The aim of this study was to evaluate the effectiveness of [F-18]-prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) imaging in discriminating high pathological grade (Gleason score > 7), and low pathological grade (Gleason score < 7) usingmachine learning techniques. The study involved 81 patientswith diagnosed prostate cancer who underwent positive [F-18]-SPMA PET/CT scans. The PET images were used to identify the primary lesions, and then radiomics analyses were performed using an Imaging Biomarker Standardization Initiative (IBSI) compliant software, namely matRadiomics. Machine learning approaches were employed to identify relevant radiomics features for predicting high-risk malignant disease. The performance of the models was validated using 10 times repeated 5-fold cross validation scheme. The results showed a value of 0.75 for the area under the curve and an accuracy of 72% using the support vector machine (SVM). In conclusion, the study showcased the c
Prediction of High Pathological Grade in Prostate Cancer Patients Undergoing [18F]-PSMA PET/CT: A Preliminary Radiomics Study / Stefano, Alessandro; Mantarro, Cristina; Richiusa, Selene; Pasini, Giovanni; Sabini, Maria Gabriella; Cosentino, Sebastiano; Ippolito, Massimo. - 14366:(2024), pp. 49-58. (Intervento presentato al convegno 2nd International Workshop on Artificial Intelligence and Radiomics in Computer-Aided Diagnosis, in the International Conference on Image Analysis and Processing tenutosi a Udine; Italy) [10.1007/978-3-031-51026-7_5].
Prediction of High Pathological Grade in Prostate Cancer Patients Undergoing [18F]-PSMA PET/CT: A Preliminary Radiomics Study
Pasini, Giovanni
;
2024
Abstract
The aim of this study was to evaluate the effectiveness of [F-18]-prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) imaging in discriminating high pathological grade (Gleason score > 7), and low pathological grade (Gleason score < 7) usingmachine learning techniques. The study involved 81 patientswith diagnosed prostate cancer who underwent positive [F-18]-SPMA PET/CT scans. The PET images were used to identify the primary lesions, and then radiomics analyses were performed using an Imaging Biomarker Standardization Initiative (IBSI) compliant software, namely matRadiomics. Machine learning approaches were employed to identify relevant radiomics features for predicting high-risk malignant disease. The performance of the models was validated using 10 times repeated 5-fold cross validation scheme. The results showed a value of 0.75 for the area under the curve and an accuracy of 72% using the support vector machine (SVM). In conclusion, the study showcased the cI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
