MRICombo: a deep-learning-based framework for universal volumetric segmentation grading-staging and malignancy detection across heterogeneous MRI.
Authors
Affiliations (9)
Affiliations (9)
- Faculty of Applied Sciences, Macao Polytechnic University, Rua de Luis Gonzaga Gomes, Macao, China.
- Netherlands Cancer Institute, Department of Radiology, Amsterdam, The Netherlands.
- Radboud University Medical Centre, Department of Radiology and Nuclear Medicine, Nijmegen, The Netherlands.
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China.
- Departments of Radiology, Biomedical Informatics, Bioengineering, and Computer Science, University of Pittsburgh, Pittsburgh, PA, USA.
- Artificial Intelligence Group, Wageningen University & Research, Wageningen, The Netherlands.
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China. [email protected].
- Faculty of Applied Sciences, Macao Polytechnic University, Rua de Luis Gonzaga Gomes, Macao, China. [email protected].
- Faculty of Applied Sciences, Macao Polytechnic University, Rua de Luis Gonzaga Gomes, Macao, China. [email protected].
Abstract
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.