Ternary-Classification Habitat Model for Invasiveness and Grade of Lung Adenocarcinoma Presenting as a Subsolid...

BACKGROUND. Habitat imaging provides a novel approach to capture spatial heterogeneity within lesions. OBJECTIVE. The purpose of this study was to develop a ternary-classification habitat model to characterize lung adenocarcinoma presenting as a subsolid nodule (SSN) on CT and to test the model's diagnostic performance compared with 2D and radiomic models. METHODS. This retrospective study included 747 patients (median age, 56 years; 241 men, 506 women) with 834 resected lung adenocarcinomas that presented as SSNs on low-dose CT between July 2018 and July 2023. Adenocarcinomas from one center were divided into training (n = 440) and internal test (n = 189) sets; adenocarcinomas from three other centers formed an external test set (n = 205). Adenocarcinomas were classified as noninvasive adenocarcinoma, grade 1 invasive adenocarcinoma (IAC), or grade 2 or 3 (hereafter, grade 2/3) IAC. Ternary-classification models were built in the training set using multivariable multinomial logistic regression analyses (2D model: diameter and consolidation-to-tumor ratio; habitat model: volume and volume ratio of attenuation-based subregions; radiomic model: extracted radiomic features; combined model: habitat and radiomic features). Performance was evaluated using macroaveraged and class-specific AUCs. RESULTS. The optimal number of habitats was four. The 2D, habitat, radiomic, and combined models had macroaveraged AUCs in the internal test set of 0.857, 0.909, 0.914, and 0.912 and in the external test set of 0.871, 0.919, 0.924, and 0.926, respectively. Those four models had class-specific AUCs in the external test set for noninvasive adenocarcinoma of 0.945, 0.956, 0.961, and 0.955; for grade 1 IAC of 0.792, 0.858, 0.857, and 0.862; and for grade 2/3 IAC of 0.875, 0.940, 0.952, and 0.961, respectively. In the external test set, macroaveraged AUCs and class-specific AUCs for grades 1 and 2/3 IAC were significantly higher for habitat, radiomic, and combined models versus the 2D model, but not for other model comparisons; class-specific AUCs for noninvasive adenocarcinoma were not significantly different for any model comparisons. CONCLUSION. The habitat model performed significantly better than the 2D model in ternary adenocarcinoma classification; its performance was not significantly different from the radiomic and combined models. CLINICAL IMPACT. The habitat model's combination of interpretability and diagnostic performance supports its utility for noninvasive risk stratification of SSNs encountered during lung cancer screening.