Statistical methods for evaluating the fine needle aspiration cytology procedure in breast cancer diagnosis

Background Statistical issues present while evaluating a diagnostic procedure for breast cancer are non rare but often ignored, leading to biased results. We aimed to evaluate the diagnostic accuracy of the fine needle aspiration cytology(FNAC), a minimally invasive and rapid technique potentially used as a rule-in or rule-out test, handling its statistical issues: suspect test results and verification bias. Methods We applied different statistical methods to handle suspect results by defining conditional estimates. When considering a partial verification bias, Begg and Greenes method and multivariate imputation by chained equations were applied, however, and a Bayesian approach with respect to each gold standard was used when considering a differential verification bias. At last, we extended the Begg and Greenes method to be applied conditionally on the suspect results. Results The specificity of the FNAC test above 94%, was always higher than its sensitivity regardless of the proposed method. All positive likelihood ratios were higher than 10, with variations among methods. The positive and negative yields were high, defining precise discriminating properties of the test. Conclusion The FNAC test is more likely to be used as a rule-in test for diagnosing breast cancer. Our results contributed in advancing our knowledge regarding the performance of FNAC test and the methods to be applied for its evaluation.

Automated summary

Statistical issues in evaluating the diagnostic accuracy of fine needle aspiration cytology for breast cancer, such as suspect test results and verification bias, were addressed using various methods including conditional estimates, Begg and Greenes method, multivariate imputation by chained equations, and a Bayesian approach. The FNAC test demonstrated high specificity, always above 94%, and performed better as a rule-in test for breast cancer diagnosis.