The role of anatomic shape features in the prognosis of uncomplicated type B aortic dissection initially treated with optimal medical therapy

• Published in: Computers in biology and medicine 2024-03, Vol.170, p.108041-108041, Article 108041
• Main Authors: Liu, Minliang, Dong, Hai, Mazlout, Adam, Wu, Yuxuan, Kalyanasundaram, Asanish, Oshinski, John N, Sun, Wei, Elefteriades, John A, Leshnower, Bradley G, Gleason, Rudolph L
• Format: Article
• Language: English
• Subjects: Aorta; Aortic Aneurysm, Thoracic - surgery; Aortic aneurysms; Aortic dissection; Aortic Dissection - diagnostic imaging; Aortic Dissection - drug therapy; Aortic growth; Blood Vessel Prosthesis Implantation; Computed tomography; Coronary vessels; Correspondence; Dissection; Dissection flap; Endovascular Procedures - adverse effects; Errors; Failure; Growth rate; Humans; Internal Medicine; Mathematical models; Medical imaging; Medical prognosis; Optimal medical therapy; Other; Patients; Principal components analysis; Regression; Retrospective Studies; Risk Factors; Statistical analysis; Statistical shape model; Support vector machines; Survival; Treatment Outcome; Type B aortic dissection; Veins & arteries
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2024.108041

AbstractObjectiveCurrently, the long-term outcomes of uncomplicated type B aortic dissection (TBAD) patients managed with optimal medical therapy (OMT) remain poor. Aortic expansion is a major factor that determines patient long-term survival. The objective of this study was to investigate the association between anatomic shape features and (i) OMT outcome; (ii) aortic growth rate for TBAD patients initially treated with OMT. Methods108 CT images of TBAD in the acute and chronic phases were collected from 46 patients who were initially treated with OMT. Statistical shape models (SSM) of TBAD were constructed to extract shape features from the earliest initial CT scans of each patient by using principal component analysis (PCA) and partial least square (PLS) regression. Additionally, conventional shape features (e.g., aortic diameter) were quantified from the earliest CT scans as a baseline for comparison. We identified conventional and SSM features that were significant in separating OMT “success” and failure patients. Moreover, the aortic growth rate was predicted by SSM and conventional features using linear and nonlinear regression with cross-validations. ResultsSize-related SSM and conventional features (mean aortic diameter: p=0.0484, centerline length: p=0.0112, PCA score c1: p=0.0192, and PLS scores t1: p=0.0004, t2: p=0.0274) were significantly different between OMT success and failure groups, but these features were incapable of predicting the aortic growth rate. SSM shape features showed superior results in growth rate prediction compared to conventional features. Using multiple linear regression, the conventional, PCA, and PLS shape features resulted in root mean square errors (RMSE) of 1.23, 0.85, and 0.84 mm/year, respectively, in leave-one-out cross-validations. Nonlinear support vector regression (SVR) led to improved RMSE of 0.99, 0.54, and 0.43 mm/year, for the conventional, PCA, and PLS features, respectively. ConclusionSize-related shape features of the earliest scan were correlated with OMT failure but led to large errors in the prediction of the aortic growth rate. SSM features in combination with nonlinear regression could be a promising avenue to predict the aortic growth rate.