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Clin Lung Cancer. 2011 Sep;12(5):313-9. doi: 10.1016/j.cllc.2011.06.005.

Development and validation of a clinical prediction model to estimate the probability of malignancy in solitary pulmonary nodules in Chinese people.

Author information

1
Department of Thoracic Surgery, People's Hospital, Peking University, Beijing, People's Republic of China.

Abstract

INTRODUCTION:

This study evaluated the clinical factors affecting the probability of malignancy of solitary pulmonary nodules (SPNs) using multivariate logistic regression analysis. A clinical prediction model was subsequently developed to estimate the probability of malignancy. This model was then validated.

METHODS:

Medical records from 371 patients (197 men, 174 women) with a pathologic diagnosis of SPN made between January 2000 and September 2009, were reviewed. Clinical data were collected to estimate the independent predictors of malignancy of SPN with multivariate analysis. A clinical prediction model was subsequently created. Between October 2009 and March 2010, data from an additional 62 patients with a pathologic diagnosis of SPN were used to validate this clinical prediction model. The model was also compared with two previously described models.

RESULTS:

Median patient age was 57.1 years old. Fifty-three percent of the nodules were malignant and 46% were benign. Logistic regression analysis identified six clinical characteristics (age, diameter, border, calcification, spiculation, and family history of tumor) as independent predictors of malignancy in patients with SPN. The area under the receiver operating characteristic (ROC) curve for our model (0.89; 50% confidence interval [CI], 0.78-0.99) was higher than those generated using another two reported models. In our model, sensitivity was 92.5%, specificity was 81.8%,positive predictive value was 90.2%, and negative predictive value was 85.7%).

CONCLUSIONS:

Age of the patient, diameter, border, calcification, spiculation, and family history of tumors were independent predictors of malignancy in patients with SPN. Our prediction model was more accurate than the two existing models and was sufficient to estimate malignancy in patients with SPN.

PMID:
21889113
DOI:
10.1016/j.cllc.2011.06.005
[Indexed for MEDLINE]

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