Academic radiology · 2026
Habitat-Based Radiomics on Dual-Energy CT for Preoperative Assessment of Invasiveness in Lung Adenocarcinoma with Ground-Glass Nodules.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Yang Daoyu, Kang Shaolei, Lv Fajin, Ma Ling, Zhao Xunran, Duan Hui, Wei Wenyan, Liu Jian, Xiong Zhenliang, Han Dan, Zeng Xianchun
Аннотация
RATIONALE AND OBJECTIVES: This study aimed to preoperatively predict the invasiveness of ground-glass nodules (GGNs) using habitat-based radiomics derived from dual-energy computed tomography (CT).
MATERIALS AND METHODS: This study retrospectively included 199 patients (267 GGNs) in the internal cohort and 52 patients (54 GGNs) in the external cohort, all with histologically confirmed minimally invasive adenocarcinoma (MIA) or invasive adenocarcinoma (IAC). All patients underwent conventional CT imaging using a dual-layer spectral detector system, and electron density (ED) images were reconstructed from the spectral base images. Habitat subregions were delineated by applying k-means clustering to voxel intensity values, and radiomic features were subsequently extracted from these subregions. The internal cohort was randomly divided into training and test sets in a 7:3 ratio. Extreme gradient boosting (XGBoost) models were developed, and model performance was evaluated using area under the curve (AUC) with 95% confidence intervals (CIs).
RESULTS: For nonhabitat models, the ED-based approach outperformed conventional CT in both the internal test set (AUC: 0.7491; 95% CI, 0.5930-0.8799 vs. 0.7303; 95% CI, 0.6005-0.8514) and the external test set (AUC 0.7366; 95% CI, 0.5986-0.8586 vs. 0.7090; 95% CI, 0.5556-0.8436). For habitat-based models, the integration of subregional features from both ED and conventional CT images achieved the best predictive performance, yielding an AUC of 0.9052 (95% CI, 0.8255-0.9676) in the internal test set and 0.8414 (95% CI, 0.7215-0.9361) in the external test set.
CONCLUSION: This habitat-based radiomics strategy may offer a promising approach for preoperative differentiation of IAC from MIA in pulmonary GGNs, supporting personalized treatment planning.
MATERIALS AND METHODS: This study retrospectively included 199 patients (267 GGNs) in the internal cohort and 52 patients (54 GGNs) in the external cohort, all with histologically confirmed minimally invasive adenocarcinoma (MIA) or invasive adenocarcinoma (IAC). All patients underwent conventional CT imaging using a dual-layer spectral detector system, and electron density (ED) images were reconstructed from the spectral base images. Habitat subregions were delineated by applying k-means clustering to voxel intensity values, and radiomic features were subsequently extracted from these subregions. The internal cohort was randomly divided into training and test sets in a 7:3 ratio. Extreme gradient boosting (XGBoost) models were developed, and model performance was evaluated using area under the curve (AUC) with 95% confidence intervals (CIs).
RESULTS: For nonhabitat models, the ED-based approach outperformed conventional CT in both the internal test set (AUC: 0.7491; 95% CI, 0.5930-0.8799 vs. 0.7303; 95% CI, 0.6005-0.8514) and the external test set (AUC 0.7366; 95% CI, 0.5986-0.8586 vs. 0.7090; 95% CI, 0.5556-0.8436). For habitat-based models, the integration of subregional features from both ED and conventional CT images achieved the best predictive performance, yielding an AUC of 0.9052 (95% CI, 0.8255-0.9676) in the internal test set and 0.8414 (95% CI, 0.7215-0.9361) in the external test set.
CONCLUSION: This habitat-based radiomics strategy may offer a promising approach for preoperative differentiation of IAC from MIA in pulmonary GGNs, supporting personalized treatment planning.