高分辨率CT影像组学联合传统影像学征象预测肺腺癌微血管浸润的价值

目的 探讨高分辨率CT（HRCT）影像组学联合传统影像学征象的综合模型预测肺腺癌微血管浸润的价值。 方法 回顾性分析2015年6月至2019年4月于青岛大学附属医院就诊的微血管浸润状态明确的肺腺癌患者65例（微血管浸润阳性30例、阴性35例），其中，男性33例、女性32例，年龄34~83（60.7±10.3）岁。以患者HRCT检查时间为编号，通过系统随机抽样方法将患者按约3∶1等距抽样分为2组:训练组46例，验证组19例。训练组用于模型的建立，验证组用于模型的效能评价。通过两独立样本t检验、χ2检验或Fisher确切概率法筛选训练组中微血管浸润阳性与阴性患者间差异有统计学意义的传统影像学征象。勾画2组患者的肿瘤三维感兴趣区并提取影像组学特征，通过单因素方差分析和Lasso-Logistic回归分析筛选训练组中有鉴别价值的最优影像组学特征，计算影像组学得分。通过Logistic回归分析构建联合影像组学得分和传统影像学征象预测肺腺癌微血管浸润的综合模型，并绘制列线图，进行效能评价。 结果 共提取影像组学特征1308个，最终得到6个最优影像组学特征。传统影像学征象中仅肿瘤最大径在微血管浸润阳性与阴性患者间的差异有统计学意义（28.10±11.39）mm对（22.32±6.26） mm；t=5.580，P=0.035]，其在训练组中的曲线下面积（AUC）为0.648(95%CI:0.493~0.783)、灵敏度为38.1%、特异度为88.0%；在验证组中的AUC为0.783(95%CI:0.538~0.936)、灵敏度为88.9%、特异度为70.0%。预测肺腺癌微血管浸润的综合模型在训练组中的AUC为0.880(95%CI:0.750~0.957)，灵敏度为90.5%，特异度为72.0%；在验证组中的AUC为0.811(95%CI:0.568~0.951)，灵敏度为88.9%，特异度为80.0%。 结论 基于HRCT影像组学联合传统影像学征象的综合模型对肺腺癌微血管浸润具有较高的预测价值，有助于肺腺癌患者的术前评估。

Value of HRCT radiomics combined with traditional imaging features in predicting microvascular invasion of lung adenocarcinoma

Objective To explore the value of the radiomic nomogram integrating high-resolution CT (HRCT) radiomics and traditional imaging features to predict the microvascular invasion (MVI) of lung adenocarcinoma (LAC). Methods A total of 65 patients with LAC (30 MVI-present LACs and 35 MVI-absent LACs) with pathologically confirmed MVI status in the Affiliated Hospital of Qingdao University from June 2015 to April 2019 were retrospectively enrolled, among whom 33 were males and 32 were females with age range of 34–83 (60.7±10.3) years old. After patients were numbered depending?on?their HRCT examination time, they were randomly divided into two groups according to the systematic sampling method (about 3∶1 ratio equidistance sampling): 46 patients constituted the training set and 19 patients constituted the validation set. The training set was used to build the model, and the validation set was used to evaluate the model effectiveness. The traditional imaging features with a significant difference between the MVI-present and MVI-absent patients were selected by two independent samples t test, χ2 test, or Fisher's exact probability method. The three-dimensional regions of interest of the tumors in the two groups were drawn, and the radiomic features were extracted. The optimal radiomic features were selected by one-way ANOVA and Lasso-Logistic regression analysis, and the radiomic scores were calculated. The combined nomogram to predict MVI of LAC, incorporating the radiomic scores and the traditional imaging features, was constructed by Logistic regression, and its effectiveness was evaluated. Results A total of 1308 radiomic features were extracted, and 6 optimal radiomic features were finally obtained. Among the traditional imaging features, only the longest diameter of the tumor was statistically different between the MVI-present and the MVI-absent patients (28.10±11.39) mm vs. (22.32±6.26) mm; t=5.580, P=0.035]. For the traditional imaging features, the area under the curve (AUC) was 0.648 (95%CI: 0.493–0.783), the sensitivity was 38.1%, and the specificity was 88.0% in the training set; meanwhile, the AUC was 0.783 (95%CI: 0.538–0.936), the sensitivity was 88.9%, and the specificity was 70.0% in the validation set. For the combined nomogram, the AUC was 0.880 (95%CI: 0.750–0.957), the sensitivity was 90.5%, and the specificity was 72.0% in the training set; whereas the AUC was 0.811 (95%CI: 0.568–0.951), the sensitivity was 88.9%, and the specificity was 80.0% in the validation set. Conclusion The radiomic nomogram, incorporating HRCT radiomics and traditional imaging features, shows favorable predictive efficacy for MVI status in LAC, which might assist in the preoperative evaluation of patients with LAC.