A magnetic resonance imaging radiomics approach predicts hepatocellular carcinoma response to stereotactic body radiation therapy.

ABSTRACT:

Background and purpose

Predicting hepatocellular carcinoma (HCC) response to Stereotactic Body Radiation Therapy (SBRT) can be challenging. Here, we assessed the value of a radiomics-based machine learning (ML) approach for predicting HCC response to SBRT, using pre-treatment and early post-treatment magnetic resonance imaging (MRI).

Materials and methods

This retrospective single-center study included 87 patients (M 67, mean age 65.3 ± 9.1y) with HCC treated with SBRT who underwent gadoxetate MRI both pre- and early post-treatment (around 9.5 weeks). Tumor radiomics features were extracted on pre- and post-SBRT MRIs on pre- and post-contrast T1-weighted imaging (T1WI) [pre-contrast, arterial phase (AP), portal venous phase (PVP), transitional phase and hepatobiliary phase].

SUBMITTER: Geahchan A 

PROVIDER: S-EPMC12410563 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature