MRICombo: a deep-learning-based framework for universal volume segmentation, grading-staging, and malignancy detection across heterogeneous MRI

Comprehensive magnetic resonance imaging (MRI) analysis in oncology involves multiple interrelated tasks including volumetric segmentation, grading, staging, and malignancy detection. However, most existing deep learning models are task-specific or sequence-specific, lacking the generalizability required for heterogeneous sequences. Here we present MRICombo, a unified multi-expert deep learning framework for universal anatomical delineation and tumor characterization across 9 heterogeneous imaging sequences. Developed using 7,376 MRI sequences from 2,282 individuals, MRICombo achieves state-of-the-art performance with mean Dice similarity coefficients of 0.839 for segmenting 14 critical anatomical structures and 0.633 for labeling 11 major tumor types. It also attains a mean area under the receiver operating characteristic curve (AUROC) of 0.913 for glioma grading, bladder and nasopharyngeal cancer staging, and breast and liver tumor malignancy detection. External validation on four independent datasets (998 sequences from 734 individuals) and transfer learning evaluation (260 individuals) confirm robust cross-protocols generalizability. Furthermore, MRICombo supports flexible inference with missing sequences and offers decision interpretability through sequence clustering and expert contribution analysis. As an all-in-one clinical solution, MRICombo significantly reduces deployment costs and streamlines diagnostic workflows, supporting more personalized oncology care.