Jungyo Suh1,2, Sangjun Yoo3, Juhyun Park4, Sung Yong Cho5, Min Chul Cho3, Hwancheol Son3, Hyeon Jeong2,3. 1. Department of Urology, Hospital Medicine Center, Seoul National University Hospital, Seoul, South Korea. 2. Department of Urology, Seoul National University College of Medicine, Seoul, South Korea. 3. Department of Urology, Seoul Metropolitan Government - Seoul National University Boramae Medical Center, Seoul, South Korea. 4. Department of Urology, Asan Medical Center, Seoul, South Korea. 5. Department of Urology, Seoul National University Hospital, Seoul, South Korea.
Abstract
OBJECTIVES: To develop and validate a risk calculator for prostate cancer (PCa) and clinically significant PCa (csPCa) using explainable artificial intelligence (XAI). PATIENTS AND METHODS: We used data of 3791 patients to develop and validate the risk calculator. We initially divided the data into development and validation sets. An extreme gradient-boosting algorithm was applied to the development calculator using five-fold cross-validation with hyperparameter tuning following feature selection in the development set. The model feature importance was determined based on the Shapley value. The area under the curve (AUC) of the receiver operating characteristic curve was analysed for each validation set of the calculator. RESULTS: Approximately 1216 (32.7%) and 562 (14.8%) patients were diagnosed with PCa and csPCa. The data of 2843 patients were used for development, whereas the data of 948 patients were used as a test set. We selected the variables for each PCa and csPCa risk calculation according to the least absolute shrinkage and selection operator regression. The AUC of the final PCa model was 0.869 (95% confidence interval [CI] 0.844-0.893), whereas that of the csPCa model was 0.945 (95% CI 0.927-0.963). The prostate-specific antigen (PSA) level, free PSA level, age, prostate volume (both the transitional zone and total), hypoechoic lesions on ultrasonography, and testosterone level were found to be important parameters in the PCa model. The number of previous biopsies was not associated with the risk of csPCa, but was negatively associated with the risk of PCa. CONCLUSION: We successfully developed and validated a decision-supporting tool using XAI for calculating the probability of PCa and csPCa prior to prostate biopsy.
OBJECTIVES: To develop and validate a risk calculator for prostate cancer (PCa) and clinically significant PCa (csPCa) using explainable artificial intelligence (XAI). PATIENTS AND METHODS: We used data of 3791 patients to develop and validate the risk calculator. We initially divided the data into development and validation sets. An extreme gradient-boosting algorithm was applied to the development calculator using five-fold cross-validation with hyperparameter tuning following feature selection in the development set. The model feature importance was determined based on the Shapley value. The area under the curve (AUC) of the receiver operating characteristic curve was analysed for each validation set of the calculator. RESULTS: Approximately 1216 (32.7%) and 562 (14.8%) patients were diagnosed with PCa and csPCa. The data of 2843 patients were used for development, whereas the data of 948 patients were used as a test set. We selected the variables for each PCa and csPCa risk calculation according to the least absolute shrinkage and selection operator regression. The AUC of the final PCa model was 0.869 (95% confidence interval [CI] 0.844-0.893), whereas that of the csPCa model was 0.945 (95% CI 0.927-0.963). The prostate-specific antigen (PSA) level, free PSA level, age, prostate volume (both the transitional zone and total), hypoechoic lesions on ultrasonography, and testosterone level were found to be important parameters in the PCa model. The number of previous biopsies was not associated with the risk of csPCa, but was negatively associated with the risk of PCa. CONCLUSION: We successfully developed and validated a decision-supporting tool using XAI for calculating the probability of PCa and csPCa prior to prostate biopsy.