Clinico-radiological characteristic-based machine learning in reducing unnecessary prostate biopsies of PI-RADS 3 lesion
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IMAGING INFORMATICS AND ARTIFICIAL INTELLIGENCE
Clinico-radiological characteristic-based machine learning in reducing unnecessary prostate biopsies of PI-RADS 3 lesions with dual validation Yansheng Kan 1 & Qing Zhang 1 & Jiange Hao 1 & Wei Wang 1 & Junlong Zhuang 1 & Jie Gao 1 & Haifeng Huang 1 & Jing Liang 1 & Giancarlo Marra 2 & Giorgio Calleris 2 & Marco Oderda 2 & Xiaozhi Zhao 1 & Paolo Gontero 2 & Hongqian Guo 1 Received: 19 December 2019 / Revised: 27 March 2020 / Accepted: 14 May 2020 # European Society of Radiology 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. Keywords Prostate cancer . Multiparametric MRI . Screening . Machine learning . Multicenter study
Yansheng Kan, Qing Zhang contributed equally to this work. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00330-020-06958-8) contains supplementary material, which is available to authorized users. * Xiaozhi Zhao [email protected]
1
Department of Urology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Institute of Urology, Nanjing University, N
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