Preoperative Ternary Classification of Pulmonary Ground-Glass Nodules (AIS/MIA/IAC): ResNet-10 Outperforms Radiomics...

IntroductionPreoperative differentiation among adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) is crucial for guiding ground-glass nodule (GGN) management. This multicenter study evaluated the comparative utility of deep learning (DL), radiomics, and conventional machine learning (cML)-based clinicoradiographic models for this ternary classification.MethodsWe developed four DL models (DenseNet-121, ResNet-10, ResNet-18, and VGG-13) for the ternary classification of AIS, MIA, and IAC using multicenter CT datasets. For comparative analysis, we constructed two additional classification models: (1) a radiomics model employing feature engineering through analysis of variance, recursive feature elimination with cross-validation, and least absolute shrinkage and selection operator, and (2) the cML-based clinicoradiographic model utilizing 12 different classifiers. The performance of all models was evaluated using the macro area under the curve (Macro-AUC) metric.Results847 GGNs postoperatively confirmed as lung adenocarcinoma were included in this multicenter study, which were randomly split into a training set (70%, n=592) and a validation set (30%, n=255). The DL model ResNet-10 demonstrated superior performance, achieving a Macro-AUC of 0.8055 (95% CI: 0.7723-0.8387), an accuracy of 0.6300 (95% CI: 0.5541-0.6764), and an F1-score of 0.4206 (95% CI: 0.3821-0.4598). This performance surpassed that of the radiomics model, which had a Macro-AUC of 0.7801 (95% CI: 0.7432-0.8170), an accuracy of 0.6100 (95% CI: 0.5276-0.6204), and an F1-score of 0.5505 (95% CI: 0.4983-0.6017), and the cML-based clinicoradiographic model, which achieved a Macro-AUC of 0.7770 (95% CI: 0.708-0.846), an accuracy of 0.6000 (95% CI: 0.5376-0.6604), and an F1-score of 0.4438 (95% CI: 0.3925-0.4961).ConclusionThe ResNet-10 network established a novel ternary classification model for predicting the invasiveness of GGNs. This approach provides clinically actionable insights that support surgical planning and facilitate risk-adapted management.