Tuberculosis and Respiratory Diseases

The Korean Academy of Tuberculosis and Respiratory Diseases (대한결핵및호흡기학회)
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A Study to Validate the Pretest Probability of Malignancy in Solitary Pulmonary Nodule

사전검사를 통한 고립성 폐결절 환자에서의 악성 확률 타당성에 대한 연구

  • Jang, Joo Hyun (Department of Internal Medicine, Hallym University College of Medicine) ;
  • Park, Sung Hoon (Department of Internal Medicine, Hallym University College of Medicine) ;
  • Choi, Jeong Hee (Department of Internal Medicine, Hallym University College of Medicine) ;
  • Lee, Chang Youl (Department of Internal Medicine, Hallym University College of Medicine) ;
  • Hwang, Yong Il (Department of Internal Medicine, Hallym University College of Medicine) ;
  • Shin, Tae Rim (Department of Internal Medicine, Hallym University College of Medicine) ;
  • Park, Yong Bum (Department of Internal Medicine, Hallym University College of Medicine) ;
  • Lee, Jae Young (Department of Internal Medicine, Hallym University College of Medicine) ;
  • Jang, Seung Hun (Department of Internal Medicine, Hallym University College of Medicine) ;
  • Kim, Cheol Hong (Department of Internal Medicine, Hallym University College of Medicine) ;
  • Park, Sang Myeon (Department of Internal Medicine, Hallym University College of Medicine) ;
  • Kim, Dong Gyu (Department of Internal Medicine, Hallym University College of Medicine) ;
  • Lee, Myung Goo (Department of Internal Medicine, Hallym University College of Medicine) ;
  • Hyun, In Gyu (Department of Internal Medicine, Hallym University College of Medicine) ;
  • Jung, Ki Suck (Department of Internal Medicine, Hallym University College of Medicine)
  • 장주현 (한림대학교 의과대학 내과학교실) ;
  • 박성훈 (한림대학교 의과대학 내과학교실) ;
  • 최정희 (한림대학교 의과대학 내과학교실) ;
  • 이창률 (한림대학교 의과대학 내과학교실) ;
  • 황용일 (한림대학교 의과대학 내과학교실) ;
  • 신태림 (한림대학교 의과대학 내과학교실) ;
  • 박용범 (한림대학교 의과대학 내과학교실) ;
  • 이재영 (한림대학교 의과대학 내과학교실) ;
  • 장승훈 (한림대학교 의과대학 내과학교실) ;
  • 김철홍 (한림대학교 의과대학 내과학교실) ;
  • 박상면 (한림대학교 의과대학 내과학교실) ;
  • 김동규 (한림대학교 의과대학 내과학교실) ;
  • 이명구 (한림대학교 의과대학 내과학교실) ;
  • 현인규 (한림대학교 의과대학 내과학교실) ;
  • 정기석 (한림대학교 의과대학 내과학교실)
  • Received : 2009.06.13
  • Accepted : 2009.07.13
  • Published : 2009.08.30
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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.

Keywords

References

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