OS02.6.A MICCAI BRAIN TUMOR SEGMENTATION - METASTASES (BRATS-METS) 2025 LIGHTHOUSE CHALLENGE: COMPETITIVE ENVIRONMENT FOR THE DEVELOPMENT OF ROBUST AUTOMATIC SEGMENTATION ALGORITHMS FOR PRE- AND POST-TREATMENT BRAIN METASTASES

Abstract BACKGROUND Volumetric analysis of brain metastatic lesions is of great relevance in neuro-oncology. Since manual annotation of a lesion is a laborious and time-consuming task, utilizing artificial intelligence (AI) tools for automated segmentation of brain metastases has shown promising potential, but published algorithms are not incorporated into clinical practice because of poor generalizability across institutions. Large publicly available and well-annotated datasets from across the world are critical for further clinical translation. MATERIAL AND METHODS The Brain Tumor Segmentation - Metastases (BraTS-METS) 2025 Lighthouse Challenge provides a competitive environment where scientists from various disciplines can benchmark their AI-based algorithms for automatic segmentation of pre- and post-treatment brain metastases on a well-curated publicly available brain metastasis dataset comprising 1,778 multiparametric brain MRI cases. Each case consists of T1-weighed, T1 post-contrast and FLAIR sequences. The cases were retrospectively obtained from 8 institutions in the USA and overseas, and underwent 3D nnU-Net pre-segmentation, followed by manual refinements performed by medical students and neuroradiologists, and a final approval by two independent board-certified neuroradiologists. A testing dataset was prepared through four rounds of annotation: two completed ‘from scratch’ and two following 3D nnU-Net pre-segmentation, all conducted by two independent neuroradiologists, with the segmentation process screen recorded for future analysis. This allowed us to establish inter- and intra-rater variability in brain metastases segmentation and create a reference standard dataset that will be publicly available after the conclusion of the challenge. The model performance of the participating teams in the challenge will be assessed via the following metrics: Dice Similarity Coefficient (DSC), Normalized Surface Distance (NSD), sensitivity, and precision of the model. RESULTS A multi-annotator testing dataset of 75 MRI cases of brain metastases and 300 segmentations (150 created ‘from scratch’ and 150 from 3D nnU-Net pre-segmentations of the cases) was developed as a new reference standard. The mean intra- and inter-rater DSC for the segmentations ‘from scratch’ were 0.68 and 0.59, respectively. The mean intra- and inter-rater DSC for the segmentations built upon 3D nnU-Net pre-segmentations were 0.88 and 0.77, respectively. CONCLUSION The BraTS-METS 2025 Lighthouse Challenge is a key initiative designed to advance the use of AI to improve characterization of brain metastases by providing algorithms for automatic segmentation of all lesions and peritumoral edema that are measured against a novel reference standard based on rigorous multi-annotator brain MRI segmentations.