增强CT影像组学评分和临床指标列线图预测食管胃结合部腺癌患者脉管癌栓的价值

滕志鹏; 王绪宁; 韩雅琳; 徐茂林; 柯木; 徐红丽; 宁伯彬; 孟庆禹; 黄埔; 邝衍绅; 李森; 王文琛; 李鹏; 刘洪一; 贾宝庆

doi:10.3969/j.issn.2095-5227.2022.06.011

增强CT影像组学评分和临床指标列线图预测食管胃结合部腺癌患者脉管癌栓的价值

Value of a nomogram based on contrast-enhanced CT radiomics score and clinical data for prediction of tumor thrombus in patients with adenocarcinoma of esophagogastric junction

摘要

摘要:
背景食管胃结合部腺癌(adenocarcinoma of esophagogastric junction，AEG)的位置特殊，生物学行为不稳定，患者预后较差。脉管癌栓是影响AEG预后的独立危险因素，术前了解AEG脉管癌栓情况，有助于临床医生制定更加合理的个体化治疗方案。
目的探讨CT影像组学预测AEG脉管癌栓的应用价值。
方法选取2015年1月- 2019年7月于解放军总医院第一医学中心普外二科诊断明确并接受手术治疗的79例AEG患者的CT影像图像，应用3D Slicer软件在静脉期CT影像最大病灶层面的图像上提取影像组学特征，并通过Lasso回归降维筛选出影像组学特征构建预测模型。计算患者的影像组学评分，将影像组学评分和临床指标作为参数构建列线图，通过受试者工作特征曲线(receiver operator characteristic curve，ROC)评价影像组学模型和列线图术前预测AEG脉管癌栓效能。
结果 79例患者中，男性68例，女性11例，年龄30 ~ 80(63.8±9.5)岁。无脉管癌栓54例，有脉管癌栓25例，脉管癌栓的发生率为31.6%。在每例患者的增强CT图像中均提取873个特征，通过Lasso回归分析并10折交差验证最终确定2D平面最大直径等7个影像组学特征构建了影像组学模型，最大曲线下面积(area under the curve，AUC)达到0.852，敏感度为0.880，特异性为0.849。列线图的AUC为0.885，敏感度为0.880，特异性为0.892。随机抽样2/3样本行ROC分析，结果与所建模型非常接近。
结论基于增强CT影像组学评分和临床指标的列线图对AEG脉管癌栓有较好的预测效能，可以为临床医生提供较准确的诊断和决策支持。

Abstract:
Background The incidence of adenocarcinoma of esophagogastric junction (AEG) has been increasing. Due to its special location and unstable biological behavior, prognosis of AEG is poor. Tumor thrombus is an independent risk factor affecting the prognosis of AEG. Preoperative understanding of tumor thrombus of AEG can help clinicians scientifically and objectively formulate a more reasonable individual treatment plan.
Objective To evaluate the application value of CT radiomics in prediction of vascular tumor thrombus of AEG.
Methods CT images of 79 patients with AEG who were diagnosed and surgically treated in the Second Department of General Surgery, the First Medical Center, Chinese PLA General Hospital from January 2015 to July 2019 were selected. 3D Slicer software was used to extract the radiomics features from the images of the largest lesion layer of venous CT images, and the radiomics features were screened out by Lasso regression dimensionality reduction to construct the prediction model. Radiomic scores were calculated, then the radiomics scores and clinical data were used as parameters to construct the nomogram. The receiver operator characteristic curve (ROC) was used to evaluate the model for preoperative prediction of tumor thrombus of AEG.
Results Of the 79 cases, 68 cases were male and 11 cases were female, and the age ranged from 30 to 80 years, with the average of (63.8 + 9.5) years. There were 54 patients without tumor thrombus and 25 patients with tumor thrombus, and the incidence of tumor thrombus was 31.6%. Totally 873 features were extracted from each patient, while 7 features including 2D maximum diameter were finally determined by Lasso regression analysis and ten fold cross validation to construct a radiomics model, with AUC of 0.852, sensitivity of 0.880 and specificity of 0.849. The AUC, sensitivity and specificity of the nomogram were 0.885, 0.880 and 0.892, respectively. Two-thirds of the samples were randomly sampled for ROC analysis, and the results were very close to the established model.
Conclusion The nomogram based on contrast-enhanced CT radiomics score and clinical data has better predictive efficacy for vascular tumor thrombus of AEG.

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