Diagnosis of Acute Aortic Syndromes on Non-Contrast CT Images with Radiomics-Based Machine Learning

• Published in: Biology (Basel, Switzerland) Switzerland), 2023-02, Vol.12 (3), p.337
• Main Authors: Ma, Zhuangxuan, Jin, Liang, Zhang, Lukai, Yang, Yuling, Tang, Yilin, Gao, Pan, Sun, Yingli, Li, Ming
• Format: Article
• Language: English
• Subjects: acute aortic syndromes; Algorithms; Anabolic steroids; Angiography; Aorta; Collaboration; Comparative analysis; Computed tomography; Coronary vessels; CT imaging; Diagnosis; Discriminant analysis; Emergency medical care; Hematoma; Hospitals; Learning algorithms; Machine learning; Medical centers; Medical imaging; Medical imaging equipment; non-contrast CT; Patients; Radiomics; Tomography
• ISSN: 2079-7737; 2079-7737
• DOI: 10.3390/biology12030337

We aimed to detect acute aortic syndromes (AAS) on non-contrast computed tomography (NCCT) images using a radiomics-based machine learning model. A total of 325 patients who underwent aortic CT angiography (CTA) were enrolled retrospectively from 2 medical centers in China to form the internal cohort (230 patients, 60 patients with AAS) and the external testing cohort (95 patients with AAS). The internal cohort was divided into the training cohort ( = 135), validation cohort ( = 49), and internal testing cohort ( = 46). The aortic mask was manually delineated on NCCT by a radiologist. Least Absolute Shrinkage and Selection Operator regression (LASSO) was used to filter out nine feature parameters; the Support Vector Machine (SVM) model showed the best performance. In the training and validation cohorts, the SVM model had an area under the curve (AUC) of 0.993 (95% CI, 0.965-1); accuracy (ACC), 0.946 (95% CI, 0.877-1); sensitivity, 0.9 (95% CI, 0.696-1); and specificity, 0.964 (95% CI, 0.903-1). In the internal testing cohort, the SVM model had an AUC of 0.997 (95% CI, 0.992-1); ACC, 0.957 (95% CI, 0.945-0.988); sensitivity, 0.889 (95% CI, 0.888-0.889); and specificity, 0.973 (95% CI, 0.959-1). In the external testing cohort, the ACC was 0.991 (95% CI, 0.937-1). This model can detect AAS on NCCT, reducing misdiagnosis and improving examinations and prognosis.