一项基于非增强计算机断层扫描的临床-放射组学模型，通过机器学习预测中风后出血性转化：一项多中心研究。

A clinical-radiomics model based on noncontrast computed tomography to predict hemorrhagic transformation after stroke by machine learning: a multicenter study.

作者信息

Ren Huanhuan, Song Haojie, Wang Jingjie, Xiong Hua, Long Bangyuan, Gong Meilin, Liu Jiayang, He Zhanping, Liu Li, Jiang Xili, Li Lifeng, Li Hanjian, Cui Shaoguo, Li Yongmei

机构信息

Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China.

Department of Radiology, Chongqing General Hospital, Chongqing, China.

出版信息

Insights Imaging. 2023 Mar 29;14(1):52. doi: 10.1186/s13244-023-01399-5.

OBJECTIVE

To build a clinical-radiomics model based on noncontrast computed tomography images to identify the risk of hemorrhagic transformation (HT) in patients with acute ischemic stroke (AIS) following intravenous thrombolysis (IVT).

MATERIALS AND METHODS

A total of 517 consecutive patients with AIS were screened for inclusion. Datasets from six hospitals were randomly divided into a training cohort and an internal cohort with an 8:2 ratio. The dataset of the seventh hospital was used for an independent external verification. The best dimensionality reduction method to choose features and the best machine learning (ML) algorithm to develop a model were selected. Then, the clinical, radiomics and clinical-radiomics models were developed. Finally, the performance of the models was measured using the area under the receiver operating characteristic curve (AUC).

RESULTS

Of 517 from seven hospitals, 249 (48%) had HT. The best method for choosing features was recursive feature elimination, and the best ML algorithm to build models was extreme gradient boosting. In distinguishing patients with HT, the AUC of the clinical model was 0.898 (95% CI 0.873-0.921) in the internal validation cohort, and 0.911 (95% CI 0.891-0.928) in the external validation cohort; the AUC of radiomics model was 0.922 (95% CI 0.896-0.941) and 0.883 (95% CI 0.851-0.902), while the AUC of clinical-radiomics model was 0.950 (95% CI 0.925-0.967) and 0.942 (95% CI 0.927-0.958) respectively.

CONCLUSION

The proposed clinical-radiomics model is a dependable approach that could provide risk assessment of HT for patients who receive IVT after stroke.

目的

基于非增强计算机断层扫描图像构建临床-影像组学模型，以识别急性缺血性卒中（AIS）患者静脉溶栓（IVT）后出血转化（HT）的风险。

材料与方法

共筛选出517例连续的AIS患者纳入研究。来自六家医院的数据集以8:2的比例随机分为训练队列和内部队列。第七家医院的数据集用于独立的外部验证。选择最佳的降维方法来选择特征，并选择最佳的机器学习（ML）算法来开发模型。然后，开发临床、影像组学和临床-影像组学模型。最后，使用受试者操作特征曲线（AUC）下的面积来衡量模型的性能。

结果

在来自七家医院的517例患者中，249例（48%）发生了HT。选择特征的最佳方法是递归特征消除，构建模型的最佳ML算法是极端梯度提升。在区分HT患者时，内部验证队列中临床模型的AUC为0.898（95%CI 0.873-0.921），外部验证队列中为0.911（95%CI 0.891-0.928）；影像组学模型的AUC分别为0.922（95%CI 0.896-0.941）和0.883（95%CI 0.851-0.902），而临床-影像组学模型AUC分别为0.950（95%CI 0.925-0.967）和0.942（95%CI 0.927-0.958）。