Predicting Positive Repeat Prostate Biopsy Outcomes: Comparison of Machine Learning Approaches to Identify Key Parameters and Optimal Algorithms

ABSTRACT

Background: Innovative strategies are necessary to enhance prostate cancer diagnosis whilst reducing unnecessary and invasive repeat biopsies. This study aimed to determine the significant parameters affecting repeat prostate biopsy outcomes and develop an optimal machine learning algorithm for predicting positive repeat prostate biopsy results. Methods: We analysed data from 174 men who underwent repeated prostate biopsies between January 2008 and December 2022. Systematic multiple-core, ultrasound-targeted prostate biopsies were performed, each two samples from prostatic transitional zone and peripheral zone were obtained bilaterally. Clinical characteristics were collected, including patients’ age, initial prostate volume, prostate-specific antigen (PSA) level, free PSA (fPSA)/PSA ratio, biopsy core numbers, pathological result; The time interval between first and latest prostate biopsy; Latest PSA level, fPSA/PSA ratio, biopsy core numbers; And final pathological diagnosis. Six feature selection methods, namely, variable ranking, correlation matrix, random forest regression, recursive feature elimination, cross-validation and forward selection, were employed to identify key influencing factors for repeat biopsy outcomes. Subsequently, the performance of seven machine learning algorithms, namely, multivariable logistic regression (LR), K-nearest neighbour search (KNN), support vector classification (SVC), decision tree (DT), random forest classifier (RF), naïve Bayes classifier (NBC) and gradient booster tree (GB), was assessed based on accuracy, misclassification, recall, specificity, precision and receiver operating characteristic (ROC) area under the curve (AUC). About 70% of patients were used as the training dataset, meanwhile remaining 30% as validation dataset. Results: 52 were ultimately diagnosed with prostate cancer following the final pathological examination. The remaining 122 patients were negative (AU)