用于早期预测急性胰腺炎严重程度的对比增强磁共振成像的影像组学模型。
Radiomics model of contrast-enhanced MRI for early prediction of acute pancreatitis severity.
作者信息
Lin Qiao, Ji Yi-Fan, Chen Yong, Sun Huan, Yang Dan-Dan, Chen Ai-Li, Chen Tian-Wu, Zhang Xiao Ming
机构信息
Sichuan Key Laboratory of Medical Imaging and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China.
Medical Imaging and Department of Radiology, Gaoping District People's Hospital of Nanchong, Nanchong, Sichuan, China.
出版信息
J Magn Reson Imaging. 2020 Feb;51(2):397-406. doi: 10.1002/jmri.26798. Epub 2019 May 27.
BACKGROUND
Computed tomography (CT) or MR images may cause the severity of early acute pancreatitis (AP) to be underestimated. As an innovative image analysis method, radiomics may have potential clinical value in early prediction of AP severity.
PURPOSE
To develop a contrast-enhanced (CE) MRI-based radiomics model for the early prediction of AP severity.
STUDY TYPE
Retrospective.
SUBJECTS
A total of 259 early AP patients were divided into two cohorts, a training cohort (99 nonsevere, 81 severe), and a validation cohort (43 nonsevere, 36 severe).
FIELD STRENGTH/SEQUENCE: 3.0T, T -weighted CE-MRI.
ASSESSMENT
Radiomics features were extracted from the portal venous-phase images. The "Boruta" algorithm was used for feature selection and a support vector machine model was established with optimal features. The MR severity index (MRSI), the Acute Physiology and Chronic Health Evaluation (APACHE) II, and the bedside index for severity in acute pancreatitis (BISAP) were calculated to predict the severity of AP.
STATISTICAL TESTS
Independent t-test, Mann-Whitney U-test, chi-square test, Fisher's exact tests, Boruta algorithm, receiver operating characteristic analysis, DeLong test.
RESULTS
Eleven potential features were chosen to develop the radiomics model. In the training cohort, the area under the curve (AUC) of the radiomics model, APACHE II, BISAP, and MRSI were 0.917, 0.750, 0.744, and 0.749, and the P value of AUC comparisons between the radiomics model and scoring systems were all less than 0.001. In the validation cohort, the AUC of the radiomics model, APACHE II, BISAP, and MRSI were 0.848, 0.725, 0.708, and 0.719, respectively, and the P value of AUC comparisons were 0.96 (radiomics vs. APACHE II), 0.40 (radiomics vs. BISAP), and 0.46 (radiomics vs. MRSI).
DATA CONCLUSION
The radiomics model had good performance in the early prediction of AP severity.
LEVEL OF EVIDENCE
3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:397-406.
背景
计算机断层扫描（CT）或磁共振成像（MR）图像可能会低估早期急性胰腺炎（AP）的严重程度。作为一种创新的图像分析方法，放射组学在AP严重程度的早期预测中可能具有潜在的临床价值。
目的
建立基于对比增强（CE）MRI的放射组学模型，用于早期预测AP的严重程度。
研究类型
回顾性研究。
研究对象
共259例早期AP患者被分为两个队列，一个训练队列（99例非重症，81例重症）和一个验证队列（43例非重症，36例重症）。
场强/序列：采用3.0T，T加权CE-MRI。
评估
从门静脉期图像中提取放射组学特征。使用“Boruta”算法进行特征选择，并使用最佳特征建立支持向量机模型。计算磁共振严重指数（MRSI）、急性生理与慢性健康状况评估（APACHE）II以及急性胰腺炎严重程度床边指数（BISAP），以预测AP的严重程度。
统计检验
独立t检验、曼-惠特尼U检验、卡方检验、费舍尔精确检验、Boruta算法、受试者工作特征分析、德龙检验。
结果
选择了11个潜在特征来建立放射组学模型。在训练队列中，放射组学模型、APACHE II、BISAP和MRSI的曲线下面积（AUC）分别为0.917, 0.750, 0.744和0.749，放射组学模型与评分系统之间AUC比较的P值均小于0.001。在验证队列中，放射组学模型、APACHE II、BISAP和MRSI的AUC分别为0.848、0.725、0.708和0.719，AUC比较的P值分别为0.96（放射组学模型与APACHE II）、0.40（放射组学模型与BISAP）和0.46（放射组学模型与MRSI）。
数据结论
放射组学模型在早期预测AP严重程度方面具有良好的性能。
证据水平
3技术效能阶段：2《磁共振成像杂志》, 2020;51:397-406。
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