Utility of Machine Learning Models to Predict Lymph Node Metastasis of Japanese Localized Prostate Cancer.

ABSTRACT:

Background/objectives

Extended pelvic lymph node dissection is a crucial surgical technique for managing intermediate to high-risk prostate cancer. Accurately predicting lymph node metastasis before surgery can minimize unnecessary lymph node dissections and their associated complications. This study assessed the efficacy of various machine learning models for predicting lymph node metastasis in a cohort of Japanese patients who underwent robot-assisted laparoscopic radical prostatectomy.

Methods

Data from 625 patients who underwent extended pelvic lymph node dissection or standard dissection with lymph node metastasis between October 2010 and February 2023 were analyzed. Four machine learning models-Random Forest, Light Gradient-Boosting Machine, Logistic Regression, and Su

SUBMITTER: Ueki H 

PROVIDER: S-EPMC11640458 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature