Predicting Biochemical Recurrence After Robot-Assisted Prostatectomy with Interpretable Machine Learning Model

TL;DR

This study developed a machine learning model to predict biochemical recurrence after prostate surgery, showing strong performance and identifying key clinical factors.

Contribution

The novel contribution is the development of an interpretable LightGBM model for predicting BCR after RARP with high accuracy and clinical relevance.

Findings

The LightGBM model achieved an AUC of 0.881 in predicting biochemical recurrence.

Key predictors included pathological T stage, PSA levels, and biopsy positive rate.

SHAP values provided interpretable insights into feature contributions.

Abstract

Background: This study aimed to develop and evaluate machine learning (ML) models to predict biochemical recurrence (BCR) after robot-assisted radical prostatectomy (RARP). Methods: We retrospectively analyzed clinical data from 1125 patients who underwent RARP between July 2013 and December 2023. The dataset was divided into a training set (70%) and a testing set (30%) using a stratified sampling strategy. Five ML models were developed using the training set. Model performance was evaluated on the testing set using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and F1 scores. Additionally, model interpretability was assessed using SHapley Additive exPlanations (SHAP) values to determine the contribution of individual features. Results: Among the five ML models, the LightGBM model achieved the best prediction ability with an AUC of 0.881 (95%CI: 0.840–0.922) in the testing set. For model interpretability, SHAP values explained the contribution of individual features to the model, revealing that pathological T stage (pT), positive surgical margin (PSM), prostate-specific antigen (PSA) nadir, initial PSA, systematic prostate biopsy positive rate, seminal vesicle invasion (SVI), pathological International Society of Urological Pathology Grade Group (pGG), and perineural invasion (PI) were the key contributors to the predictive performance. Conclusions: We developed and validated ML models to predict BCR following RARP and identified that the LightGBM model with 8 variables achieved promising performance and demonstrated a high level of clinical applicability.

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