CoRe-BT: A Multimodal Radiology-Pathology-Text Benchmark for Robust Brain Tumor Typing

• URL: http://arxiv.org/abs/2603.03618v1
• Date: Wed, 04 Mar 2026 01:06:50 GMT
• Title: CoRe-BT: A Multimodal Radiology-Pathology-Text Benchmark for Robust Brain Tumor Typing
• Authors: Juampablo E. Heras Rivera, Daniel K. Low, Xavier Xiong, Jacob J. Ruzevick, Daniel D. Child, Wen-wai Yim, Mehmet Kurt, Asma Ben Abacha,
• Abstract summary: We introduce CoRe-BT, a cross-modal radiology-pathology-text benchmark for brain tumor typing.<n>The dataset comprises 310 patients with multi-sequence brain MRI (T1, T1c, T2, FLAIR)<n>All cases are annotated with tumor type and grade, and MRI volumes include expert-annotated tumor masks.
• Score: 3.6380774806458214
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate brain tumor typing requires integrating heterogeneous clinical evidence, including magnetic resonance imaging (MRI), histopathology, and pathology reports, which are often incomplete at the time of diagnosis. We introduce CoRe-BT, a cross-modal radiology-pathology-text benchmark for brain tumor typing, designed to study robust multimodal learning under missing modality conditions. The dataset comprises 310 patients with multi-sequence brain MRI (T1, T1c, T2, FLAIR), including 95 cases with paired H&E-stained whole-slide pathology images and pathology reports. All cases are annotated with tumor type and grade, and MRI volumes include expert-annotated tumor masks, enabling both region-aware modeling and auxiliary learning tasks. Tumors are categorized into six clinically relevant classes capturing the heterogeneity of common and rare glioma subtypes. We evaluate tumor typing under variable modality availability by comparing MRI-only models with multimodal approaches that incorporate pathology information when present. Baseline experiments demonstrate the feasibility of multimodal fusion and highlight complementary modality contributions across clinically relevant typing tasks. CoRe-BT provides a grounded testbed for advancing multimodal glioma typing and representation learning in realistic scenarios with incomplete clinical data.

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