A semiautomated radiomics model based on multimodal spectral CT for preoperative discriminating of the invasiveness of pure ground-glass nodules.

Abstract

Abstract Background To investigate the value of dual-layer spectral CT-based multimodal radiomics in accessing the invasiveness of lung adenocarcinoma manifesting as ground glass nodules (GGNs). Method In this study, 125 GGNs with pathologically confirmed preinvasive adenocarcinoma and lung adenocarcinoma were divided into a training set (n = 87) and a test set (n = 38). Each lesion was automatically detected and segmented by the pre-trained neural networks (SCPM-Net and 3D-RCNN), and 63 multimodal radiomic features were extracted. The least absolute shrinkage and selection operator (LASSO) was used to select target features, and a rad-score was constructed in the training set. Logistic regression analysis was conducted to establish a joint model which combined age, gender, and the rad-score. Diagnostic performance of the two models were compared by the receiver operating characteristic (ROC) curve and precision-recall (PR) curve. The test set was used to evaluate the predictive performance and calibration of the model. Results Five radiomic features (a_ED_original_firstorder_90Percentile, a_ID_original_firstorder_Entropy, p_original_shape_Maximum2DDiameterSlice, v_ED_original_firstorder_90Percentile and v_Zeff_original_firstorder_Uniformity) were selected. In the training and test sets, the AUC of the radiomics model was 0.896 (95% CI: 0.830, 0.962) and 0.881 (95% CI: 0.777, 0.985) respectively, and the AUC of the joint model was 0.932 (95% CI: 0.882–0.982) and 0.887 (95% CI: 0.786, 0.988) respectively. There was no significant difference in AUC between the training and test sets (0.896 vs. 0.932, p = 0.088; 0.881 vs. 0.887, p = 0.480). Conclusion Multimodal radiomics based on dual-layer spectral CT showed good predictive performance in differentiating the invasiveness of GGNs, which could assist in the decision of clinical treatment strategies.