Abstract

Epidermal growth factor receptor variant III is a common mutation in glioblastoma, found in approximately 25% of tumors. Epidermal growth factor receptor variant III may accelerate angiogenesis in malignant gliomas. We correlated T1-weighted dynamic contrast-enhanced MR imaging perfusion parameters with epidermal growth factor receptor variant III status.Eighty-two consecutive patients with glioblastoma and known epidermal growth factor receptor variant III status who had dynamic contrast-enhanced MR imaging before surgery were evaluated. Volumes of interest were drawn around the entire enhancing tumor on contrast T1-weighted images and then were transferred onto coregistered dynamic contrast-enhanced MR imaging perfusion maps. Histogram analysis with normalization was performed to determine the relative mean, 75th percentile, and 90th percentile values for plasma volume and contrast transfer coefficient. A Wilcoxon rank sum test was applied to assess the relationship between baseline perfusion parameters and positive epidermal growth factor receptor variant III status. The receiver operating characteristic method was used to select the cutoffs of the dynamic contrast-enhanced MR imaging perfusion parameters.Increased relative plasma volume and increased relative contrast transfer coefficient parameters were both significantly associated with positive epidermal growth factor receptor variant III status. For epidermal growth factor receptor variant III-positive tumors, relative plasma volume mean was 9.3 and relative contrast transfer coefficient mean was 6.5; for epidermal growth factor receptor variant III-negative tumors, relative plasma volume mean was 3.6 and relative contrast transfer coefficient mean was 3.7 (relative plasma volume mean, P.001, and relative contrast transfer coefficient mean, P = .008). The predictive powers of relative plasma volume histogram metrics outperformed those of the relative contrast transfer coefficient histogram metrics (P= .004).Dynamic contrast-enhanced MR imaging shows greater perfusion and leakiness in epidermal growth factor receptor variant III-positive glioblastomas than in epidermal growth factor receptor variant III-negative glioblastomas, consistent with the known effect of epidermal growth factor receptor variant III on angiogenesis. Quantitative evaluation of dynamic contrast-enhanced MR imaging may be useful as a noninvasive tool for correlating epidermal growth factor receptor variant III expression and related tumor neoangiogenesis. This potential may have implications for monitoring response to epidermal growth factor receptor variant III-targeted therapies.

Published
2016

44. Reproducibility of dynamic contrast-enhanced MRI and dynamic susceptibility contrast MRI in the study of brain gliomas: a comparison of data obtained using different commercial software

Author

Luisa Altabella, Andrea Falini, Nicoletta Anzalone, Gian Marco Conte, Marcello Cadioli, Antonella Iadanza, Antonella Castellano, Conte G., M, Castellano, Antonella, Altabella, L, Iadanza, A, Cadioli, M, Falini, A, and Anzalone, N.

Subjects
Adult, Male, Adolescent, Intraclass correlation, Contrast Media, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Software, Image Interpretation, Computer-Assisted, Organometallic Compounds, Medicine, Humans, Radiology, Nuclear Medicine and imaging, Aged, Retrospective Studies, Reproducibility, Commercial software, business.industry, Brain Neoplasms, Contrast (statistics), Reproducibility of Results, General Medicine, Glioma, Middle Aged, Image Enhancement, Magnetic Resonance Imaging, Dynamic contrast-enhanced MRI, Female, Deconvolution, business, Nuclear medicine, 030217 neurology & neurosurgery, Algorithms, Dynamic susceptibility
Abstract

Dynamic susceptibility contrast MRI (DSC) and dynamic contrast-enhanced MRI (DCE) are useful tools in the diagnosis and follow-up of brain gliomas; nevertheless, both techniques leave the open issue of data reproducibility. We evaluated the reproducibility of data obtained using two different commercial software for perfusion maps calculation and analysis, as one of the potential sources of variability can be the software itself. DSC and DCE analyses from 20 patients with gliomas were tested for both the intrasoftware (as intraobserver and interobserver reproducibility) and the intersoftware reproducibility, as well as the impact of different postprocessing choices [vascular input function (VIF) selection and deconvolution algorithms] on the quantification of perfusion biomarkers plasma volume (Vp), volume transfer constant (K trans) and rCBV. Data reproducibility was evaluated with the intraclass correlation coefficient (ICC) and Bland–Altman analysis. For all the biomarkers, the intra- and interobserver reproducibility resulted in almost perfect agreement in each software, whereas for the intersoftware reproducibility the value ranged from 0.311 to 0.577, suggesting fair to moderate agreement; Bland–Altman analysis showed high dispersion of data, thus confirming these findings. Comparisons of different VIF estimation methods for DCE biomarkers resulted in ICC of 0.636 for K trans and 0.662 for Vp; comparison of two deconvolution algorithms in DSC resulted in an ICC of 0.999. The use of single software ensures very good intraobserver and interobservers reproducibility. Caution should be taken when comparing data obtained using different software or different postprocessing within the same software, as reproducibility is not guaranteed anymore.

Published
2016

45. Persistent modification of forebrain networks and metabolism in rats following adolescent exposure to a 5-HT7 receptor agonist

Author

Rossella Canese, Gianvito Martino, Luisa Altabella, Francesco de Pasquale, Francesca Zoratto, Enza Lacivita, Laura Mercurio, Giovanni Laviola, Paola Porcari, Erica Butti, Marcello Leopoldo, Walter Adriani, Canese, R, Zoratto, F, Altabella, L, Porcari, P, Mercurio, L, de Pasquale, F, Butti, E, Martino, Gianvito, Lacivita, E, Leopoldo, M, Laviola, G, and Adriani, W.

Subjects
Male, Agonist, medicine.medical_specialty, medicine.drug_class, Hippocampus, Amygdala, Piperazines, 5-HT7 receptor, Prosencephalon, Internal medicine, medicine, Animals, Rats, Wistar, Receptor, Pharmacology, Age Factors, Magnetic Resonance Imaging, Rats, Serotonin Receptor Agonists, medicine.anatomical_structure, Endocrinology, Receptors, Serotonin, Forebrain, 5-HT6 receptor, Serotonin, Nerve Net, Psychology, Neuroscience
Abstract

The serotonin 7 receptor (5-HT7-R) is part of a neuro-transmission system with a proposed role in neural plasticity and in mood, cognitive or sleep regulation.We investigated long-term consequences of sub-chronic treatment, during adolescence (43-45 to 47-49 days old) in rats, with a novel 5-HT7-R agonist (LP-211, 0 or 0.250 mg/kg/day).We evaluated behavioural changes as well as forebrain structural/functional modifications by in vivo magnetic resonance (MR) in a 4.7 T system, followed by ex vivo histology.Adult rats pre-treated during adolescence showed reduced anxiety-related behaviour, in terms of reduced avoidance in the light/dark test and a less fragmented pattern of exploration in the novel object recognition test. Diffusion tensor imaging (DTI) revealed decreased mean diffusivity (MD) in the amygdala, increased fractional anisotropy (FA) in the hippocampus (Hip) and reduced axial (D||) together with increased radial (D⊥) diffusivity in the nucleus accumbens (NAcc). An increased neural dendritic arborization was confirmed in the NAcc by ex vivo histology. Seed-based functional MR imaging (fMRI) identified increased strength of connectivity within and between "limbic" and "cortical" loops, with affected cross-correlations between amygdala, NAcc and Hip. The latter displayed enhanced connections through the dorsal striatum (dStr) to dorso-lateral prefrontal cortex (dl-PFC) and cerebellum. Functional connection also increased between amygdala and limbic elements such as NAcc, orbito-frontal cortex (OFC) and hypothalamus. MR spectroscopy (1H-MRS) indicated that adolescent LP-211 exposure increased glutamate and total creatine in the adult Hip.Persistent MR-detectable modifications indicate a rearrangement within forebrain networks, accounting for long-lasting behavioural changes as a function of developmental 5-HT7-R stimulation.

Published
2015

46. Machine learning for multi-parametric breast MRI: radiomics-based approaches for lesion classification.

Author

Altabella L, Benetti G, Camera L, Cardano G, Montemezzi S, and Cavedon C

Subjects
Machine Learning, Magnetic Resonance Imaging methods, Mammography, Retrospective Studies, Artificial Intelligence, Breast Density
Abstract

In the artificial intelligence era, machine learning (ML) techniques have gained more and more importance in the advanced analysis of medical images in several fields of modern medicine. Radiomics extracts a huge number of medical imaging features revealing key components of tumor phenotype that can be linked to genomic pathways. The multi-dimensional nature of radiomics requires highly accurate and reliable machine-learning methods to create predictive models for classification or therapy response assessment.Multi-parametric breast magnetic resonance imaging (MRI) is routinely used for dense breast imaging as well for screening in high-risk patients and has shown its potential to improve clinical diagnosis of breast cancer. For this reason, the application of ML techniques to breast MRI, in particular to multi-parametric imaging, is rapidly expanding and enhancing both diagnostic and prognostic power. In this review we will focus on the recent literature related to the use of ML in multi-parametric breast MRI for tumor classification and differentiation of molecular subtypes. Indeed, at present, different models and approaches have been employed for this task, requiring a detailed description of the advantages and drawbacks of each technique and a general overview of their performances., (© 2022 Institute of Physics and Engineering in Medicine.)

Published
2022
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47. Computed tomography-based radiomic to predict resectability in locally advanced pancreatic cancer treated with chemotherapy and radiotherapy.

Author

Rossi G, Altabella L, Simoni N, Benetti G, Rossi R, Venezia M, Paiella S, Malleo G, Salvia R, Guariglia S, Bassi C, Cavedon C, and Mazzarotto R

Abstract

Background: Surgical resection after neoadjuvant treatment is the main driver for improved survival in locally advanced pancreatic cancer (LAPC). However, the diagnostic performance of computed tomography (CT) imaging to evaluate the residual tumour burden at restaging after neoadjuvant therapy is low due to the difficulty in distinguishing neoplastic tissue from fibrous scar or inflammation. In this context, radiomics has gained popularity over conventional imaging as a complementary clinical tool capable of providing additional, unprecedented information regarding the intratumor heterogeneity and the residual neoplastic tissue, potentially serving in the therapeutic decision-making process., Aim: To assess the capability of radiomic features to predict surgical resection in LAPC treated with neoadjuvant chemotherapy and radiotherapy., Methods: Patients with LAPC treated with intensive chemotherapy followed by ablative radiation therapy were retrospectively reviewed. One thousand six hundred and fifty-five radiomic features were extracted from planning CT inside the gross tumour volume. Both extracted features and clinical data contribute to create and validate the predictive model of resectability status. Patients were repeatedly divided into training and validation sets. The discriminating performance of each model, obtained applying a LASSO regression analysis, was assessed with the area under the receiver operating characteristic curve (AUC). The validated model was applied to the entire dataset to obtain the most significant features., Results: Seventy-one patients were included in the analysis. Median age was 65 years and 57.8% of patients were male. All patients underwent induction chemotherapy followed by ablative radiotherapy, and 19 (26.8%) ultimately received surgical resection. After the first step of variable selections, a predictive model of resectability was developed with a median AUC for training and validation sets of 0.862 (95%CI: 0.792-0.921) and 0.853 (95%CI: 0.706-0.960), respectively. The validated model was applied to the entire dataset and 4 features were selected to build the model with predictive performance as measured using AUC of 0.944 (95%CI: 0.892-0.996)., Conclusion: The present radiomic model could help predict resectability in LAPC after neoadjuvant chemotherapy and radiotherapy, potentially integrating clinical and morphological parameters in predicting surgical resection., Competing Interests: Conflict-of-interest statement: We have no financial relationships to disclose., (©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.)

Published
2022
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48. Automatic feathering algorithm for VMAT craniospinal irradiation: A comprehensive comparison with other VMAT planning strategies.

Author

Maddalo M, Benecchi G, Altabella L, Ghetti C, and D'Abbiero N

Subjects
Algorithms, Humans, Radiotherapy Dosage, Radiotherapy Planning, Computer-Assisted, Retrospective Studies, Craniospinal Irradiation, Radiotherapy, Intensity-Modulated
Abstract

In craniospinal irradiation, field matching is very sensitive to intrafraction positional uncertainties in cranio-caudal direction, which could lead to severe overdoses/underdoses inside the planning target volume. During the last decade, significant efforts were made to develop volumetric-modulated arc therapy strategies, which were less sensitive to setup uncertainties. In this study, a treatment planning system-integrated method, named automatic feathering (AF) algorithm, was compared against other volumetric-modulated arc therapy strategies. Three patients were retrospectively included. Five different planning techniques were compared, including overlap (O), staggered overlap (SO), gradient optimization (GO), overlap with AF algorithm turned on (O-AF), and staggered overlap with AF algorithm turned on (SO-AF). Three overlapping lengths were considered (5 cm, 7.5 cm, and 10 cm). The middle isocenter was shifted of ±1 mm, ±3 mm, and ±5 mm to simulate setup uncertainties. Plan robustness against simulated uncertainties was evaluated by calculating near maximum and near minimum dose differences between shifted and nonshifted plans (ΔD 2% , ΔD 98% ). Dose differences among combinations of techniques and junction lengths were tested using Wilcoxon signed-rank test. Higher ΔD 2% and ΔD 98% were obtained using the overlap technique (ΔD 2%  = 15.4%, ΔD 98%  = 15.0%). O-AF and SO-AF provided comparable plan robustness to GO technique. Their performance improved significantly for grater overlapping length. For 10-cm overlap and 5-mm shift, GO, O-AF, and SO-AF yielded to the better plan robustness (5.7% < ΔD 2% < 6.0%, 6.1% < ΔD 98% < 7.6%). SO provided an intermediate plan robustness (9.8% < ΔD 2% < 10.8%, 8.9% < ΔD 98% < 10.3%). The addition of AF to the overlap technique significantly improves plan robustness especially if larger overlapping lengths are used. Using the AF algorithm, plans become as robust as plans optimized with more sophisticated and time-consuming approaches (like GO)., (Copyright © 2020 American Association of Medical Dosimetrists. Published by Elsevier Inc. All rights reserved.)

Published
2021
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49. On the dependence of quantitative diffusion-weighted imaging on scanner system characteristics and acquisition parameters: A large multicenter and multiparametric phantom study with unsupervised clustering analysis.

Author

Fedeli L, Benelli M, Busoni S, Belli G, Ciccarone A, Coniglio A, Esposito M, Nocetti L, Sghedoni R, Tarducci R, Altabella L, Belligotti E, Bettarini S, Betti M, Caivano R, Carnì M, Chiappiniello A, Cimolai S, Cretti F, Fulcheri C, Gasperi C, Giacometti M, Levrero F, Lizio D, Maieron M, Marzi S, Mascaro L, Mazzocchi S, Meliadò G, Morzenti S, Niespolo A, Noferini L, Oberhofer N, Orsingher L, Quattrocchi M, Ricci A, Savini A, Taddeucci A, Testa C, Tortoli P, Gobbi G, Gori C, Bernardi L, Giannelli M, and Mazzoni LN

Subjects
Cluster Analysis, Diffusion, Phantoms, Imaging, Reproducibility of Results, Diffusion Magnetic Resonance Imaging
Abstract

Purpose: The purpose of this multicenter phantom study was to exploit an innovative approach, based on an extensive acquisition protocol and unsupervised clustering analysis, in order to assess any potential bias in apparent diffusion coefficient (ADC) estimation due to different scanner characteristics. Moreover, we aimed at assessing, for the first time, any effect of acquisition plan/phase encoding direction on ADC estimation., Methods: Water phantom acquisitions were carried out on 39 scanners. DWI acquisitions (b-value = 0-200-400-600-800-1000 s/mm 2 ) with different acquisition plans (axial, coronal, sagittal) and phase encoding directions (anterior/posterior and right/left, for the axial acquisition plan), for 3 orthogonal diffusion weighting gradient directions, were performed. For each acquisition setup, ADC values were measured in-center and off-center (6 different positions), resulting in an entire dataset of 84 × 39 = 3276 ADC values. Spatial uniformity of ADC maps was assessed by means of the percentage difference between off-center and in-center ADC values (Δ)., Results: No significant dependence of in-center ADC values on acquisition plan/phase encoding direction was found. Ward unsupervised clustering analysis showed 3 distinct clusters of scanners and an association between Δ-values and manufacturer/model, whereas no association between Δ-values and maximum gradient strength, slew rate or static magnetic field strength was revealed. Several acquisition setups showed significant differences among groups, indicating the introduction of different biases in ADC estimation., Conclusions: Unsupervised clustering analysis of DWI data, obtained from several scanners using an extensive acquisition protocol, allows to reveal an association between measured ADC values and manufacturer/model of scanner, as well as to identify suboptimal DWI acquisition setups for accurate ADC estimation., (Copyright © 2021 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.)

Published
2021
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50. Publisher Correction: Mice repeatedly exposed to Group-A β-Haemolytic Streptococcus show perseverative behaviors, impaired sensorimotor gating, and immune activation in rostral diencephalon.

Author

Macrì S, Ceci C, Proietti Onori M, Invernizzi RW, Bartolini E, Altabella L, Canese R, Imperi M, Orefici G, Creti R, Margarit I, Magliozzi R, and Laviola G

Abstract

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Published
2020
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