CarotidMamba: Foundation Model-Enabled CTA Phenotyping of Symptomatic Carotid Plaques in a Multi-Center Retrospective Study

Background: Treatment decisions for carotid atherosclerotic disease rely primarily on luminal stenosis, although plaque vulnerability and symptomatic status better reflect short-term cerebrovascular risk. A scalable CTA tool for automated phenotyping of symptomatic carotid disease is lacking. Materials & Methods: In this multi-institutional retrospective study, 689 patients (mean age, 67.9 {+/-} 7.7 years; 366 men) from four hospitals were analyzed after screening 705 CTA examinations. 423 patients from one center were used for five-fold development and internal validation, and 266 patients from three centers for independent external validation. CarotidMamba, a deep learning framework combining dual foundation-model encoders with Mamba-based sequence modeling, was developed and benchmarked against clinical, radiomics, clinic-radiomics, CNN, and transformer comparators. Results: In the development cohort, CarotidMamba achieved an AUC of 0.839 (95% CI, 0.799-0.879) and accuracy of 0.825 (95% CI, 0.793-0.857), outperforming the strongest comparator by 0.066 and 0.050, respectively. External validation yielded AUCs of 0.897 (95% CI, 0.835-0.959) in YCH, 0.809 (95% CI, 0.720-0.898) in DCH, and 0.762 (95% CI, 0.649-0.875) in GH-NTC. CarotidMamba showed the lowest Brier score and expected calibration error across cohorts, with calibration slopes near 1.0. Conclusion: CarotidMamba provides an interpretable, clinically oriented, and externally validated CTA framework for phenotyping symptomatic carotid plaques, supporting vulnerability-aware imaging assessment beyond stenosis alone.