Explainable Machine Learning for Predicting Invasiveness of Pulmonary Adenocarcinoma Presenting as Ground-Glass...

Accurate prediction of the invasiveness of early-stage pulmonary adenocarcinoma presenting as ground-glass nodules (GGNs) remains highly challenging. This study aims to integrate radiomics features from non-contrast CT (NECT) and contrast-enhanced CT (CECT), deep learning features, and intratumoral habitat features to improve prediction accuracy and provide robust support for clinical personalized surgical decision-making.

This dual-center retrospective study included 516 patients with pathologically confirmed GGNs (≤30mm) from December 2018 to September 2023. Patients from center 1 were randomly divided into training (276 patients) and internal-validation (120 patients) sets, while patients from center 2 were all included into external-validation (120 patients) set. Intratumoral habitat analysis (ITH) was performed on NECT and CECT images using the K-means clustering algorithm. Radiomic features were extracted from the lesion regions and clustered subregions, deep learning features were obtained via a fine-tuned ResNet50 model. After feature selection, eight predictive models were established. Additionally, a dynamic nomogram (the comprehensive model) was developed and subjected to explainable analysis using SHAP (SHapley Additive exPlanations). Model performance was assessed using area under the curve (AUC), decision curve analysis (DCA), and calibration curves.

Among eight predictive models, the comprehensive model, which utilized multi-modal data as input demonstrated the highest accuracy in distinguishing invasive adenocarcinoma (IAC) from pre-invasive lesions (AAH/AIS/MIA). In the training set, the AUC was 0.92 (95% CI: 0.89-0.95), with 84% accuracy, 85% sensitivity, and 84% specificity. In the internal-validation set, the AUC was 0.90 (95% CI: 0.86-0.95), with 82% accuracy, 88% sensitivity, and 74% specificity. In the external-validation set, the AUC was 0.85 (95% CI: 0.80-0.91), with 80% accuracy, 80% sensitivity, and 80% specificity. DCA analysis showed that the nomogram provided the highest net benefit when the threshold probability was ≥0.4, and the Hosmer-Lemeshow test confirmed good calibration (P>0.05). SHAP analysis and the selected of optimal features revealed that wavelet-based texture features, deep learning features, and ITH features made significant contributions to the model's performance.

The comprehensive model (radiomics, deep learning, ITH, clinical variables) enables reliable prediction of the invasiveness of GGNs-ADC. It bridges imaging and pathology, potentially advancing personalized surgical decision-making in early-stage lung adenocarcinoma.