The value of AI for assessing longitudinal brain metastases treatment response.

ABSTRACT:

Background

Effective follow-up of brain metastasis (BM) patients post-treatment is crucial for adapting therapies and detecting new lesions. Current guidelines (Response Assessment in Neuro-Oncology-BM) have limitations, such as patient-level assessments and arbitrary lesion selection, which may not reflect outcomes in high tumor burden cases. Accurate, reproducible, and automated response assessments can improve follow-up decisions, including (1) optimizing re-treatment timing to avoid treating responding lesions or delaying treatment of progressive ones, and (2) enhancing precision in evaluating responses during clinical trials.

Methods

We compared manual and automatic (deep learning-based) lesion contouring using unidimensional and volumetric criteria. Analysis focused on

SUBMITTER: Andrearczyk V 

PROVIDER: S-EPMC11786217 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature