MRICombo: a deep-learning-based framework for universal volumetric segmentation grading-staging and malignancy...

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 7,380 MRI sequences from 2,354 individuals, MRICombo achieves state-of-the-art performance with mean Dice similarity coefficients of 0.836 for segmenting 14 critical anatomical structures and 0.625 for labeling 11 major tumor types. It also attains a mean area under the receiver operating characteristic curve (AUROC) of 0.920 for glioma grading, bladder and nasopharyngeal cancer staging, and breast and liver tumor malignancy detection. External validation on four independent datasets (1082 sequences from 734 individuals) and transfer learning evaluation (512 individuals) confirm robust cross-protocol generalizability. Furthermore, MRICombo supports flexible inference with missing sequences and offers decision interpretability through sequence clustering and expert contribution analysis. As a unified clinical solution, MRICombo significantly reduces deployment costs and has the potential to streamline diagnostic workflows, supporting more personalized oncology care.