Comparative analysis of two-dimensional and three-dimensional machine learning models for predicting the degree of...

Most malignant subsolid nodules (SSNs) are non-mucinous adenocarcinomas, and prognosis varies substantially with the degree of invasion. Previous studies have shown that the solid component of SSNs is closely associated with invasiveness. With advances in image analysis, three-dimensional (3D) computed tomography (CT) features may provide more comprehensive information than conventional two-dimensional (2D) measurements. This study aimed to investigate the value of 2D and 3D CT features for predicting the extent of invasion in SSNs and to compare the diagnostic performance and potential clinical utility of 2D- and 3D-based predictive models.

This single-center retrospective model-development study included 234 SSNs from 223 patients with pathologically confirmed lung non-mucinous adenocarcinoma. Minimally invasive adenocarcinoma and Grade 1 tumors were classified as the low-risk group, whereas Grades 2 and 3 tumors were classified as the high-risk group. Patients were randomly assigned at the patient level to a training set comprising 171 SSNs from 164 patients and a held-out internal test set comprising 63 SSNs from 59 patients. 2D and 3D CT parameters, including consolidation-to-tumor ratio of volume (CTRV), were evaluated. Least absolute shrinkage and selection operator regression was used for feature selection, and models based on five machine-learning algorithms were developed using 5×5-fold nested cross-validation within the training set. The best-performing 2D and 3D models were selected according to the area under the receiver operating characteristic curve (AUC) calculated from pooled out-of-fold (OOF) predictions and were subsequently evaluated in the held-out test set. Model performance was assessed in terms of discrimination, calibration, and decision curve analysis, and AUCs were compared using the paired DeLong test.

The best-performing 2D and 3D models were 2D-XGB and 3D-RF, respectively, with pooled OOF AUCs of 0.713 (0.632-0.790) and 0.712 (0.633-0.785). In the held-out test set, 2D-XGB achieved an AUC of 0.766 (0.648-0.869), whereas 3D-RF achieved an AUC of 0.847 (0.745-0.931). No significant difference in AUC was observed between the two models based on either the pooled OOF predictions or the held-out test set predictions. Permutation importance analysis indicated that consolidation-to-tumor ratio was the most influential feature in 2D-XGB, whereas mass and CTRV_-100 were among the most important features in 3D-RF. Compared with 2D-XGB, 3D-RF showed higher net benefit across a wider range of threshold probabilities and better calibration.

Both 2D- and 3D-based models showed comparable discriminative performance for predicting the degree of invasion in lung non-mucinous adenocarcinomas presenting as SSNs. The 3D-RF model showed better calibration and a broader range of clinical net benefit, suggesting potential advantages for risk estimation and clinical decision-making. Mass and CTRV derived from multiple density thresholds may provide additional information for characterizing solid components and may support clinical decision-making.