A Study to Validate the Pretest Probability of Malignancy in Solitary Pulmonary Nodule.
10.4046/trd.2009.67.2.105
- Author:
Joo Hyun JANG
1
;
Sung Hoon PARK
;
Jeong Hee CHOI
;
Chang Youl LEE
;
Yong Il HWANG
;
Tae Rim SHIN
;
Yong Bum PARK
;
Jae Young LEE
;
Seung Hun JANG
;
Cheol Hong KIM
;
Sang Myeon PARK
;
Dong Gyu KIM
;
Myung Goo LEE
;
In Gyu HYUN
;
Ki Suck JUNG
Author Information
1. Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Korea. thoraxmd@hallym.or.kr
- Publication Type:Original Article
- Keywords:
Solitary pulmonary nodule;
Pretest probability of malignancy;
Bayesian analysis;
Multiple logistic regression analysis
- MeSH:
Bayes Theorem;
Biopsy;
Humans;
Judgment;
Logistic Models;
Retrospective Studies;
Solitary Pulmonary Nodule;
Thorax
- From:Tuberculosis and Respiratory Diseases
2009;67(2):105-112
- CountryRepublic of Korea
- Language:Korean
-
Abstract:
BACKGROUND: Solitary pulmonary nodules (SPN) are encountered incidentally in 0.2% of patients who undergo chest X-ray or chest CT. Although SPN has malignant potential, it cannot be treated surgically by biopsy in all patients. The first stage is to determine if patients with SPN require periodic observation and biopsy or resection. An important early step in the management of patients with SPN is to estimate the clinical pretest probability of a malignancy. In every patient with SPN, it is recommended that clinicians estimate the pretest probability of a malignancy either qualitatively using clinical judgment or quantitatively using a validated model. This study examined whether Bayesian analysis or multiple logistic regression analysis is more predictive of the probability of a malignancy in SPN. METHODS: From January 2005 to December 2008, this study enrolled 63 participants with SPN at the Kangnam Sacred Hospital. The accuracy of Bayesian analysis and Bayesian analysis with a FDG-PET scan, and Multiple logistic regression analysis was compared retrospectively. The accurate probability of a malignancy in a patient was compared by taking the chest CT and pathology of SPN patients with <30 mm at CXR incidentally. RESULTS: From those participated in study, 27 people (42.9%) were classified as having a malignancy, and 36 people were benign. The result of the malignant estimation by Bayesian analysis was 0.779 (95% confidence interval [CI], 0.657 to 0.874). Using Multiple logistic regression analysis, the result was 0.684 (95% CI, 0.555 to 0.796). This suggests that Bayesian analysis provides a more accurate examination than multiple logistic regression analysis. CONCLUSION: Bayesian analysis is better than multiple logistic regression analysis in predicting the probability of a malignancy in solitary pulmonary nodules but the difference was not statistically significant.
