Value of a Combined Model Integrating Clinical Data，CT Features，and Radiomics in Predicting Ki-67 Expression Levels in Stage IA Lung Adenocarcinoma

Yang CHEN; Jianning LIU; Zidong ZHANG; Dongmei PAN; Zhengteng LI

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Yang CHEN, Jianning LIU, Zidong ZHANG, Dongmei PAN, Zhengteng LI. Value of a Combined Model Integrating Clinical Data，CT Features，and Radiomics in Predicting Ki-67 Expression Levels in Stage IA Lung Adenocarcinoma[J]. Journal of Kunming Medical University.

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Value of a Combined Model Integrating Clinical Data，CT Features，and Radiomics in Predicting Ki-67 Expression Levels in Stage IA Lung Adenocarcinoma

1.
Department of Radiology，Yantai Yeda Hospital，Yantai Shandong 2640002
2.
Department of Radiology，Jining No.1 People’s Hospital，Jining Shandong 272000 ，China

Received Date: 2026-01-24

Abstract

Abstract

Objective To explore and construct a combined prediction model integrating clinical, CT features and radiomics for preoperative prediction of Ki-67 expression levels in stage IA lung adenocarcinoma, and to assess its diagnostic efficacy; Methods A total of 193 patients with surgically and pathologically confirmed stage IA lung adenocarcinoma from Jining First People's Hospital （ January 2016 to January 2024） were retrospectively selected as the training set, and 73 patients from Yantai Yeda Hospital （May 2019 to February 2024） served as the validation set. Based on the Ki-67 index, patients were divided into high expression （Ki-67 ≥ 15%） and low expression （Ki-67<15%） groups. Tumor region of interest （ROI） were delineated with the ITK-SNAP; Radiomics features were extracted with Python 3.7.3 and R Studio 4.1.2; Univariate and multivariate logistic regression were used to analyze the clinical and CT features in both sets. Variance threshold method and LASSO regression were used to screen the radiomics features, and the predictive models were constructed through logistic regression. The risk factors of clinical and CT features are combined with radiomics features to construct a joint model and generate a nomogram. Model predictive performance was evaluated by the receiver operating characteristic curve （ROC）, calibration curve and decision curve analysis. Result The patient's gender, age, lesion characteristics, lobulation sign, and mean lesion diameter were identified as independent risk factors for elevated Ki-67 expression （P < 0.05）. The area under the curve （AUC） for the clinical model is 0.775 in the training set and 0.703 in the validation set. The AUC for the CT feature model is 0.762 in the training set and 0.747 in the validation set. The AUC for the radiomics model was 0.835 in the training set and 0.811 in the validation set, indicating a relatively high predictive efficacy. The combined model, which integrated clinical and CT feature risk factors with radiomics, achieved AUCs of 0.892 and 0.866 in the training and validation sets, respectively, both surpassing the performance of the radiomics model alone.The Delong test also demonstrated statistical significance （P < 0.05）. Conclusion A model based on clinical, CT features, and radiomics allows for the preoperative prediction of Ki-67 expression levels in stage IA lung adenocarcinoma, and the combined model can enhance predictive accuracy.
- Lung adenocarcinoma,
- Ki-67,
- X-ray computer tomography,
- Radiomics,
- Prediction

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References(30)

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