CLEAR item#6

“Study objective(s). Describe the purpose of the study while focusing on the scientific problem. Mention the expected contributions to the current literature.” [1] (from the article by Kocak et al.; licensed under CC BY 4.0)

Reporting examples for CLEAR item#6

Example#1. “[…] However, the association of GBM recurrence and certain radiomic features has rarely been investigated. Moreover, it has been shown that preoperative MRI parameters contribute to prediction of the prognosis of patients with GBM.

Therefore, this study aimed to identify heterogeneity within the peritumoral edema region via radiomics, which may be beneficial to the optimization of surgical resection.” [2] (from the article by Long et al.; licensed under CC BY 4.0)

Example#2. “However, no study has assessed bone quality based on the high-throughput radiomic features extracted from routing CT scans. In this study, radiomic features were extracted from the 3D segmentation of the entire vertebral body, comprising both the cancellous and cortical bones to determine the efficacy of a radiomics model based on routine preoperative lumbar spine CT scans in screening for osteoporosis.” [3] (from the article by Jiang et al.; licensed under CC BY 4.0)

Example#3. “In this study, we chose well-established ML classifiers from previous literature in the field and compared their performance to predict outcome variables of HGG: OS, IDH mutation, O-6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation, epidermal growth factor receptor vIII (EGFR) amplification, and Ki-67 expression, based on features extracted from conventional and advanced MRI. Our objectives were (1) to assess the best algorithm for each prediction task, providing a benchmark for future clinical applications. Particularly, we wanted to compare classic and ensemble learners among ML classifiers to provide a comprehensive view on model performance; (2) to evaluate highly predictive radiomic features extracted from different tumor regions, highlighting possible correlations between MR parameters and tumor molecular/genetic characteristics.” [4] (from the article by Pasquini et al.; licensed under CC BY 4.0)

Explanation and elaboration of CLEAR item#6

Item#6 highlights how a clear and thorough summary of the goals and importance of the research should be included, along with an explanation of how your study will add to the body of knowledge already in existence. This could entail presenting fresh perspectives, bolstering, or refuting accepted theories, offering useful applications, or recommending lines of inquiry for further study. All of the three examples above clearly state the objective(s) of the studies and some notes on the previous literature and related expectations.

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. Long H, Zhang P, Bi Y, et al (2023) MRI radiomic features of peritumoral edema may predict the recurrence sites of glioblastoma multiforme. Front Oncol 12. https://doi.org/10.3389/fonc.2022.1042498
  3. Jiang Y-W, Xu X-J, Wang R, Chen C-M (2022) Radiomics analysis based on lumbar spine CT to detect osteoporosis. Eur Radiol 32:8019–8026. https://doi.org/10.1007/s00330-022-08805-4
  4. Pasquini L, Napolitano A, Lucignani M, et al (2021) AI and High-Grade Glioma for Diagnosis and Outcome Prediction: Do All Machine Learning Models Perform Equally Well? Front Oncol 11. https://doi.org/10.3389/fonc.2021.601425

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