Prediction of prostate tumour hypoxia using pre-treatment MRI-derived radiomics: preliminary findings.

ABSTRACT:

Purpose

To develop a machine learning (ML) model based on radiomic features (RF) extracted from whole prostate gland magnetic resonance imaging (MRI) for prediction of tumour hypoxia pre-radiotherapy.

Material and methods

Consecutive patients with high-grade prostate cancer and pre-treatment MRI treated with radiotherapy between 01/12/2007 and 1/08/2013 at two cancer centres were included. Cancers were dichotomised as normoxic or hypoxic using a biopsy-based 32-gene hypoxia signature (Ragnum signature). Prostate segmentation was performed on axial T2-weighted (T2w) sequences using RayStation (v9.1). Histogram standardisation was applied prior to RF extraction. PyRadiomics (v3.0.1) was used to extract RFs for analysis. The cohort was split 80:20 into training and test sets. S

SUBMITTER: Zhong J 

PROVIDER: S-EPMC10264289 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature