CLEAR item#8

“Ethical details (e.g., approval, consent, data protection). Describe the ethical questions to ensure that the study was conducted appropriately. Give information about ethical approval, informed consent, and data protection (e.g., de-identification) if the data are from private sources.” [1] (from the article by Kocak et al.; licensed under CC BY 4.0)

Reporting examples for CLEAR item#8

Example#1. “Ethical approval was obtained from the National Maternity Hospital, Dublin (EC30.2018) and Rotunda Hospital Dublin ethics committees (RAG 2019–10). Participants provided written, informed consent.” [2] (from the article by Bartels et al.; licensed under CC BY 4.0)

Example#2. “The need for informed consent was waived owing to the retrospective design of the study.” [3] (from the article by Zorzi et al.; licensed under CC BY 4.0)

Example#3. “This retrospective analysis was approved by each hospital’s ethics committees, and all patient information was anonymized.” [4] (from the article by He et al.; licensed under CC BY 4.0)

Explanation and elaboration of CLEAR item#8

Item#8 addresses the ethical aspects of a study, focusing on ethical approval, informed consent, and data protection measures. Ethical considerations are fundamental in research to ensure the rights and welfare of participants are protected and to maintain the integrity of the research process. In Example#1, an explicit mention is made of obtaining ethical approval from specific ethics committees, along with the detail that participants provided written, informed consent. This demonstrates adherence to ethical protocols, ensuring participants are fully informed and agree to the study’s terms. Example#2 highlights a scenario where informed consent is waived due to the study’s retrospective design. In such cases, ethical committees often allow waivers for consent, especially when the study involves minimal risk to participants or utilizes already collected data. Example#3 illustrates another retrospective study, where approval was obtained from hospital ethics committees, and all patient data was anonymized. Anonymization is a crucial aspect of data protection, ensuring patient privacy and confidentiality, especially in studies where consent is not directly obtained.

References

  1. Kocak B, Baessler B, Bakas S, et al (2023) CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII. Insights Imaging 14:75. https://doi.org/10.1186/s13244-023-01415-8
  2. Bartels HC, O’Doherty J, Wolsztynski E, et al (2023) Radiomics-based prediction of FIGO grade for placenta accreta spectrum. Eur Radiol Exp 7:54. https://doi.org/10.1186/s41747-023-00369-2
  3. Zorzi G, Berta L, Rizzetto F, et al (2023) Artificial intelligence for differentiating COVID-19 from other viral pneumonias on CT: comparative analysis of different models based on quantitative and radiomic approaches. Eur Radiol Exp 7:3. https://doi.org/10.1186/s41747-022-00317-6
  4. He Q-H, Feng J-J, Lv F-J, et al (2023) Deep learning and radiomic feature-based blending ensemble classifier for malignancy risk prediction in cystic renal lesions. Insights Imaging 14:6. https://doi.org/10.1186/s13244-022-01349-7

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