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 (1) agreement in size and RANO-BM categories, (2) stability of measurements under scanner rotations and over time, and (3) predictability of 1-year outcomes. The study included 49 BM patients, with 184 MRI studies and 448 lesions, retrospectively assessed by radiologists.

Results: Automatic contouring and volumetric criteria demonstrated superior stability (P < .001 for rotation; P < .05 over time) and better outcome predictability compared to manual methods. These approaches reduced observer variability, offering reliable and efficient response assessments. The best outcome predictability, defined as 1-year response, was achieved using automatic contours and volumetric measurements. These findings highlight the potential of automated tools to streamline clinical workflows and provide consistency across evaluators, regardless of expertise.

Conclusion: Automatic BM contouring and volumetric measurements provide promising tools to improve follow-up and treatment decisions in BM management. By enhancing precision and reproducibility, these methods can streamline clinical workflows and improve the evaluation of response in trials and practice.