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