Volume 21 Issue 1
Jan.  2023
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Article Navigation >  Chinese Journal of General Practice  > 2023  >  21(1): 6-9
ZHANG Jun-jie, HAO Li-gang, XU Qian, FENG Hui, ZHANG Ning, SHI Gao-feng. CT-derived model for the diagnosis of pulmonary invasive mucinous adenocarcinoma by machine learning[J]. Chinese Journal of General Practice, 2023, 21(1): 6-9. doi: 10.16766/j.cnki.issn.1674-4152.002799
Citation: ZHANG Jun-jie, HAO Li-gang, XU Qian, FENG Hui, ZHANG Ning, SHI Gao-feng. CT-derived model for the diagnosis of pulmonary invasive mucinous adenocarcinoma by machine learning[J]. Chinese Journal of General Practice, 2023, 21(1): 6-9. doi: 10.16766/j.cnki.issn.1674-4152.002799
shu

CT-derived model for the diagnosis of pulmonary invasive mucinous adenocarcinoma by machine learning

doi: 10.16766/j.cnki.issn.1674-4152.002799
  • 1.

    Department of Computed Tomography and Magnetic Resonance, Hebei Medical University Fourth Affiliated Hospital, Shijiazhuang, Hebei 050011, China

Funds:

 2018YFC0116404

 ZC20301-健康医疗领域

  • Received Date: 2022-10-11
    Available Online: 2023-04-07
    Abstract
  •   Objective  Lung mucinous adenocarcinoma is a rare subtype of lung cancer with unique molecular biology characteristics. It influences the choice of treatment options. We explore a machine learning model based on clinical and CT features in the diagnosis of lung invasive mucinous adenocarcinoma, propose to improve the diagnostic accuracy of pre-treatment mucinous adenocarcinoma.  Methods  A retrospective analysis of 620 cases with pulmonary invasive adenocarcinoma confirmed by needle biopsy or surgical pathology in the Fourth Hospital of Hebei Medical University from January 2017 to May 2022 was performed. After matching by using the propensity score matching (PSM) with a matching ratio 1 : 1, the patients were randomly divided into the training set and the test set based on the 7 : 3 ratio. Three machine learning models, namely, support vector machine (SVM), random forest (RF) and logistic regression (LR), were constructed using the variables with statistical differences, and the optimal model was selected by AUC values. The AUC value of the optimal machine learning model was analysed by 5-fold cross-validation method, the DCA curve was drawn to evaluate the diagnostic efficiency of the constructed model, and a Nomogram is constructed.  Results  Analysis showed that lesion location in the lower lobe, cystic lumen, bronchial truncation and ΔCTV value were independent predictive factors for invasive mucinous adenocarcinoma. The 4 above mentioned features were constructed by machine learning, and the prediction model was compared. Finally, the logistic regression model (AUC=0.801) was shown to be the optimal model. 30% of 285 cases were randomly selected as the test set (n=85 cases), and the remaining samples were used as the training set for 5-fold cross-validation. The logistic regression model obtained AUC of 0.777 in the validation set, AUC of 0.785 in the test set, accuracy of 0.682, AUC of 0.803 in the training set and accuracy of 0.749. Finally, the Nomogram of the logistic regression model was constructed. The Briser Score in the calibration curve of the model was 0.149, and the DCA curve also showed that the model had good predictive ability and stability in potential clinic application.  Conclusion  By using machine learning models based on clinical and CT features, a clinical prediction model for primary pulmonary invasive mucinous adenocarcinoma was constructed, which has a potential role in guiding clinical diagnosis.

     

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