Radiomics artificial intelligence modelling for prediction of local control for colorectal liver metastases treated with radiotherapy.

ABSTRACT:

Background and purpose

Prognostic assessment of local therapies for colorectal liver metastases (CLM) is essential for guiding management in radiation oncology. Computed tomography (CT) contains liver texture information which may be predictive of metastatic environments. To investigate the feasibility of analyzing CT texture, we sought to build an automated model to predict progression-free survival using CT radiomics and artificial intelligence (AI).

Materials and methods

Liver CT scans and outcomes for N = 97 CLM patients treated with radiotherapy were retrospectively obtained. A survival model was built by extracting 108 radiomic features from liver and tumor CT volumes for a random survival forest (RSF) to predict local progression. Accuracies were measured by concordan

SUBMITTER: Hu R 

PROVIDER: S-EPMC9485899 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature