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02.12.2021 | Imaging Informatics and Artificial Intelligence

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

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

Erschienen in: European Radiology | Ausgabe 4/2022

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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.

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Titel: Automated Stanford classification of aortic dissection using a 2-step hierarchical neural network at computed tomography angiography
verfasst von: Li-Ting Huang
Yi-Shan Tsai
Cheng-Fu Liou
Tsung-Han Lee
Po-Tsun Paul Kuo
Han-Sheng Huang
Chien-Kuo Wang
Publikationsdatum: 02.12.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology / Ausgabe 4/2022
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-021-08370-2

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