Evaluation of MRI anatomy in machine learning predictive models to assess hydrogel spacer benefit for prostate cancer patients.

ABSTRACT:

Introduction

Hydrogel spacers (HS) are designed to minimise the radiation doses to the rectum in prostate cancer radiation therapy (RT) by creating a physical gap between the rectum and the target treatment volume inclusive of the prostate and seminal vesicles (SV). This study aims to determine the feasibility of incorporating diagnostic MRI (dMRI) information in statistical machine learning (SML) models developed with planning CT (pCT) anatomy for dose and rectal toxicity prediction. The SML models aim to support HS insertion decision-making prior to RT planning procedures.

Methods

Regions of interest (ROIs) were retrospectively contoured on the pCT and registered dMRI scans for 20 patients. ROI Dice and Hausdorff distance (HD) comparison metrics were calculated. The ROI an

SUBMITTER: Bush M 

PROVIDER: S-EPMC11986981 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature