Literature DB >> 16216029

Clinical impact and frequency of anatomic pathology errors in cancer diagnoses.

Stephen S Raab1, Dana Marie Grzybicki, Janine E Janosky, Richard J Zarbo, Frederick A Meier, Chris Jensen, Stanley J Geyer.   

Abstract

BACKGROUND: To the authors' knowledge, the frequency and clinical impact of errors in the anatomic pathology diagnosis of cancer have been poorly characterized to date.
METHODS: The authors examined errors in patients who underwent anatomic pathology tests to determine the presence or absence of cancer or precancerous lesions in four hospitals. They analyzed 1 year of retrospective errors detected through a standardized cytologic-histologic correlation process (in which patient same-site cytologic and histologic specimens were compared). Medical record reviews were performed to determine patient outcomes. The authors also measured the institutional frequency, cause (i.e., pathologist interpretation or sampling), and clinical impact of diagnostic cancer errors.
RESULTS: The frequency of errors in cancer diagnosis was found to be dependent on the institution (P < 0.001) and ranged from 1.79-9.42% and from 4.87-11.8% of all correlated gynecologic and nongynecologic cases, respectively. A statistically significant association was found between institution and error cause (P < 0.001); the cause of errors resulting from pathologic misinterpretation ranged from 5.0-50.7% (the remainder were due to clinical sampling). A statistically significant association was found between institution and assignment of the clinical impact of error (P < 0.001); the aggregated data demonstrated that for gynecologic and nongynecologic errors, 45% and 39%, respectively, were associated with harm. The pairwise kappa statistic for interobserver agreement on cause of error ranged from 0.118-0.737.
CONCLUSIONS: Errors in cancer diagnosis are reported to occur in up to 11.8% of all reviewed cytologic-histologic specimen pairs. To the authors' knowledge, little agreement exists regarding whether pathology errors are secondary to misinterpretation or poor clinical sampling of tissues and whether pathology errors result in serious harm. Copyright 2005 American Cancer Society

Entities:  

Mesh:

Year:  2005        PMID: 16216029     DOI: 10.1002/cncr.21431

Source DB:  PubMed          Journal:  Cancer        ISSN: 0008-543X            Impact factor:   6.860


  23 in total

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2.  Multicenter Assessment of Gram Stain Error Rates.

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Journal:  J Clin Microbiol       Date:  2016-02-17       Impact factor: 5.948

3.  Use of radiomics based on 18F-FDG PET/CT and machine learning methods to aid clinical decision-making in the classification of solitary pulmonary lesions: an innovative approach.

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4.  The diagnostic "gold standard" in oncology: increasing importance and increasing concerns.

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Journal:  Curr Oncol Rep       Date:  2006-07       Impact factor: 5.075

5.  Evaluation of computer-aided detection and diagnosis systems.

Authors:  Nicholas Petrick; Berkman Sahiner; Samuel G Armato; Alberto Bert; Loredana Correale; Silvia Delsanto; Matthew T Freedman; David Fryd; David Gur; Lubomir Hadjiiski; Zhimin Huo; Yulei Jiang; Lia Morra; Sophie Paquerault; Vikas Raykar; Frank Samuelson; Ronald M Summers; Georgia Tourassi; Hiroyuki Yoshida; Bin Zheng; Chuan Zhou; Heang-Ping Chan
Journal:  Med Phys       Date:  2013-08       Impact factor: 4.071

6.  Advancing Research on Medical Image Perception by Strengthening Multidisciplinary Collaboration.

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Journal:  JNCI Cancer Spectr       Date:  2022-01-05

7.  Diagnostic accuracy of MRI texture analysis for grading gliomas.

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8.  Association of Omics Features with Histopathology Patterns in Lung Adenocarcinoma.

Authors:  Kun-Hsing Yu; Gerald J Berry; Daniel L Rubin; Christopher Ré; Russ B Altman; Michael Snyder
Journal:  Cell Syst       Date:  2017-11-15       Impact factor: 10.304

9.  High-throughput profiling of tissue and tissue model microarrays: Combined transmitted light and 3-color fluorescence digital pathology.

Authors:  Michel Nederlof; Shigeo Watanabe; Bill Burnip; D Lansing Taylor; Rebecca Critchley-Thorne
Journal:  J Pathol Inform       Date:  2011-11-15

10.  Deep Learning-Based Multi-Class Classification of Breast Digital Pathology Images.

Authors:  Weiming Mi; Junjie Li; Yucheng Guo; Xinyu Ren; Zhiyong Liang; Tao Zhang; Hao Zou
Journal:  Cancer Manag Res       Date:  2021-06-10       Impact factor: 3.989

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