Academic radiology · 2025
Habitat Radiomics and Deep Learning Features Based on CT for Predicting Lymphovascular Invasion in T1-stage Lung Adenocarcinoma: A Multicenter Study.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Xu Pengliang, Yao Fandi, Xu Yunyu, Yu Huanming, Li Wenhui, Zhi Shengxu, Peng Xiuhua
Аннотация
RATIONALE AND OBJECTIVES: The research aims to examine how CT-derived habitat radiomics can be used to predict lymphovascular invasion (LVI) in patients with T1-stage lung adenocarcinoma (LUAD), and compare its effectiveness to traditional radiomics and deep learning (DL) models.
MATERIALS AND METHODS: We retrospectively analyzed 349 T1-stage LUAD patients from three centers from January 2021 to March 2024. The K-means algorithm was utilized to cluster CT images and apparent diffusion coefficient maps. Following features selection, we constructed three types of models, namely radiomics, habitat, and DL to identify patients with LVI. The evaluation of all models was conducted by employing the area under the receiver operating characteristic curve (AUC), calibration curves and decision curve analysis.
RESULTS: 349 eligible patients were divided into an internal training set of 210 and an external test set of 139. We identified four distinct habitats, with the AUC for the overall habitat area outperforming that of the four sub-areas. Within the test set, the habitat model reached a higher AUC of 0.941 in contrast to the radiomics model at 0.918 and the deep learning model at 0.896.
CONCLUSION: CT-based habitat radiomics shows promise in predicting LVI in T1-stage LUAD patients, with the habitat signature demonstrating superior performance and significant advantages in identifying patients who are LVI-positive.
MATERIALS AND METHODS: We retrospectively analyzed 349 T1-stage LUAD patients from three centers from January 2021 to March 2024. The K-means algorithm was utilized to cluster CT images and apparent diffusion coefficient maps. Following features selection, we constructed three types of models, namely radiomics, habitat, and DL to identify patients with LVI. The evaluation of all models was conducted by employing the area under the receiver operating characteristic curve (AUC), calibration curves and decision curve analysis.
RESULTS: 349 eligible patients were divided into an internal training set of 210 and an external test set of 139. We identified four distinct habitats, with the AUC for the overall habitat area outperforming that of the four sub-areas. Within the test set, the habitat model reached a higher AUC of 0.941 in contrast to the radiomics model at 0.918 and the deep learning model at 0.896.
CONCLUSION: CT-based habitat radiomics shows promise in predicting LVI in T1-stage LUAD patients, with the habitat signature demonstrating superior performance and significant advantages in identifying patients who are LVI-positive.