Radiomics Nomogram for Prediction of Peritoneal Metastasis in Patients With Gastric Cancer
Radiomics Nomogram for Prediction of Peritoneal Metastasis in Patients With Gastric Cancer
复制标题
放射组学列线图预测胃癌患者腹膜转移
DOI：
10.3389/fonc.2020.01416
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发表时间：
2020-08-20
影响因子：
4.7
通讯作者：
Li, Guoxin
中科院分区：
文献类型：
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作者：
Huang, Weicai;Zhou, Kangneng;Li, Guoxin
目的：评价基于计算机断层扫描(CT)的放射组学成像特征能否预测胃癌腹膜转移(PM)，并建立术前预测PM状态的标准图。方法：收集两个肿瘤中心连续955例T4胃癌患者的CT图像，对其放射组学特征进行回顾性分析，并对训练队列和两个验证队列中的292个定量图像特征建立的预测模型进行验证。采用套索回归模型进行特征选择和放射组学特征构建。采用多因素Logistic回归分析建立预测模型。放射组学诺模图是将放射组学标志物和临床T、N阶段相结合而形成的。使用校准、辨别力和临床实用性来评估诺模图的性能。结果：在训练和验证队列中，PM状态与放射组学特征显著相关。在多变量Logistic分析中发现放射组学特征是腹膜转移的独立预测因子。对于训练和内部和外部验证队列，放射组学签名预测PM的受试者操作特征曲线(AUC)下面积分别为0.751(95%CI，0.703-0.799)、0.802(95%CI，0.691-0.912)和0.745(95%CI，0.683-0.806)。此外，对于培训和内部和外部验证队列，放射组学诺模图预测PM的AUC分别为0.792(95%CI，0.748-0.836)、0.870(95%CI，0.795-0.946)和0.815(95%CI，0.763-0.867)。结论：基于CT的放射组学征象可以预测腹膜转移，放射组学正常图对预测胃癌患者术前PM状态有重要意义。
Objective:The aim of this study is to evaluate whether radiomics imaging signatures based on computed tomography (CT) could predict peritoneal metastasis (PM) in gastric cancer (GC) and to develop a nomogram for preoperative prediction of PM status. Methods:We collected CT images of pathological T4 gastric cancer in 955 consecutive patients of two cancer centers to analyze the radiomics features retrospectively and then developed and validated the prediction model built from 292 quantitative image features in the training cohort and two validation cohorts. Lasso regression model was applied for selecting feature and constructing radiomics signature. Predicting model was developed by multivariable logistic regression analysis. Radiomics nomogram was developed by the incorporation of radiomics signature and clinicalTandNstage. Calibration, discrimination, and clinical usefulness were used to evaluate the performance of the nomogram. Results:In training and validation cohorts, PM status was associated with the radiomics signature significantly. It was found that the radiomics signature was an independent predictor for peritoneal metastasis in multivariable logistic analysis. For training and internal and external validation cohorts, the area under the receiver operating characteristic curves (AUCs) of radiomics signature for predicting PM were 0.751 (95%CI, 0.703-0.799), 0.802 (95%CI, 0.691-0.912), and 0.745 (95%CI, 0.683-0.806), respectively. Furthermore, for training and internal and external validation cohorts, the AUCs of radiomics nomogram for predicting PM were 0.792 (95%CI, 0.748-0.836), 0.870 (95%CI, 0.795-0.946), and 0.815 (95%CI, 0.763-0.867), respectively. Conclusions:CT-based radiomics signature could predict peritoneal metastasis, and the radiomics nomogram can make a meaningful contribution for predicting PM status in GC patient preoperatively.