Ma Zhuangxuan, Jin Liang, Zhang Lukai, Yang Yuling, Tang Yilin, Gao Pan, Sun Yingli, Li Ming
Radiology Department, Huadong Hospital, Affiliated with Fudan University, Shanghai 200040, China.
Radiology Department, Huashan Hospital, Affiliated with Fudan University, Shanghai 200040, China.
Biology (Basel). 2023 Feb 21;12(3):337. 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.
我们旨在使用基于影像组学的机器学习模型在非增强计算机断层扫描（NCCT）图像上检测急性主动脉综合征（AAS）。回顾性纳入了来自中国2个医学中心的325例行主动脉CT血管造影（CTA）的患者，以形成内部队列（230例患者，60例AAS患者）和外部测试队列（95例AAS患者）。内部队列分为训练队列（ = 135）、验证队列（ = 49）和内部测试队列（ = 46）。由放射科医生在NCCT上手动勾勒主动脉掩膜。使用最小绝对收缩和选择算子回归（LASSO）筛选出9个特征参数；支持向量机（SVM）模型表现最佳。在训练和验证队列中，SVM模型的曲线下面积（AUC）为0.993（95%CI，0.9
