Renato Cuocolo1, Arnaldo Stanzione2, Andrea Ponsiglione1, Valeria Romeo1, Francesco Verde1, Massimiliano Creta3, Roberto La Rocca3, Nicola Longo3, Leonardo Pace4, Massimo Imbriaco1. 1. Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy. 2. Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy. Electronic address: arnaldo.stanzione@unina.it. 3. Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples "Federico II", Naples, Italy. 4. Department of Medicine and Surgery, University of Salerno, Salerno, Italy.
Abstract
PURPOSE: Prostate multiparametric MRI (mpMRI) is the imaging modality of choice for detecting clinically significant prostate cancer (csPCa). Among various parameters, lesion maximum diameter and volume are currently considered of value to increase diagnostic accuracy. Quantitative radiomics allows for the extraction of more advanced shape features. Our aim was to assess which shape features derived from MRI index lesions correlate with csPCa presence. MATERIALS AND METHODS: We retrospectively enrolled 75 consecutive subjects, who underwent mpMRI on a 3 T scanner, divided based on MRI index lesion Gleason Score in a csPCa group (GS > 3 + 4, n = 41) and a non-csPCa one (n = 34). Ten shape features were extracted both from axial T2-weighted and ADC maps images, after lesion tridimensional segmentation. Univariable and multivariable logistic analysis were used to evaluate the relationship between shape features and csPCa. Diagnostic performance was assessed measuring the area under the curve of the receiver operating characteristic (ROC) analysis. Diagnostic accuracy, sensitivity, and specificity were determined using the best cut-off on each ROC. A P value < 0.05 was considered statistically significant. RESULTS: Univariable analysis demonstrated that almost every shape feature was statistically significant between csPCa e non-csPCa groups. However, multivariable analysis revealed that the parameter defined as surface area to volume ratio (SAVR), especially when extracted from ADC maps is the strongest independent predictor of csPCa among tested shape features. CONCLUSION: The radiomic shape feature SAVR, extracted from ADC maps after index lesion segmentation, appears as a promising tool for csPCa detection.
PURPOSE: Prostate multiparametric MRI (mpMRI) is the imaging modality of choice for detecting clinically significant prostate cancer (csPCa). Among various parameters, lesion maximum diameter and volume are currently considered of value to increase diagnostic accuracy. Quantitative radiomics allows for the extraction of more advanced shape features. Our aim was to assess which shape features derived from MRI index lesions correlate with csPCa presence. MATERIALS AND METHODS: We retrospectively enrolled 75 consecutive subjects, who underwent mpMRI on a 3 T scanner, divided based on MRI index lesion Gleason Score in a csPCa group (GS > 3 + 4, n = 41) and a non-csPCa one (n = 34). Ten shape features were extracted both from axial T2-weighted and ADC maps images, after lesion tridimensional segmentation. Univariable and multivariable logistic analysis were used to evaluate the relationship between shape features and csPCa. Diagnostic performance was assessed measuring the area under the curve of the receiver operating characteristic (ROC) analysis. Diagnostic accuracy, sensitivity, and specificity were determined using the best cut-off on each ROC. A P value < 0.05 was considered statistically significant. RESULTS: Univariable analysis demonstrated that almost every shape feature was statistically significant between csPCa e non-csPCa groups. However, multivariable analysis revealed that the parameter defined as surface area to volume ratio (SAVR), especially when extracted from ADC maps is the strongest independent predictor of csPCa among tested shape features. CONCLUSION: The radiomic shape feature SAVR, extracted from ADC maps after index lesion segmentation, appears as a promising tool for csPCa detection.
Authors: Rodrigo Delgadillo; John C Ford; Matthew C Abramowitz; Alan Dal Pra; Alan Pollack; Radka Stoyanova Journal: Strahlenther Onkol Date: 2020-08-21 Impact factor: 3.621
Authors: Teodora Telecan; Iulia Andras; Nicolae Crisan; Lorin Giurgiu; Emanuel Darius Căta; Cosmin Caraiani; Andrei Lebovici; Bianca Boca; Zoltan Balint; Laura Diosan; Monica Lupsor-Platon Journal: J Pers Med Date: 2022-06-16
Authors: Lorenzo Ugga; Renato Cuocolo; Domenico Solari; Elia Guadagno; Alessandra D'Amico; Teresa Somma; Paolo Cappabianca; Maria Laura Del Basso de Caro; Luigi Maria Cavallo; Arturo Brunetti Journal: Neuroradiology Date: 2019-08-02 Impact factor: 2.804
Authors: Harri Merisaari; Pekka Taimen; Rakesh Shiradkar; Otto Ettala; Marko Pesola; Jani Saunavaara; Peter J Boström; Anant Madabhushi; Hannu J Aronen; Ivan Jambor Journal: Magn Reson Med Date: 2019-11-08 Impact factor: 4.668