基于CT的放射组学分析用于肝门部胆管癌神经周围侵犯的无创预测
CT-Based Radiomics Analysis for Noninvasive Prediction of Perineural Invasion of Perihilar Cholangiocarcinoma.
作者信息
Zhan Peng-Chao, Lyu Pei-Jie, Li Zhen, Liu Xing, Wang Hui-Xia, Liu Na-Na, Zhang Yuyuan, Huang Wenpeng, Chen Yan, Gao Jian-Bo
机构信息
Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Henan Key Laboratory of Imaging Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, China.
出版信息
Front Oncol. 2022 Jun 20;12:900478. doi: 10.3389/fonc.2022.900478. eCollection 2022.
PURPOSE
The study aimed to construct and evaluate a CT-Based radiomics model for noninvasive detecting perineural invasion (PNI) of perihilar cholangiocarcinoma (pCCA) preoperatively.
MATERIALS AND METHODS
From February 2012 to October 2021, a total of 161 patients with pCCA who underwent resection were retrospectively enrolled in this study. Patients were allocated into the training cohort and the validation cohort according to the diagnostic time. Venous phase images of contrast-enhanced CT were used for radiomics analysis. The intraclass correlation efficient (ICC), the correlation analysis, and the least absolute shrinkage and selection operator (LASSO) regression were applied to select radiomics features and built radiomics signature. Logistic regression analyses were performed to establish a clinical model, a radiomics model, and a combined model. The performance of the predictive models was measured by area under the receiver operating characteristic curve (AUC), and pairwise ROC comparisons between models were tested using the Delong method. Finally, the model with the best performance was presented as a nomogram, and its calibration and clinical usefulness were assessed.
RESULTS
Finally, 15 radiomics features were selected to build a radiomics signature, and three models were developed through logistic regression. In the training cohort, the combined model showed a higher predictive capability (AUC = 0.950) than the radiomics model and the clinical model (AUC: radiomics = 0.914, clinical = 0.756). However, in the validation cohort, the AUC of the radiomics model (AUC = 0.885) was significantly higher than the other two models (AUC: combined = 0.791, clinical = 0.567). After comprehensive consideration, the radiomics model was chosen to develop the nomogram. The calibration curve and decision curve analysis (DCA) suggested that the nomogram had a good consistency and clinical utility.
CONCLUSION
We developed a CT-based radiomics model with good performance to noninvasively predict PNI of pCCA preoperatively.
目的
本研究旨在构建并评估一种基于CT的放射组学模型，用于术前无创检测肝门部胆管癌（pCCA）的神经周围侵犯（PNI）。
材料与方法
2012年2月至2021年10月，本研究回顾性纳入了161例行肝门部胆管癌切除术的患者。根据诊断时间将患者分为训练队列和验证队列。使用对比增强CT的静脉期图像进行放射组学分析。应用组内相关系数（ICC）、相关性分析和最小绝对收缩和选择算子（LASSO）回归来选择放射组学特征并构建放射组学特征。进行逻辑回归分析以建立临床模型、放射组学模型和联合模型。通过受试者操作特征曲线下面积（AUC）来衡量预测模型的性能，并使用德龙法对模型之间的成对ROC比较进行检验。最后，将性能最佳的模型呈现为列线图，并评估其校准和临床实用性。
结果
最终，选择了15个放射组学特征来构建放射组学特征，并通过逻辑回归开发了三个模型。在训练队列中，联合模型显示出比放射组学模型和临床模型更高的预测能力（AUC = 0.950）（AUC：放射组学 = 0.914，临床 = 0.756）。然而，在验证队列中，放射组学模型的AUC（AUC = 0.885）显著高于其他两个模型（AUC：联合 = 0.791，临床 = 0.567）。综合考虑后，选择放射组学模型来开发列线图。校准曲线和决策曲线分析（DCA）表明列线图具有良好的一致性和临床实用性。
结论
我们开发了一种性能良好的基于CT的放射组学模型，用于术前无创预测肝门部胆管癌的神经周围侵犯。
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