CLEAR item#3

“Relevant keywords for radiomics. List the primary keywords that indicate (e.g., radiomics, texture analysis) and characterize a radiomic study (e.g., machine learning, deep learning, computed tomography, magnetic resonance imaging, reproducibility), unless the journal requires exclusive use of certain terms (e.g., MeSH terms, which do not yet include radiomics-specific terms).” [1] (from the article by Kocak et al.; licensed under CC BY 4.0)

Reporting examples for CLEAR item#3

Example#1. “Renal cell carcinoma, Computed tomography, Radiomics, Deep learning” [2] (from the article by Wang et al.; licensed under CC BY 4.0)

Example#2. “Magnetic resonance imaging, Radiomics, Knee osteoarthritis, Bone, Machine learning” [3] (from the article by Hirvasniemi et al.; licensed under CC BY 4.0)

Explanation and elaboration of CLEAR item#3

To make the published work searchable, the keywords must highlight its most important aspects. As Example#1 and Example#2 show, radiomics studies must clearly specify imaging modalities (MRI, CT, etc.), target conditions or pathology and anatomy, and analysis methods (machine learning, deep learning). Keywords that clearly state these characteristics help investigators find studies for their own work, systematic reviews, and meta-analyses of radiomics research.

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. Wang S, Zhu C, Jin Y, et al (2023) A multi-model based on radiogenomics and deep learning techniques associated with histological grade and survival in clear cell renal cell carcinoma. Insights Imaging 14:207. https://doi.org/10.1186/s13244-023-01557-9
  3. Hirvasniemi J, Klein S, Bierma-Zeinstra S, et al (2021) A machine learning approach to distinguish between knees without and with osteoarthritis using MRI-based radiomic features from tibial bone. Eur Radiol 31:8513–8521. https://doi.org/10.1007/s00330-021-07951-5

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