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REVIEW article

Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1402838
This article is part of the Research Topic Precision Medical Imaging for Cancer Diagnosis and Treatment - Vol. II View all 27 articles

The ins and outs of errors in oncology imaging: the DAC framework for radiologists

Provisionally accepted
Antoine IANNESSI Antoine IANNESSI 1,2Hubert Beaumont Hubert Beaumont 2*Carlos Aguillera Carlos Aguillera 3Francois Nicol Francois Nicol 4Anne-Sophie BERTRAND Anne-Sophie BERTRAND 5
  • 1 Centre Antoine Lacassagne, Nice, Provence-Alpes-Côte d'Azur, France
  • 2 Median Technologies, Valbonne, Provence-Alpes-Côte-d'Azur, France
  • 3 Therapixel Research Unit, Nice, France
  • 4 Centre Mémoire de Ressources et de Recherche de Nice (CMRR), Nice, Provence-Alpes-Côte d'Azur, France
  • 5 Imaging Center, Beaulieu sur mer, France

The final, formatted version of the article will be published soon.

    With the increasingly central role of imaging in medical diagnosis, understanding and monitoring radiological errors has become essential. In the field of oncology, the severity of the disease makes radiological error more visible, with both individual consequences and public health issues. The quantitative trend radiology allows to consider the diagnostic task as a problem of classification supported by the latest neurocognitive theories in explaining decision making errors, this purposeful model provides an actionable framework to support root cause analysis of diagnostic errors in radiology and envision corresponding riskmanagement strategies. The D for Data, A for Analysis and C for Communication are the three drivers of errors and we propose a practical toolbox for our colleagues to prevent individual and systemic sources of error.

    Keywords: Diagnostic errors / statistics & numerical data, radiologists, Cognition, Scientific mistake, Quality Improvement, oncology, Risk factors, Tomography Mammography

    Received: 18 Mar 2024; Accepted: 29 Aug 2024.

    Copyright: © 2024 IANNESSI, Beaumont, Aguillera, Nicol and BERTRAND. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Hubert Beaumont, Median Technologies, Valbonne, Provence-Alpes-Côte-d'Azur, France

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.