ArticleBMC pulmonary medicine2025
Prediction of the Ki-67 proliferation index in lung adenocarcinoma using an interpretable CT-based deep learning radiomics model: a two-center study.
Article in BMC pulmonary medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Conventional chest CT features are associated with Ki-67 labeling index in lung adenocarcinoma: a single-center retrospective radiopathologic study.Journal of thoracic disease · 2026Article
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7 authors.
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Abstract
purposeTo develop an interpretable computed tomography (CT)-based deep learning radiomics (DLR) model that integrates deep learning (DL) and radiomics features to predict the preoperative Ki-67 proliferation index in lung adenocarcinoma.
methodsA retrospective investigation involved 136 patients with lung adenocarcinoma who had undergone surgical resection between 2020 and 2024. Ki-67 expression was categorized as negative when ≤ 5%, and positive when > 5%. The training set comprised 91 patients, and the external test set contained 45 patients. Preoperative CT images were analyzed to derive 794 radiomics features, alongside 120 DL features obtained from the ResNet50 model. Model development was carried out by integrating DLR features through the application of ten distinct machine learning classifiers. The model’s diagnostic performance was evaluated based on the area under the curve (AUC). The interpretability was further examined using the Shapley additive explanations (SHAP) technique.
resultsAmong the classifiers evaluated, the logistic regression (LR) model that integrates both DL and radiomics features exhibited the superior predictive performance, achieving an AUC of 0.904 in the training set, and the external test set AUC was 0.865, respectively. This result surpassed both traditional radiomics-based models (AUCs of 0.847 and 0.821 were observed for the training set and external test set, respectively) and DL models alone (AUCs of 0.802 and 0.733 were observed for the training set and external test set, respectively). Additionally, SHAP analysis demonstrated the two radiomics features with the greatest influence on model predictions.
conclusionThe CT-based DLR model demonstrates strong predictive capabilities for distinguishing Ki-67 expression status in lung adenocarcinoma, indicating its capacity as an interpretable and robust tool for preoperative evaluation.
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