Objective: To evaluate machine learning–based classifiers in detecting clinically significant prostate cancer (PCa) with Prostate Imaging Reporting and Data System (PI-RADS) score 3 lesions. Methods: We retrospectively enrolled 346 patients with PI-RADS 3 lesions at two institutions. All patients underwent prostate multiparameter MRI (mpMRI) and transperineal MRI-ultrasonography (MRI-US)-targeted biopsy. We collected data on age, pre-biopsy serum prostate-specific antigen (PSA) level, prostate volume (PV), PSA density (PSAD), the location of suspicious PI-RADS 3 lesions, and histopathology results. Four machine learning–based classifiers—logistic regression, support vector machine, eXtreme Gradient Boosting (XGBoost), and random forest—were trained using datasets from Nanjing Drum Tower Hospital. External validation was carried out using datasets from Molinette Hospital. Results: Among 287 PI-RADS 3 patients, prostate cancer was proven pathologically in 59 (20.6%), and 228 (79.4%) had benign lesions. For 380 PI-RADS 3 lesions, 81 (21.3%) were proven to be PCa and 299 (78.7%) benign. Among four classifiers, the random forest classifier had the best performance in both patient-based and lesion-based datasets, with overall accuracy of 0.713 and 0.860, sensitivity of 0.857 and 0.613, and area under curve (AUC) of 0.771 and 0.832, respectively. In external validation, our best classifiers had an AUC of 0.688 with the best sensitivity (0.870) and specificity (0.500) in the 59 PI-RADS 3 patients in Molinette Hospital dataset. Conclusions: The machine learning–based random forest classifier provided a reliable probability if a PI-RADS 3 patient was benign. Key Points: • Machine learning–based classifiers could combine the clinical characteristics with accessible information on image report of PI-RADS 3 patient to generate a probability of malignancy. • This probability could assist surgeons to make diagnostic decisions with more confidence and higher efficiency.

Clinico-radiological characteristic-based machine learning in reducing unnecessary prostate biopsies of PI-RADS 3 lesions with dual validation

Marra G.;Calleris G.;Oderda M.;Gontero P.;
2020

Abstract

Objective: To evaluate machine learning–based classifiers in detecting clinically significant prostate cancer (PCa) with Prostate Imaging Reporting and Data System (PI-RADS) score 3 lesions. Methods: We retrospectively enrolled 346 patients with PI-RADS 3 lesions at two institutions. All patients underwent prostate multiparameter MRI (mpMRI) and transperineal MRI-ultrasonography (MRI-US)-targeted biopsy. We collected data on age, pre-biopsy serum prostate-specific antigen (PSA) level, prostate volume (PV), PSA density (PSAD), the location of suspicious PI-RADS 3 lesions, and histopathology results. Four machine learning–based classifiers—logistic regression, support vector machine, eXtreme Gradient Boosting (XGBoost), and random forest—were trained using datasets from Nanjing Drum Tower Hospital. External validation was carried out using datasets from Molinette Hospital. Results: Among 287 PI-RADS 3 patients, prostate cancer was proven pathologically in 59 (20.6%), and 228 (79.4%) had benign lesions. For 380 PI-RADS 3 lesions, 81 (21.3%) were proven to be PCa and 299 (78.7%) benign. Among four classifiers, the random forest classifier had the best performance in both patient-based and lesion-based datasets, with overall accuracy of 0.713 and 0.860, sensitivity of 0.857 and 0.613, and area under curve (AUC) of 0.771 and 0.832, respectively. In external validation, our best classifiers had an AUC of 0.688 with the best sensitivity (0.870) and specificity (0.500) in the 59 PI-RADS 3 patients in Molinette Hospital dataset. Conclusions: The machine learning–based random forest classifier provided a reliable probability if a PI-RADS 3 patient was benign. Key Points: • Machine learning–based classifiers could combine the clinical characteristics with accessible information on image report of PI-RADS 3 patient to generate a probability of malignancy. • This probability could assist surgeons to make diagnostic decisions with more confidence and higher efficiency.
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Machine learning; Multicenter study; Multiparametric MRI; Prostate cancer; Screening
Kan Y.; Zhang Q.; Hao J.; Wang W.; Zhuang J.; Gao J.; Huang H.; Liang J.; Marra G.; Calleris G.; Oderda M.; Zhao X.; Gontero P.; Guo H.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2318/1781854
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