Abstract
IntroductionPreoperative differentiation of adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) using computed tomography (CT) is crucial for clinical management. However, accurately classifying pure ground-glass nodules (pGGNs) presents significant challenges. The quantitative integration of intratumor heterogeneity (ITH) scores may enhance the accuracy of this ternary classification. Therefore, this study aimed to develop ternary classification models to classify AIS, MIA, and IAC by leveraging insights from 15 machine-learning algorithms and integrating ITH scores with clinical data.MethodsThe ternary classification models were evaluated using an independent validation set to assess metrics, such as the macro-average area under the curve (AUC), accuracy, precision, recall, and F1 score. We subsequently applied binary classification models to various tasks derived from the optimal ternary classification model to sequentially address the discordant classifications.ResultsIn this retrospective study, a total of 512 potential pGGNs were classified into training and validation sets at a ratio of 7:3. Among the 15 models, the light gradient boosting machine (LightGBM) exhibited the best predictive performance as a ternary classification model, achieving a macro-average AUC and an accuracy of 0.808 and 0.630, respectively. Upon binary classification, the model achieved a respective AUC and accuracy of 0.839 and 0.630 for classifying AIS, 0.677 and 0.620 for classifying MIA, and 0.908 and 0.780 for classifying IAC.ConclusionThe LightGBM model, identified as the optimal algorithm for integrating ITH scores with clinical data, effectively serves as a ternary classification model for assessing adenocarcinoma invasiveness on chest CT.