Radiomics-based machine learning models for predicting genomic alterations in metastatic prostate cancer using PSMA PET imaging: a pilot study.

ABSTRACT:

Background and objective

Genomic characterization of metastatic prostate cancer (mPCa) plays a pivotal role in guiding precision oncology. This study aimed to evaluate the feasibility of combining radiomics and clinical data within a machine learning (ML) framework to non-invasively predict key genomic mutations in patients with mPCa undergoing PSMA PET imaging.

Methods

A retrospective cohort of 14 mPCa patients who underwent [¹⁸ F]PSMA-1007 PET/CT was analysed. Prostate and metastatic lesions were segmented, and radiomics features were extracted. Somatic genomic alterations were obtained from formalin-fixed paraffin-embedded tissue samples using FoundationOne CDx testing. Six ML algorithms - Discriminant Analysis, Support Vector Machines, K-Nearest Neighbours, Ne

SUBMITTER: Scavuzzo A 

PROVIDER: S-EPMC12753601 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature