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

Zhuangxuan Ma; Liang Jin; Lukai Zhang; Yuling Yang; Yilin Tang; Pan Gao; Yingli Sun; Ming Li

doi:10.3390/biology12030337

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

Biology (Basel). 2023 Feb 21;12(3):337. doi: 10.3390/biology12030337.

Authors

Zhuangxuan Ma¹, Liang Jin^{1

2}, Lukai Zhang¹, Yuling Yang¹, Yilin Tang¹, Pan Gao¹, Yingli Sun¹, Ming Li^{1

3}

Affiliations

¹ Radiology Department, Huadong Hospital, Affiliated with Fudan University, Shanghai 200040, China.
² Radiology Department, Huashan Hospital, Affiliated with Fudan University, Shanghai 200040, China.
³ Institute of Functional and Molecular Medical Imaging, Shanghai 200040, China.

Abstract

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 (n = 135), validation cohort (n = 49), and internal testing cohort (n = 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.

Keywords: acute aortic syndromes; non-contrast CT; radiomics.

Grants and funding

This work was supported by Shanghai Key Lab of Forensic Medicine, Ministry of Justice, China (Academy of Forensic Science) [grant number, KF202113], the Youth Medical Talents-Medical Imaging Practitioner Program [grant number, AB83030002019004], Science and Technology Planning Project of Shanghai Science and Technology Commission [grant number, 22Y11910700], the Health Commission of Shanghai [grant number, 2018ZHYL0103], National Natural Science Foundation of China [grant number 61976238], and Shanghai “Rising Stars of Medical Talent” Youth Development Program “Outstanding Youth Medical Talents” (SHWJRS [2021]-99), Emerging Talent Program (XXRC2213) and Leading Talent Program (LJRC2202) of Huadong Hospital, and Excellent Academic Leaders of Shanghai (2022XD042).