Volume 21 Issue 1
Jan.  2023
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Article Navigation >  Chinese Journal of General Practice  > 2023  >  21(1): 10-14
MENG Shan, HUI Dong-ming, WANG Kun, LI Zhi-chao. Value of analysis of coal workers' pneumoconiosis nodules based on radiomics combined with clinical factors in predicting the occurrence of lung cancer[J]. Chinese Journal of General Practice, 2023, 21(1): 10-14. doi: 10.16766/j.cnki.issn.1674-4152.002800
Citation: MENG Shan, HUI Dong-ming, WANG Kun, LI Zhi-chao. Value of analysis of coal workers' pneumoconiosis nodules based on radiomics combined with clinical factors in predicting the occurrence of lung cancer[J]. Chinese Journal of General Practice, 2023, 21(1): 10-14. doi: 10.16766/j.cnki.issn.1674-4152.002800
shu

Value of analysis of coal workers' pneumoconiosis nodules based on radiomics combined with clinical factors in predicting the occurrence of lung cancer

doi: 10.16766/j.cnki.issn.1674-4152.002800
  • 1.

    Department of Radiology, Chongqing Western Hospital, Chongqing 400050, China

Funds:

 2022MSXM140

  • Received Date: 2022-10-21
    Available Online: 2023-04-07
    Abstract
  •   Objective  To investigate the relationship between radiomics, clinical factors and malignant transformation of coal workers' pneumoconiosis nodules, establishing the best prediction model for malignant transformation of coal workers' pneumoconiosis nodules.  Methods  The clinical data of 425 cases of coal workers' pneumoconiosis treated in the Second People ' s Hospital of Jiulongpo district from January 2015 to June 2019 and CT imaging data of 628 pneumoconiosis nodules were collected. They were randomly divided into training set and verification cohorts at a ratio of 7 : 3. Each group of data was divided into malignant group and non-malignant group. The basic image features of the nodule were interpreted and the region of interest (ROI) was delineated. The Radscore calculation formula was constructed using the key features of radiomics. The predictive models were established base on clinical features, radiomics features, and clinical features combined with Radscore through logistic regression, and AUC and Delong were used to compare the prediction efficiency of models.  Results  During the 3-year follow-up, 54 malignant nodules were confirmed by pathology, with a diameter of (1.70±0.63) cm, 574 nodules without malignant change, with a diameter of (1.68±0.76) cm. There was no significant difference in the diameter between the two groups (t=0.468, P=0.642). Statistically significant differences were existed between the two groups in terms of length of service exposed to coal dust, family history, smoking history, drinking history and image features of spicule sign and air sign (all P < 0.05). There was no significant difference in age, tuberculosis history and chronic obstructive pulmonary disease (COPD) history (all P>0.05). LASSO screened 11 imaging features. The mixed model had the best effect in the validation set, with an AUC of 0.895. AUC of radiomics model and clinical model were 0.671 and 0.654 respectively. Delong test showed that the model difference was statistically significant (P < 0.05).  Conclusion  There is a certain correlation between the length of service exposed to coal dust, family history, smoking history, drinking history, burr sign, air sign, radiomics features and the malignant change of coal worker pneumoconiosis nodule, which can be used to build a model to predict the malignant change of coal worker pneumoconiosis nodule.

     

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