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Updated: Mar 21, 2026
Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
Published on: May 19, 2023
[Establishment of a predicting model to evaluate the probability of malignancy or benign in patients with solid
Yangwei Xiang1, Yifeng Sun, Wen Gao
1Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200030, China.
Objective:
To develop a predicting model for evaluating the probability of malignancy or benign in patients with solid solitary pulmonary nodules through analyzing the clinical, radiologic, laboratory examination and radionuclide (18)F-Fluorodeoxyglucose examinations data.
Methods:
The data of the 203 patients(110 males and 93 females) with solid SPN who underwent surgical resection with definite postoperative pathological diagnosis from January 2012 to December 2014 in Shanghai Chest Hospital (group A)were retrospectively analyzed. The clinical data included age, gender, history of smoking, history of tumor; radiologic data included diameter in lung window, location, shape, clear border, lobulation, spiculation, vascular convergence, tumor cycle blood vessel, density, calcification, pleura indentation; laboratory examination included five serum tumor markers consisting of CA125, CEA, CYFRAL21-1, NSE, SCC. (18)F-Fluorodeoxyglucose examinations included (18)F-FDG PET-CT or SPECT. The independent predictors of malignancy were estimated through univariate and multivariate analysis, then the predicting model was built. Another 110 patients with solid SPN(group B)from January 2015 to December 2015 with definite pathological diagnosis were used to validate the predictive value of the model.
Results:
There were 159(78.3%) cases of malignancy and 44(21.7%) cases of benign in group A. Logistic regression analysis showed age, clear border, spiculation, calcification and (18)F-FDG examination were independent predictors of malignancy in patients with solid SPN(P<0.05). A predicting nomogram was built according to the result of the multivariate logistic regression analysis. The area under the ROC curve was 0.890±0.038 for group B. The cut off value was 0.708. The sensitivity in group B was 86%, specificity 80%, accuracy 84.5%.
Conclusion:
Age of patients, clear border, spiculation, calcification and (18)F-FDG examination were independent predictors of malignancy in patients with solid SPN. The model showed good diagnosis efficiency in external validation, and could be applied to make decision for patients with solid SPN.
