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.