术前预测胃癌Lauren分型的CT影像组学诺模图的构建和验证

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摘要 目的: 探讨基于CT图像的影像组学诺模图术前预测胃癌Lauren分型的可行性。方法: 回顾性分析经病理检查确认的539例胃癌患者的临床资料，按照7 ∶3的比例随机分为训练集和验证集，利用ITKSNAP软件对门脉期CT图像进行勾画，从瘤内及瘤周提取两组影像组学特征。利用最小绝对收缩和选择算子(least absolute shrinkage and selection operator,LASSO)回归筛选出最优特征组合，分别构建了基于瘤内的模型、基于瘤周的模型及联合瘤内及瘤周特征的影像组学标签，基于临床特征构建了临床模型1和临床模型2，最后结合临床特征和影像组学标签构建了影像组学模型。利用受试者工作特征（receiver operator characteristic, ROC）曲线下面积（area under curve,AUC）评估模型的预测能力。采用Delong检验比较各个模型间的预测性能。采用校准曲线验证模型预测概率与实际病理结果的匹配性，决策曲线评估临床信息的有效性。结果：影像组学标签在训练集与验证集的AUC分别为0.715 (95%CI：0.663～0.767)，0.714(95%CI：0.636～0.792)。结合了临床特征及影像组学标签的影像组学模型性能优于其他模型，在训练集与验证集的AUC分别为0.745(95%CI：0.696～0.795)，0.758（95%CI：0.685～0.831）。此外，校准曲线和决策曲线证明了影像组学诺模图具有良好的匹配性以及临床实用性。结论： 结合了影像组学标签及临床特征的影像组学模型有助于区分Lauren分型中的弥漫型及肠型胃癌，为合理制定临床治疗策略提供了依据。

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	丁奕1
	卢超2
	王霄霄2
	陈建1
	单秀红2

关键词 ： Lauren分型, 影像组学, 瘤周分析, 胃癌, 计算机断层扫描

Abstract：Objective: To explore the feasibility of CT-based radiomics nomogram to preoperatively differentiate Lauren diffuse type from intestinal type in gastric cancer. Methods: The clinical data of 539 patients with pathologically diagnosed gastric cancer were retrospectively analyzed. All patients randomly separated into two cohorts at a 7 ∶3 ratio for training and validation. Venous phase CT images were segmented by radiologists with ITK-SNAP software manually.Two sets of radiomic features were derived from tumor region and peritumor on venous phase CT images.With the least absolute shrinkage and selection operator(LASSO) logistic regression，the effective characteristics were selected. A tumor-based model, a peripheral ring-based model, a combined radiomic signature,clinical model 1 and clinical model 2 were proposed. Afterwards, a radiomic nomogram integrating the combined radiomic signature and clinical characteristics was developed. Receiver operating characteristic (ROC) curves as well as corresponding area under ROC curves (AUC) were estimated for both cohorts to assess the corresponding discrimination ability.We adopted Delong-test to compare the predictive performance between each two models. Calibration curves were conducted to verify the good fitness of model predictive outputs with actual values for the radiomic nomogram. Decision curves were conducted in the validation to quantify the usefulness in clinical trials. Results: The combined radiomic signature achieving an AUC of 0.715 (95% CI： 0.663-0.767) in the training cohort and 0.714 (95%CI： 0.636-0.792) in the validation cohort. The radiomic nomogram incorporating the combined radiomic signature,and clinical characteristics supassed all the other models with a training AUC of 0.745 (95%CI： 0.696-0.795) and a validation AUC of 0.758 (95%CI：0.685-0.831). Further, calibration curves and decision curves demonstrated its great model fitness and clinical usefulness. Conclusion: The radiomic nomogram based on the combined radiomic signature and clinical characteristics held potential in differentiating Lauren diffuse type from intestinal type, which is benefical for reasonable clinical treatment strategy.

收稿日期: 2020-04-13

通讯作者: 单秀红（通讯作者），主任医师，教授，E-mail：13913433095@163.com

作者简介: 丁奕（1987—），男，硕士研究生

引用本文:

丁奕1，卢超2，王霄霄2，陈建1，单秀红2. 术前预测胃癌Lauren分型的CT影像组学诺模图的构建和验证[J]. 江苏大学学报:医学版, 2020, 30(05): 439-444. DING Yi1, LU Chao2, WANG Xiao-xiao2, CHEN Jian1, SHAN Xiu-hong2.

Development and validation of a radiomic nomogram for preoperative Lauren classification in gastric cancer

. Journal of Jiangsu University(Medicine Edition), 2020, 30(05): 439-444.

链接本文:

http://zzs.ujs.edu.cn/xbyxb/CN/ 或 http://zzs.ujs.edu.cn/xbyxb/CN/Y2020/V30/I05/439

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