Frontiers in oncology · 2026
A fusion model based on tumor and peritumoral CT radiomics for differentiating bronchiolar adenoma from lung adenocarcinoma.
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Zhao Ziqian, Ke Tengfei, Xu Zeyan, Zhou Yong, Dong Yanwen, Liu Yifan, Wu Jianping, Wei Wenyan, Han Dan, Zhao Wen
Аннотация
PURPOSE: This study aimed to develop and validate a combined clinical and tumor-peritumoral CT radiomics model to differentiate bronchiolar adenoma (BA) from lung adenocarcinoma (LUAD), thereby improving preoperative diagnostic accuracy and guiding individualized treatment strategies.
METHODS: A total of 362 patients with pathologically confirmed BA or LUAD were retrospectively analyzed. Data from Medical Center 1 (n = 281) were divided into training and test sets (7:3 ratio), and data from Medical Center 2 (n = 81) served as an external validation set. Clinical characteristics, CT morphological features, and tumor-peritumoral radiomics features were extracted. Five machine learning algorithms were applied to construct and compare predictive models.
RESULTS: Lung lobe distribution, density, vacuolar sign, tumor-associated vessels, distance to pleura, and nodule diameters differed significantly between BA and LUAD. Among radiomics models, the tumor-peritumoral MLP model achieved the best performance (AUCs: 0.918, 0.912, 0.888). The clinical-radiomics fusion model outperformed single models, with AUCs of 0.935, 0.939, and 0.910 and accuracies of 0.862, 0.847, and 0.864 in the training, test, and validation sets, respectively.
CONCLUSION: The proposed fusion model enables accurate, non-invasive differentiation between BA and LUAD, offering valuable support for personalized clinical decision-making.
METHODS: A total of 362 patients with pathologically confirmed BA or LUAD were retrospectively analyzed. Data from Medical Center 1 (n = 281) were divided into training and test sets (7:3 ratio), and data from Medical Center 2 (n = 81) served as an external validation set. Clinical characteristics, CT morphological features, and tumor-peritumoral radiomics features were extracted. Five machine learning algorithms were applied to construct and compare predictive models.
RESULTS: Lung lobe distribution, density, vacuolar sign, tumor-associated vessels, distance to pleura, and nodule diameters differed significantly between BA and LUAD. Among radiomics models, the tumor-peritumoral MLP model achieved the best performance (AUCs: 0.918, 0.912, 0.888). The clinical-radiomics fusion model outperformed single models, with AUCs of 0.935, 0.939, and 0.910 and accuracies of 0.862, 0.847, and 0.864 in the training, test, and validation sets, respectively.
CONCLUSION: The proposed fusion model enables accurate, non-invasive differentiation between BA and LUAD, offering valuable support for personalized clinical decision-making.