Preoperative predicting invasiveness of lung adenocarcinoma manifesting as ground-glass nodules based on multimodal images of dual-layer spectral detector CT radiomics models

Objective To construct and validate conventional and radiomics models based on dual-layer spectral CT radiomics for preoperative prediction of lung ground glass nodules (GGNs) invasiveness. Materials and methods A retrospective study was conducted on 176 GGNs patients who underwent chest non-contrast enhancement scan on dual-layer spectral detector CT at our hospital within 2 weeks before surgery. Patients were randomized into the training cohort and testing cohort. Clinical features, imaging features and spectral quantitative parameters were collected to establish a conventional model. Radiomics models were established by extracting 1781 radiomics features form regions of interest of each spectral image [120 kVp poly energetic images (PI), 60 keV images and electron density maps], respectively. After selecting the optimal radiomic features and integrating multiple machine learning models, the conventional model, PI model, 60 keV model, electron density (ED) model and combined model based on multimodal spectral images were finally established. The performance of these models was assessed through the evaluation of discrimination, calibration, and clinical application. Results In the conventional model, age, vacuole sign, 60 keV and ED were independent risk factors of invasiveness. The combined model using logistic regression-least absolute shrinkage and selection operator classifiers was the optimal model with a higher area under the curve of the training (0.961, 95% confidence interval, CI: 0.932–0.991) and testing set (0.944, 0.890–0.999). Conclusion The combined models are helpful to predict the invasiveness of GGNs before surgery and guide the individualized treatment of patients.