Risk assessment of malignancy in solitary pulmonary nodules in lung computed tomography: a multivariable predictive model study
| Autor: | Hai-Yang Liu, Xing-Ru Zhao, Meng Chi, Xiang-Song Cheng, Zi-Qi Wang, Zhi-Wei Xu, Yong-Li Li, Rui Yang, Yong-Jun Wu, Xiao-Ju Zhang, Pei-Fang Wei |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
Lung Neoplasms Malignancy Logistic regression Risk Assessment Regression algorithm Prediction model medicine Humans Lung cancer Lung Multiple Pulmonary Nodules Receiver operating characteristic business.industry Solitary Pulmonary Nodule Original Articles General Medicine Nomogram medicine.disease Confidence interval CT image Medicine Radiology Risk assessment business Tomography X-Ray Computed Pulmonary nodules |
| Zdroj: | Chinese Medical Journal, Vol 134, Iss 14, Pp 1687-1694 (2021) Chinese Medical Journal |
| ISSN: | 2542-5641 0366-6999 |
| DOI: | 10.1097/CM9.0000000000001507 |
| Popis: | Background:. Computed tomography images are easy to misjudge because of their complexity, especially images of solitary pulmonary nodules, of which diagnosis as benign or malignant is extremely important in lung cancer treatment. Therefore, there is an urgent need for a more effective strategy in lung cancer diagnosis. In our study, we aimed to externally validate and revise the Mayo model, and a new model was established. Methods:. A total of 1450 patients from three centers with solitary pulmonary nodules who underwent surgery were included in the study and were divided into training, internal validation, and external validation sets (n = 849, 365, and 236, respectively). External verification and recalibration of the Mayo model and establishment of new logistic regression model were performed on the training set. Overall performance of each model was evaluated using area under receiver operating characteristic curve (AUC). Finally, the model validation was completed on the validation data set. Results:. The AUC of the Mayo model on the training set was 0.653 (95% confidence interval [CI]: 0.613–0.694). After re-estimation of the coefficients of all covariates included in the original Mayo model, the revised Mayo model achieved an AUC of 0.671 (95% CI: 0.635–0.706). We then developed a new model that achieved a higher AUC of 0.891 (95% CI: 0.865–0.917). It had an AUC of 0.888 (95% CI: 0.842–0.934) on the internal validation set, which was significantly higher than that of the revised Mayo model (AUC: 0.577, 95% CI: 0.509–0.646) and the Mayo model (AUC: 0.609, 95% CI, 0.544–0.675) (P |
| Databáze: | OpenAIRE |
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