Clinical application of machine learning models in patients with prostate cancer before prostatectomy.

ABSTRACT:

Background

To build machine learning predictive models for surgical risk assessment of extracapsular extension (ECE) in patients with prostate cancer (PCa) before radical prostatectomy; and to compare the use of decision curve analysis (DCA) and receiver operating characteristic (ROC) metrics for selecting input feature combinations in models.

Methods

This retrospective observational study included two independent data sets: 139 participants from a single institution (training), and 55 from 15 other institutions (external validation), both treated with Robotic Assisted Radical Prostatectomy (RARP). Five ML models, based on different combinations of clinical, semantic (interpreted by a radiologist) and radiomics features computed from T2W-MRI images, were built to predict ext

SUBMITTER: Guerra A 

PROVIDER: S-EPMC10854130 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature