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
- 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
- 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
- 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
- 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
- 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