CLEAR item#10

“Study nature (e.g., retrospective, prospective). Indicate whether the study is prospective or retrospective and case/control or cohort, etc. In the case of prospective studies, provide registration details if available.” [1] (from the article by Kocak et al.; licensed under CC BY 4.0)

Reporting examples for CLEAR item#10

Example#1. “We retrospectively reviewed a cohort of 105 patients with grade II-IV astrocytomas. […] A total of 105 patients met the study criteria and were divided into a training dataset (31 December 2011 to 2 November 2015, n = 74) and a time-independent validation dataset (16 November 2015 to 21 March 2017, n = 31)” [2] (from the article by Wei et al.; licensed under CC BY 4.0)

Example#2. “This was a multicentre, prospective study. […] This multicentre study was approved by the ethics committee of the principal investigator’s hospital and is registered at ClinicalTrials.gov (NCT02313649).” [3] (from the article by Wang et al.; licensed under CC BY-NC 4.0)

Example#3. “We designed a retrospective case-control study that patients with ACS (n = 90) were well matched to patients with no cardiac events (n = 1496) during 3 years follow-up, then which were randomly divided into training and test datasets with a ratio of 3:1” [4] (from the article by Shang et al.; licensed under CC BY 4.0)

Explanation and elaboration of CLEAR item#10

Providing study´s nature such as retrospective (see Example#1), prospective (see Example#2), case/control (see Example#3), observational, diagnostic, or prognostic enables readers to assess the applicability of findings to their own context. Different study designs have distinct strengths and weaknesses in mitigating bias and confounding variables [5]. In prospective research, by making registration information available (see Example#2), transparency is encouraged and contributes to maintaining the trustworthiness of the evidence used to create health policies and treatment decisions.

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. Wei J, Yang G, Hao X, et al (2019) A multi-sequence and habitat-based MRI radiomics signature for preoperative prediction of MGMT promoter methylation in astrocytomas with prognostic implication. Eur Radiol 29:877–888. https://doi.org/10.1007/s00330-018-5575-z
  3. Wang K, Lu X, Zhou H, et al (2019) Deep learning Radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study. Gut 68:729–741. https://doi.org/10.1136/gutjnl-2018-316204
  4. Shang J, Ma S, Guo Y, et al (2022) Prediction of acute coronary syndrome within 3 years using radiomics signature of pericoronary adipose tissue based on coronary computed tomography angiography. Eur Radiol 32:1256–1266. https://doi.org/10.1007/s00330-021-08109-z
  5. Mann CJ (2003) Observational research methods. Research design II: cohort, cross sectional, and case-control studies. Emerg Med J EMJ 20:54–60. https://doi.org/10.1136/emj.20.1.54

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