Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Hangzhou, 310000, China.
The First Clinical Medical College of Zhejiang Chinese Medical University, 548 Binwen Road, Hangzhou, 310000, China.
Eur Radiol. 2021 Sep;31(9):7067-7076. doi: 10.1007/s00330-021-07768-2. Epub 2021 Mar 23.
To develop a non-contrast CT-based radiomic signature to effectively screen for thoracic aortic dissections (ADs).
We retrospectively enrolled 378 patients who underwent non-contrast chest CT scans along with CT angiography or MRI from 4 medical centers. The training and validation sets were from 3 centers, while the external test set was from a 4th center. Radiomic features were extracted from non-contrast CT images. The radiomic signature was created on the basis of selected features by a logistic regression algorithm. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were conducted to assess the predictive ability of radiomic signature.
The radiomic signature demonstrated AUCs of 0.91 (95% confidence interval [CI], 0.86-0.95) in the training set, 0.92 (95% CI, 0.86-0.98) in the validation set, and 0.90 (95% CI, 0.82-0.98) in the external test set. The predicted diagnosis was in good agreement with the probability of thoracic AD. In the external test group, the diagnostic accuracy, sensitivity, specificity, PPV, and NPV were 90.5%, 85.7%, 91.7%, 70.6%, and 96.5%, respectively.
A radiomic signature based on non-contrast CT images can effectively predict thoracic ADs. This method may serve as a potential screening tool for thoracic ADs.
• The non-contrast CT-based radiomic signature can effectively predict the thoracic aortic dissections. • This radiomic signature shows better predictive performance compared to the current clinical model. • This prediction method may be a potential tool for screening thoracic aortic dissections.
开发一种基于非对比 CT 的放射组学特征，以有效筛查胸主动脉夹层（AD）。
我们回顾性纳入了 378 名在 4 家医疗机构接受非对比胸部 CT 扫描并进行 CT 血管造影或 MRI 的患者。训练集和验证集来自 3 家中心，外部测试集来自第 4 家中心。从非对比 CT 图像中提取放射组学特征。基于选定的特征，通过逻辑回归算法创建放射组学特征。采用受试者工作特征（ROC）曲线下面积（AUC）、准确率、敏感度、特异度、阳性预测值（PPV）和阴性预测值（NPV）评估放射组学特征的预测能力。
在训练集、验证集和外部测试集中，放射组学特征的 AUC 分别为 0.91（95%置信区间[CI]，0.86-0.95）、0.92（95% CI，0.86-0.98）和 0.90（95% CI，0.82-0.98）。预测诊断与胸 AD 的概率吻合良好。在外部测试组中，诊断准确率、敏感度、特异度、PPV 和 NPV 分别为 90.5%、85.7%、91.7%、70.6%和 96.5%。
基于非对比 CT 图像的放射组学特征可有效预测胸 AD。这种方法可能成为胸 AD 的一种潜在筛查工具。
基于非对比 CT 的放射组学特征可有效预测胸主动脉夹层。
与现有临床模型相比，该放射组学特征具有更好的预测性能。
这种预测方法可能是筛查胸主动脉夹层的潜在工具。
