CLEAR item#1

“Relevant title, specifying the radiomic methodology. Indicate the use of radiomics in the title. The following details can also be considered to be specified in the title: radiomic technique (e.g., hand-crafted, engineered, deep, delta, etc.), modality (e.g., computed tomography [CT], magnetic resonance imaging [MRI], ultrasound), important aspects of the scans (e.g., unenhanced, dynamic), use of machine learning (e.g., machine learning-based), external validation, and multi-center design.” [1] (from the article by Kocak et al.; licensed under CC BY 4.0)

Reporting examples for CLEAR item#1

Example#1. “Comparative performance of MRI-derived PRECISE scores and delta-radiomics models for the prediction of prostate cancer progression in patients on active surveillance” [2] (from the article by Sushentsev et al.; licensed under CC BY 4.0)

Example#2. “Development and validation of CT-based radiomics deep learning signatures to predict lymph node metastasis in non-functional pancreatic neuroendocrine tumors: a multicohort study” [3] (from the article by Gu et al.; licensed under CC BY-NC-ND 4.0)

Explanation and elaboration of CLEAR item#1

Item#1 stresses the significance of properly drafting a title that explains the radiomic approach and strategically integrates crucial elements for clarity and specificity. “Radiomics” should be in the title, not merely mentioned in the abstract. Example#1 and Example#2 use “radiomics” to emphasize the study’s focus and further describe radiomic methods (i.e., “delta” and “deep”). In addition to imaging modality, the title should also specify that the study’s results were validated to establish its significance and dependability. Readers can instantly understand the study’s experimental framework from these title details. Both examples report their imaging modality. Example#2 successfully shows that multiple cohorts trained and tested the study (potentially external). An informative title beyond CLEAR item#1’s definition is also advised. For instance, the title of Example#1 mentions a comparison with non-radiomics approaches, which half of radiomic research lacks [4].

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. Sushentsev N, Rundo L, Blyuss O, et al (2022) Comparative performance of MRI-derived PRECISE scores and delta-radiomics models for the prediction of prostate cancer progression in patients on active surveillance. Eur Radiol 32:680–689. https://doi.org/10.1007/s00330-021-08151-x
  3. Gu W, Chen Y, Zhu H, et al (2023) Development and validation of CT-based radiomics deep learning signatures to predict lymph node metastasis in non-functional pancreatic neuroendocrine tumors: a multicohort study. eClinicalMedicine 65:. https://doi.org/10.1016/j.eclinm.2023.102269
  4. Kocak B, Bulut E, Bayrak ON, et al (2023) NEgatiVE results in Radiomics research (NEVER): A meta-research study of publication bias in leading radiology journals. Eur J Radiol 163:110830. https://doi.org/10.1016/j.ejrad.2023.110830

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