Computer-aided shape features extraction and regression models for predicting the ascending aortic aneurysm growth rate
Clinical monitoring of ascending aortic aneurysms (AAoA) suffers from low accuracy in predicting aneurysm growth rate using conventional radial measurements. Method: We propose a quantitative, 3D morphology–driven prediction framework integrating local and global geometric features. Specifically, we construct a robust shape representation by jointly encoding multi-scale surface curvature and topological invariants, and design a growth-rate–sensitive dynamic feature-weighting regression scheme. Implicit surface reconstruction and differential-geometric feature extraction are performed directly from clinical CT volumes, followed by LASSO-regularized gradient-boosted decision tree (GBDT) regression. Contribution/Results: Validated on a multicenter cohort, our method achieves a mean absolute error of 0.18 mm/yr in growth-rate prediction—37% lower than radial metrics—and an AUC of 0.89 for binary classification of fast versus slow growth. This work is the first to incorporate topological invariants into AAoA growth modeling, substantially improving the reliability of noninvasive, patient-specific risk assessment.