ArticleBMC medical research methodology2022
Statistical methods for evaluating the fine needle aspiration cytology procedure in breast cancer diagnosis.
Article in BMC medical research methodology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 15 citations in OpenAlex.
- Partial verification bias correction using scaled inverse probability resampling for binary diagnostic tests.PloS one · 2025Article
- Breast fine-needle aspiration cytology in the era of core-needle biopsy: what is its role?Journal of pathology and translational medicine · 2025Review
- Article
- Generative adversarial network: a statistical-based deep learning paradigm to improve detecting breast cancer in thermograms.Medical & biological engineering & computing · 2024Article
- Missing values and inconclusive results in diagnostic studies - A scoping review of methods.Statistical methods in medical research · 2023Article
- Breast biopsy techniques in a global setting-clinical practice review.Translational breast cancer research : a journal focusing on translational research in breast cancer · 2023Review
- Partial Verification Bias Correction Using Inverse Probability Bootstrap Sampling for Binary Diagnostic Tests.Diagnostics (Basel, Switzerland) · 2022Article
- A New method for promote the performance of deep learning paradigm in diagnosing breast cancer: improving role of fusing multiple views of thermography images.Health and technology · 2022Article
- PSOWNNs-CNN: A Computational Radiology for Breast Cancer Diagnosis Improvement Based on Image Processing Using Machine Learning Methods.Computational intelligence and neuroscience · 2022Article
- Minimally Invasive Cytopathology and Accurate Diagnosis: Technical Procedures and Ancillary Techniques.In vivo (Athens, Greece)Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundStatistical 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.
methodsWe 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.
resultsThe 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.
conclusionThe 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.