AI-based framework to fuse pre-RT brain metastases contours with follow-up MRI to improve post-RT assessment.

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Abstract

Brain metastases (BMs) patients often receive multiple courses of radiotherapy (RT). Comparing follow-up imaging with RT plans is time-intensive and currently not possible in picture archiving and communication system (PACS). In this study, we aim to develop an automated tool to overlay pre-RT tumor contours onto follow-up MRI in the PACS to support response assessment and identification of untreated metastases, reducing review complexity and time. We built an AI-driven workflow that registers planning CT to follow-up MRI and propagates treated-lesion contours; outputs are exported as PACS-compliant DICOM. Performance was evaluated in 40 patients: 20 underwent quantitative comparison between manual and automated registrations using Dice similarity coefficient (DSC) and mean surface distance (MSD), following AAPM TG-132 guidelines; 20 additional patients (5-35 lesions per patient) underwent clinical evaluation of follow-up and pre-RT lesions contours visualization in PACS. Three physicians assessed these cases to measure review time and inter-observer agreement in treatment response classification (improved/stable vs indeterminate). The workflow successfully registered all CT-MRI pairs. Mean DSC/MSD was: brain 0.97 ± 0.01/0.0 ± 0.0 mm, brainstem 0.89 ± 0.03/0.1 ± 0.1 mm, and gross tumor volumes 0.65 ± 0.18/0.6 ± 0.4 mm. Average physician review time per case decreased from 7.97 to 3.95 min with the automated workflow, and full inter-physician agreement increased from 72.4% to 93.5%. We developed and validated an AI-based tool that accurately fuses pre-RT contours with follow-up MRI for BMs, addressing a key gap in current PACS systems. The workflow significantly reduced review time per lesion and enhanced inter-physician agreement, and has the potential to enable faster, more consistent multidisciplinary follow-up assessment.

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