Automated Stanford classification of aortic dissection using a 2-step hierarchical neural network at computed tomography angiography

Li Ting Huang, Yi Shan Tsai, Cheng Fu Liou, Tsung Han Lee, Po Tsun Paul Kuo, Han Sheng Huang, Chien Kuo Wang

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Objectives: This study aimed to evaluate the feasibility of automatic Stanford classification of classic aortic dissection (AD) using a 2-step hierarchical neural network. Methods: Between 2015 and 2019, 130 arterial phase series (57 type A, 43 type B, and 30 negative cases) in aortic CTA were collected for the training and validation. A 2-step hierarchical model was built including the first step detecting AD and the second step predicting the probability (0–1) of Stanford types. The model’s performance was evaluated with an off-line prospective test in 2020. The sensitivity and specificity for Stanford type A, type B, and no AD (Sens A, B, N and Spec A, B, N, respectively) and Cohen’s kappa were reported. Results: Of 298 cases (22 with type A, 29 with type B, and 247 without AD) in the off-line prospective test, the Sens A, Sens B, and Sens N were 95.45% (95% confidence interval [CI], 77.16–99.88%), 79.31% (95% CI, 60.28–92.01%), and 93.52% (95% CI, 89.69–96.25%), respectively. The Spec A, Spec B, and Spec N were 98.55% (95% CI, 96.33–99.60%), 94.05% (95% CI, 90.52–96.56%), and 94.12% (95% CI, 83.76–98.77%), respectively. The classification rate achieved 92.28% (95% CI, 88.64–95.04%). The Cohen’s kappa was 0.766 (95% CI, 0.68–0.85; p < 0.001). Conclusions: Stanford classification of classic AD can be determined by a 2-step hierarchical neural network with high sensitivity and specificity of type A and high specificity in type B and no AD. Key Points: • The Stanford classification for aortic dissection is widely adopted and divides it into Stanford type A and type B based on the ascending thoracic aorta dissected or not. • The 2-step hierarchical neural network for Stanford classification of classic aortic dissection achieved high sensitivity (95.45%) and specificity (98.55%) of type A and high specificity in type B and no aortic dissection (94.05% and 94.12%, respectively) in 298 test cases. • The 2-step hierarchical neural network demonstrated moderate agreement (Cohen’s kappa: 0.766, p < 0.001) with cardiovascular radiologists in detection and Stanford classification of classic aortic dissection in 298 test cases.

Original languageEnglish
Pages (from-to)2277-2285
Number of pages9
JournalEuropean Radiology
Volume32
Issue number4
DOIs
Publication statusPublished - 2022 Apr

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging

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