Volume 21 Issue 1
Jan.  2023
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Article Navigation >  Chinese Journal of General Practice  > 2023  >  21(1): 15-18
SUN Xin-ran, ZHAO Hong, ZHAO Da-hai, QI Wen, WU Jiang-fen. The value of establishing a model in the differentiation of solid pulmonary nodules based on CT radiomics[J]. Chinese Journal of General Practice, 2023, 21(1): 15-18. doi: 10.16766/j.cnki.issn.1674-4152.002801
Citation: SUN Xin-ran, ZHAO Hong, ZHAO Da-hai, QI Wen, WU Jiang-fen. The value of establishing a model in the differentiation of solid pulmonary nodules based on CT radiomics[J]. Chinese Journal of General Practice, 2023, 21(1): 15-18. doi: 10.16766/j.cnki.issn.1674-4152.002801
shu

The value of establishing a model in the differentiation of solid pulmonary nodules based on CT radiomics

doi: 10.16766/j.cnki.issn.1674-4152.002801
  • 1.

    Department of Radiology, the Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230601, China

Funds:

 2021zhyx-c67

 2021037

 2020LCZD12

  • Received Date: 2022-11-30
    Available Online: 2023-04-07
    Abstract
  •   Objective  To explore the diagnostic efficacy of CT radiomics model for benign and malignant solid pulmonary nodules.  Methods  CT imaging data of 231 cases of solid pulmonary nodules confirmed by surgery, puncture or clinical diagnosis in our hospital from March 2019 to November 2022 were retrospectively analyzed, select 231 typical pulmonary nodules, and they were divided into benign group (98 cases) and malignant group (133 cases) according to pathological types. Infer Scholar software was used to outline the lesion contour from two and three dimensions respectively. The radiomics features were extracted by software, and the enrolled cases were divided into the training set and the test set in a ratio of 7∶ 3. Features were screened by Pearson correlation coefficient, significance test and LASSO regression analysis. Two dimensional and three dimensional radiomics feature models (model Ⅰ and model Ⅱ) were constructed respectively in the training set, and verified by test set. The area under receiver operating characteristic (ROC) curve (AUC) was used to evaluate the predictive efficiency of the model.  Results  Total 919 and 1 746 radiomics features were extracted from two dimensions and three dimensions respectively. After screening, 12 and 20 optimal radiomics features were obtained, which were used to construct radiomics model Ⅰ and Ⅱ by machine algorithms. The AUC value of model Ⅰ in the training set was 0.97. The AUC value of model Ⅱ in the training set was 0.98. The AUC value, sensitivity, specificity, positive predictive value, negative predictive value and accuracy of model Ⅰ in the test set were 0.94 (95% CI: 0.87-0.98), 83.9%, 89.5%, 86.7%, 87.2%, 87.0%, respectively. The AUC value, sensitivity, specificity, positive predictive value, negative predictive value and accuracy of model Ⅱ were 0.97 (95% CI: 0.94-0.99), 75.9%, 97.5%, 95.7%, 84.8%, 88.4%, respectively.  Conclusion  The model based on CT radiomics features can well predict the benign and malignant of solid pulmonary nodules. The diagnostic efficacy of model Ⅱ constructed from three-dimensional perspective is better than that of model Ⅰ constructed from two-dimensional perspective.

     

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