Use of Bayesian Modeling to Estimate the Sensitivity of Stereotactic Directional Vacuum-Assisted Breast Biopsy When the Gold Standard is Incomplete

Wang, Jane; Kuo, Wen-Hung; Pan, Shin-Liang; Chang, King-Jen; Teng, Yu-Chuan; Shih, Tiffany Ting-Fang

doi:10.1016/j.acra.2009.05.010

« Previous Next »Academic Radiology
Volume 16, Issue 11 , Pages 1316-1322, November 2009

Use of Bayesian Modeling to Estimate the Sensitivity of Stereotactic Directional Vacuum-Assisted Breast Biopsy When the Gold Standard is Incomplete

Jane Wang, MD
- Department of Medical Imaging, National Taiwan University Hospital, Taipei, Taiwan
- Department of Radiology, College of Medicine, National Taiwan University, 7 Chung-Shan South Road, Taipei 100, Taiwan
Wen-Hung Kuo, MD, PhD
- Department of Surgery, National Taiwan University Hospital, Taipei, Taiwan
Shin-Liang Pan, MD, PhD
- Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, Taipei, Taiwan
King-Jen Chang, MD, PhD
- Department of Surgery, National Taiwan University Hospital, Taipei, Taiwan
- Department of Surgery, College of Medicine, National Taiwan University, 7 Chung-Shan South Road, Taipei 100, Taiwan
Yu-Chuan Teng, MD
- Department of Medical Imaging, National Taiwan University Hospital, Taipei, Taiwan
Tiffany Ting-Fang Shih, MD
- Department of Medical Imaging, National Taiwan University Hospital, Taipei, Taiwan
- Department of Radiology, College of Medicine, National Taiwan University, 7 Chung-Shan South Road, Taipei 100, Taiwan
- Address correspondence to: T.T.-F.S.

Received 2 March 2009; accepted 14 May 2009. published online 10 August 2009.

Rationale and Objectives

This study is conducted to estimate the sensitivity of stereotactic directional vacuum-assisted breast biopsy (ST DVAB) using Bayesian modeling and to predict how many more cancers can be inferred from those lesions without surgical correlation.

Materials and methods

We retrospectively reviewed the 103 lesions from 84 women who underwent ST DVAB. The study was approved by the Institutional Review Board of our hospital. We estimated the sensitivity and prevalence of the study population for ST DVAB by two types of approaches: for the type I approach, the gold standards were surgical correlation or postbiopsy mammographic follow-up. For the type II approach using Bayesian modeling by a beta-binomial model, the only gold standard was surgical correlation and the predicted number of cancerous lesions in those patients without surgical correlation was estimated.

Results

For the type I approach, the sensitivity was 92.3%, and the prevalence 12.6%. For the type II approach, the mean sensitivity of ST DVAB was 89%, and the mean prevalence was 15%. We predicted that an average of 1.7 cancerous lesions occurred among those lesions without surgical correlation by the Bayesian estimation.

Conclusions

The mean sensitivity of ST DVAB using the Bayesian (type II) approach was lower than that using the type I approach, because we regarded the surgery as the only gold standard in Bayesian modeling and the nonoperated lesions were thought to be with unknown true disease status. The Bayesian approach is thus more appropriate to use than the type I approach when the gold standard is incomplete.

Key Words: Breast, biopsy, breast neoplasm, diagnosis, sensitivity, bayesian modeling