Construction and Verification of An Integrated Traditional Chinese and Western Medicine Model for Predicting Malignant Risk of Pulmonary Nodules
10.13422/j.cnki.syfjx.20242496
- VernacularTitle:肺结节中西医结合恶性风险预测模型的建立与验证
- Author:
Qian YANG
1
;
Jingmin XIAO
1
;
Yuanbing CHEN
1
;
Lei WU
1
Author Information
1. State Key Laboratory of Traditional Chinese Medicine Syndrome，The Second Clinical Medical College of Guangzhou University of Chinese Medicine，Guangzhou 510120，China
- Publication Type:Journal Article
- Keywords:
pulmonary nodule;
integrated traditional Chinese and Western medicine;
prediction model;
risk factor;
nomogram
- From:
Chinese Journal of Experimental Traditional Medical Formulae
2025;31(2):129-139
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveThis study explored the risk factors for malignant risks of pulmonary nodules based on clinical data，constructed an integrated traditional Chinese and Western medicine model for predicting malignant risks of pulmonary nodules， and visualized the prediction results by using a nomogram. MethodsBased on a cross-sectional survey study design，patients with pulmonary nodules who were hospitalized in the Department of Respiratory and Cardiothoracic Surgery of Guangdong Provincial Hospital of Traditional Chinese Medicine from April 2023 to January 2024 were included. The dataset was randomly divided into a training set and a validation set according to 7∶3. In the training set，predictive factors were selected through univariate Logistic regression analysis and Least Absolute Shrinkage and Selection Operator （LASSO） regression analysis，and Logistic regression models were built. The discriminative ability，calibration，and clinical decision-making curves of the Western medicine model and the integrated traditional Chinese and Western medicine prediction model were compared to select the optimal model，which was then visualized in a nomogram. ResultsThis study included a total of 366 patients，and they were divided into a training set （258 cases） and a validation set （108 cases）. Seven predictive factors were considered including age，preference for fatty and greasy foods，history of environmental or occupational exposure，Qi deficiency，Yang deficiency，nodule density，and nodule diameter. A Logistic regression model was constructed. A Western medicine model，defined as model1，was created using only age，history of environmental or occupational exposure，nodule density，and nodule diameter as predictive factors. In addition，an integrated traditional Chinese and Western medicine model，defined as model2，was created by adding preference for fatty and greasy foods， Qi deficiency，and Yang deficiency as predictive factors. Model2 demonstrated better predictive performance in both the training and validation sets. Its accuracy in training set was 0.740，with precision of 0.825， recall of 0.829， F1 score of 0.827， the area under the curve （AUC）of 0.865 （95% confidence interval （CI）：0.815-0.915）， and a Brier score of 0.122. The accuracy in validation set was 0.731， with precision of 0.776， recall of 0.831， F1 score of 0.803， AUC of 0.852 （95%CI：0.776-0.927）， and a Brier score of 0.149. The calibration curve and decision-making curve analysis showed that this model exhibited good consistency and clinical utility in prediction. The equation for the malignant probability of pulmonary nodules was defined as p=eY/(1+eY)
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，where Y=-4.187+0.022×（age）+1.079×（history of environmental or occupational exposure）+0.718×（preference for fatty and greasy foods）+ 1.403×（Qi deficiency）+0.732×（Yang deficiency）+0.349×（pure ground glass density nodules）+1.304×（partially solid density nodules）+0.148×（nodule diameter）. A nomogram was used for intuitive representation. ConclusionThis study develops a comprehensive assessment model for the malignant risk of pulmonary nodules， which integrates factors from traditional Chinese medicine and Western medicine. Embedding traditional Chinese medicine elements in the prediction model allows for a multidimensional assessment of relevant risk factors for nodule malignancy. This demonstrates good diagnostic performance and provides a reference for early identification of lung cancer.
