Preoperative prediction of vasculogenic mimicry in lung adenocarcinoma using a CT radiomics model

To develop and validate a non-invasive computed tomography (CT)-based radiomics model for predicting vasculogenic mimicry (VM) status in lung adenocarcinoma (LA).

Two hundred and three patients with LA were enrolled retrospectively and grouped into training and test groups with a ratio of 7:3. Uni- and multivariate logistic regression analyses were performed in the training cohort to screen the independent clinical and radiological factors for VM, and the clinical model was then established. A radiomics model was established based on the rad-scores through support vector machine (SVM). A radiomics nomogram model was subsequently constructed by combining the rad-score with clinical-radiological factors. The receiver operating characteristic curve (ROC), calibration curves, and decision curve analysis (DCA) were conducted to evaluate the performance of the three models.

Nine selected radiomics features were selected for the radiomics model and the maximum length and spiculation sign were constructed for the clinical model. The radiomics nomogram model integrating the maximum length, spiculation sign, and rad-score yielded the best AUC in both the training (AUC = 0.925) and test cohorts (AUC = 0.978), in comparison with the radiomics model (AUC = 0.907 and 0.964, in both the training and test cohorts) and the clinical model (AUC = 0.834 and 0.836 in both training and test cohorts).

The CT-based radiomics nomogram model showed satisfying discriminating performance for preoperatively and non-invasively predicting VM expression status in LA patients.