CAO Lan, TAO Yujian, ZHAO Yuanlu, ZHANG Pan. Analysis in risk factors of solitary pulmonary nodules[J]. Journal of Clinical Medicine in Practice, 2021, 25(1): 38-40, 44. DOI: 10.7619/jcmp.20200457
Citation: CAO Lan, TAO Yujian, ZHAO Yuanlu, ZHANG Pan. Analysis in risk factors of solitary pulmonary nodules[J]. Journal of Clinical Medicine in Practice, 2021, 25(1): 38-40, 44. DOI: 10.7619/jcmp.20200457

Analysis in risk factors of solitary pulmonary nodules

  •   Objective  To analyze the risk factors of solitary pulmonary nodules(SPN).
      Methods  A total of 127 patients with SPN who had determined the pathologic types and underwent thoracic surgeries in Yangzhou University Affiliated Hospital were collected as study objects, and a total of 80 patients were included after exclusion of those with pure ground-grass opacity(pGGO). The clinical data such as gender, age, smoking history, past cancer history, carcinoembryonic antigen(CEA), cytokeratin 19 fragment(CYFRA21-1), nodule diameter, location, burr sign, lobulation, pleural indentation sign, vascular convergence sign, calcification and vacuole sign were retrospectively analyzed. Taking pathological examination results as gold standard, these patients were divided into benign group and malignant group. Univariate analysis was used to explore the influencing factors of SPN, and Logistic multivariate analysis was used to determine its independent risk factors.
      Results  There were significant differences in age, past cancer history, CEA and CYFRA21-1 levels, nodule diameter, burr sign, lobulation between the two groups univariate analysis (P < 0.05). Logistic regression analysis showed that past cancer history, CEA as well as CYFRA21-1 levels, and lobulation were independent risk factors for malignat SPN. The sensitivity and specificity of predictive model for benign or malignant SPN were 70.90% and 92.00%, respectively.
      Conclusion  Past cancer history, CEA as well as CYFRA21-1 levels and lobulation are independent risk factors for malignat SPN and the prediction model has a certain clinical value.
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