Artificial intelligence in prostate cancer: The potential of machine learning models and neural networks to predict biochemical recurrence after robot-assisted radical prostatectomy

• Published in: Indian journal of urology Vol. 40; no. 4; pp. 260 - 265
• Main Authors: Singh, Gurpremjit, Agrawal, Mayank, Talwar, Gagandeep, Kankaria, Sanket, Sharma, Gopal, Ahluwalia, Puneet, Gautam, Gagan
• Format: Journal Article
• Language: English
• Published: Vellore Medknow Publications and Media Pvt. Ltd 01.10.2024; Medknow Publications & Media Pvt. Ltd; Wolters Kluwer Medknow Publications
• Subjects: Artificial intelligence; Machine learning; Medical research; Medicine, Experimental; Neural networks; Prognosis; Prostate cancer; Robotic surgery; Robots
• ISSN: 0970-1591; 1998-3824
• DOI: 10.4103/iju.iju_75_24

ABSTRACT Introduction: This study aimed to evaluate the usefulness of machine learning (ML) and neural network (NN) models versus traditional statistical methods for estimating biochemical recurrence (BCR) in men following robot-assisted radical prostatectomy (RARP). Methods: Patients who underwent RARP from November 2011 to July 2022 were taken in the study. Patients with BCR were assigned to Group 2, whereas those without BCR were placed in Group 1. Preoperative and postoperative parameters, together with demographic data, were recorded in the database. This study used one NN, the radial basis function NN (RBFNN), and two ML approaches, the K-nearest neighbor and XGboost ML models, to predict BCR. Results: Following the application of exclusion criteria, 516 patients were deemed eligible for the study. Of those, 234 (45.3%) developed BCR, and 282 (54.7%) did not. The results showed that the median follow-up period was 24 (15–42) months, and the median BCR diagnosis was 12.23 ± 15.58 months. The area under the curve (AUC) for the Cox proportional hazard analysis was 0.77. The receiver-operating characteristic curves (AUCs) for the XGBoost and K closest neighbor models were 0.82 and 0.69, respectively. The RBFNN’s AUC was 0.82. Conclusions: The classical statistical model was outperformed by XGBoost and RBFNN models in predicting BCR.