Interpretation of CheckList for EvaluAtion of Radiomics research (CLEAR)_Chinese Journal of Evidence-Based Medicine

Authors：

ZHONG Jingyu ^1,2 , XING Yue ^1,2 , HU Yangfan ^1,2 , DING Defang ^1,2 , LIU Xianwei ¹ , DAI Shun ¹ , LU Junjie ³ , YANG Jiarui ⁴ , CHU Jingshen ⁵ , SONG Yang ⁶ , LU Mingda ⁷ , ZHANG Huan ⁸ ,  YANG Guang ⁹ ,  YAO Weiwu ^1,2 , CLEAR working group

1. Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200336, P. R. China;
2. Shanghai Key Laboratory of Flexible Medical Robotics, Tongren Hospital, Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai 200336, P. R. China;
3. Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California 94305, USA;
4. Department of Biomedical Engineering, Boston University, Boston 02215, USA;
5. Editorial Office of Journal of Diagnostics Concepts & Practice, Department of Science and Technology Development, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, P. R. China;
6. MR Research Collaboration, Siemens Healthineers Ltd., Shanghai 200126, P. R. China;
7. MR Application, Siemens Healthineers Ltd., Shanghai 200126, P. R. China;
8. Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, P. R. China;
9. Shanghai Key Laboratory of Magnetic Resonance, Department of Physics, East China Normal University, Shanghai 200062, P. R. China;

Corresponding author：

YANG Guang, Email: gyang@phy.ecnu.edu.cn; YAO Weiwu, Email: yaoweiwuhuan@163.com

Keywords：

Radiomics; Artificial intelligence; Reporting guideline; Interpretation

DOI：

10.7507/1672-2531.202407143

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Radiomics transforms the medical images into minable high-throughput data, extracts the in-depth information invisible to the naked eye, in order to provide support for clinical diagnosis and treatment decision-making processes through the analysis of these data. Recently, radiomics has garnered widespread attention from researchers, with a continuously increasing number of research publications. However, there is still a lack of transparency in reporting radiomics studies. To guide the reporting of radiomics research, the CheckList for EvaluAtion of Radiomics research (CLEAR) was developed by the CLEAR working group using an expert consensus process. This checklist, which was published in May 2023, comprises 58 items and has been endorsed by the European Society of Radiology (ESR) and the European Society for Medical Imaging Informatics (EuSoMII). With authorization from the CLEAR working group, this article introduces and interprets the content of this checklist, to promote the understanding and application of CLEAR among radiomics researchers in China, and to enhance the transparency of radiomics research reporting.

1.	Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer, 2012, 48(4): 441-446.
2.	Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology, 2016, 278(2): 563-577.
3.	Volpe S, Mastroleo F, Krengli M, et al. Quo vadis radiomics. Bibliometric analysis of 10-year radiomics journey. Eur Radiol, 2023, 33(10): 6736-6745.
4.	Pinto Dos Santos D, Dietzel M, Baessler B. A decade of radiomics research: are images really data or just patterns in the noise. Eur Radiol, 2021, 31(1): 1-4.
5.	Pfaehler E, Zhovannik I, Wei L, et al. A systematic review and quality of reporting checklist for repeatability and reproducibility of radiomic features. Phys Imaging Radiat Oncol, 2021, 20: 69-75.
6.	Huang EP, O'Connor JPB, McShane LM, et al. Criteria for the translation of radiomics into clinically useful tests. Nat Rev Clin Oncol, 2023, 20(2): 69-82.
7.	Zhong J, Lu J, Zhang G, et al. An overview of meta-analyses on radiomics: more evidence is needed to support clinical translation. Insights Imaging, 2023, 14(1): 111.
8.	Kocak B, Keles A, Kose F, et al. Quality of radiomics research: comprehensive analysis of 1574 unique publications from 89 reviews. Eur Radiol, 2025, 35(4): 1980-1992.
9.	Barry N, Kendrick J, Molin K, et al. Evaluating the impact of the radiomics quality score: a systematic review and meta-analysis. Eur Radiol, 2025, 35(3): 1701-1713.
10.	Park SH, Suh CH. Reporting guidelines for artificial intelligence studies in healthcare (for both conventional and large language models): what's new in 2024. Korean J Radiol, 2024, 25(8): 687-690.
11.	Kocak B, Baessler B, Bakas S, et al. CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII. Insights Imaging, 2023, 14(1): 75.
12.	Kocak B, Borgheresi A, Ponsiglione A, et al. Explanation and elaboration with examples for CLEAR (CLEAR-E3): an EuSoMII radiomics auditing group initiative. Eur Radiol Exp, 2024, 8(1): 72.
13.	Mongan J, Moy L, Kahn CE. Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiol Artif Intell, 2020, 2(2): e200029.
14.	Stahl AC, Tietz AS, Dewey M, et al. Has the quality of reporting improved since it became mandatory to use the standards for reporting diagnostic accuracy. Insights Imaging, 2023, 14(1): 85.
15.	Zhong J, Xing Y, Lu J, et al. The endorsement of general and artificial intelligence reporting guidelines in radiological journals: a meta-research study. BMC Med Res Methodol, 2023, 23(1): 292.
16.	Koçak B, Keleş A, Köse F. Meta-research on reporting guidelines for artificial intelligence: are authors and reviewers encouraged enough in radiology, nuclear medicine, and medical imaging journals. Diagn Interv Radiol, 2024, 30(5): 291-298.
17.	Zhong J, Liu X, Lu J, et al. Overlooked and underpowered: a meta-research addressing sample size in radiomics prediction models for binary outcomes. Eur Radiol, 2025, 35(3): 1146-1156.
18.	Riley RD, Snell KIE, Archer L, et al. Evaluation of clinical prediction models (part 3): calculating the sample size required for an external validation study. BMJ, 2024, 384: e074821.
19.	Zhu L, Dong H, Sun J, et al. Robustness of radiomics among photon-counting detector CT and dual-energy CT systems: a texture phantom study. Eur Radiol, 2025, 35(2): 871-884.
20.	Zhang H, Lu T, Wang L, et al. Robustness of radiomics within photon-counting detector CT: impact of acquisition and reconstruction factors. Eur Radiol, 2025, Epub ahead of print.
21.	Kocak B, Yardimci AH, Nazli MA, et al. REliability of consensus-based segMentatIoN in raDiomic feature reproducibility (REMIND): a word of caution. Eur J Radiol, 2023, 165: 110893.
22.	Zwanenburg A, Vallières M, Abdalah MA, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology, 2020, 295(2): 328-338.
23.	Lei M, Varghese B, Hwang D, et al. Benchmarking various radiomic toolkit features while applying the image biomarker standardization initiative toward clinical translation of radiomic analysis. J Digit Imaging, 2021, 34(5): 1156-1170.
24.	Bettinelli A, Marturano F, Avanzo M, et al. A novel benchmarking approach to assess the agreement among radiomic tools. Radiology, 2022, 303(3): 533-541.
25.	Whybra P, Zwanenburg A, Andrearczyk V, et al. The image biomarker standardization initiative: standardized convolutional filters for reproducible radiomics and enhanced clinical insights. Radiology, 2024, 310(2): e231319.
26.	Monti CB, Ambrogi F, Sardanelli F. Sample size calculation for data reliability and diagnostic performance: a go-to review. Eur Radiol Exp, 2024, 8(1): 79.
27.	Marcus E, Teuwen J. Artificial intelligence and explanation: how, why, and when to explain black boxes. Eur J Radiol, 2024, 173: 111393.
28.	Luo Z, Li J, Liao Y, et al. Prediction of response to preoperative neoadjuvant chemotherapy in extremity high-grade osteosarcoma using X-ray and multiparametric MRI radiomics. J Xray Sci Technol, 2023, 31(3): 611-626.
29.	Bicego M, Mensi A. Null/no information rate (NIR): a statistical test to assess if a classification accuracy is significant for a given problem. 2023.
30.	Collins GS, Moons KGM, Dhiman P, et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ, 2024, 385: e078378.
31.	Collins GS, Dhiman P, Andaur Navarro CL, et al. Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open, 2021, 11(7): e048008.
32.	Jha AK, Bradshaw TJ, Buvat I, et al. Nuclear medicine and artificial intelligence: best practices for evaluation (the RELAINCE Guidelines). J Nucl Med, 2022, 63(9): 1288-1299.
33.	Kocak B, Yardimci AH, Yuzkan S, et al. Transparency in artificial intelligence research: a systematic review of availability items related to open science in radiology and nuclear medicine. Acad Radiol, 2023, 30(10): 2254-2266.
34.	Akinci D'Antonoli T, Cuocolo R, Baessler B, et al. Towards reproducible radiomics research: introduction of a database for radiomics studies. Eur Radiol, 2024, 34(1): 436-443.
35.	Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol, 2017, 14(12): 749-762.
36.	Kocak B, Akinci D'Antonoli T, Mercaldo N, et al. METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII. Insights Imaging, 2024, 15(1): 8.
37.	Kocak B, Chepelev LL, Chu LC, et al. Assessment of RadiomIcS rEsearch (ARISE): a brief guide for authors, reviewers, and readers from the Scientific Editorial Board of European Radiology. Eur Radiol, 2023, 33(11): 7556-7560.
38.	Liu Y, Wang Y, Hu X, et al. Multimodality deep learning radiomics predicts pathological response after neoadjuvant chemoradiotherapy for esophageal squamous cell carcinoma. Insights Imaging, 2024, 15(1): 277.
39.	Wang Z, Zhu L, Liu S, et al. Development and validation of a CT-based radiomic nomogram for predicting surgical resection risk in patients with adhesive small bowel obstruction. BMC Med Imaging, 2025, 25(1): 46.
40.	Tran K, Ginzburg D, Hong W, et al. Post-radiotherapy stage III/IV non-small cell lung cancer radiomics research: a systematic review and comparison of CLEAR and RQS frameworks. Eur Radiol, 2024, 34(10): 6527-6543.

1. Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer, 2012, 48(4): 441-446.
2. Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology, 2016, 278(2): 563-577.
3. Volpe S, Mastroleo F, Krengli M, et al. Quo vadis radiomics. Bibliometric analysis of 10-year radiomics journey. Eur Radiol, 2023, 33(10): 6736-6745.
4. Pinto Dos Santos D, Dietzel M, Baessler B. A decade of radiomics research: are images really data or just patterns in the noise. Eur Radiol, 2021, 31(1): 1-4.
5. Pfaehler E, Zhovannik I, Wei L, et al. A systematic review and quality of reporting checklist for repeatability and reproducibility of radiomic features. Phys Imaging Radiat Oncol, 2021, 20: 69-75.
6. Huang EP, O'Connor JPB, McShane LM, et al. Criteria for the translation of radiomics into clinically useful tests. Nat Rev Clin Oncol, 2023, 20(2): 69-82.
7. Zhong J, Lu J, Zhang G, et al. An overview of meta-analyses on radiomics: more evidence is needed to support clinical translation. Insights Imaging, 2023, 14(1): 111.
8. Kocak B, Keles A, Kose F, et al. Quality of radiomics research: comprehensive analysis of 1574 unique publications from 89 reviews. Eur Radiol, 2025, 35(4): 1980-1992.
9. Barry N, Kendrick J, Molin K, et al. Evaluating the impact of the radiomics quality score: a systematic review and meta-analysis. Eur Radiol, 2025, 35(3): 1701-1713.
10. Park SH, Suh CH. Reporting guidelines for artificial intelligence studies in healthcare (for both conventional and large language models): what's new in 2024. Korean J Radiol, 2024, 25(8): 687-690.
11. Kocak B, Baessler B, Bakas S, et al. CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII. Insights Imaging, 2023, 14(1): 75.
12. Kocak B, Borgheresi A, Ponsiglione A, et al. Explanation and elaboration with examples for CLEAR (CLEAR-E3): an EuSoMII radiomics auditing group initiative. Eur Radiol Exp, 2024, 8(1): 72.
13. Mongan J, Moy L, Kahn CE. Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiol Artif Intell, 2020, 2(2): e200029.
14. Stahl AC, Tietz AS, Dewey M, et al. Has the quality of reporting improved since it became mandatory to use the standards for reporting diagnostic accuracy. Insights Imaging, 2023, 14(1): 85.
15. Zhong J, Xing Y, Lu J, et al. The endorsement of general and artificial intelligence reporting guidelines in radiological journals: a meta-research study. BMC Med Res Methodol, 2023, 23(1): 292.
16. Koçak B, Keleş A, Köse F. Meta-research on reporting guidelines for artificial intelligence: are authors and reviewers encouraged enough in radiology, nuclear medicine, and medical imaging journals. Diagn Interv Radiol, 2024, 30(5): 291-298.
17. Zhong J, Liu X, Lu J, et al. Overlooked and underpowered: a meta-research addressing sample size in radiomics prediction models for binary outcomes. Eur Radiol, 2025, 35(3): 1146-1156.
18. Riley RD, Snell KIE, Archer L, et al. Evaluation of clinical prediction models (part 3): calculating the sample size required for an external validation study. BMJ, 2024, 384: e074821.
19. Zhu L, Dong H, Sun J, et al. Robustness of radiomics among photon-counting detector CT and dual-energy CT systems: a texture phantom study. Eur Radiol, 2025, 35(2): 871-884.
20. Zhang H, Lu T, Wang L, et al. Robustness of radiomics within photon-counting detector CT: impact of acquisition and reconstruction factors. Eur Radiol, 2025, Epub ahead of print.
21. Kocak B, Yardimci AH, Nazli MA, et al. REliability of consensus-based segMentatIoN in raDiomic feature reproducibility (REMIND): a word of caution. Eur J Radiol, 2023, 165: 110893.
22. Zwanenburg A, Vallières M, Abdalah MA, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology, 2020, 295(2): 328-338.
23. Lei M, Varghese B, Hwang D, et al. Benchmarking various radiomic toolkit features while applying the image biomarker standardization initiative toward clinical translation of radiomic analysis. J Digit Imaging, 2021, 34(5): 1156-1170.
24. Bettinelli A, Marturano F, Avanzo M, et al. A novel benchmarking approach to assess the agreement among radiomic tools. Radiology, 2022, 303(3): 533-541.
25. Whybra P, Zwanenburg A, Andrearczyk V, et al. The image biomarker standardization initiative: standardized convolutional filters for reproducible radiomics and enhanced clinical insights. Radiology, 2024, 310(2): e231319.
26. Monti CB, Ambrogi F, Sardanelli F. Sample size calculation for data reliability and diagnostic performance: a go-to review. Eur Radiol Exp, 2024, 8(1): 79.
27. Marcus E, Teuwen J. Artificial intelligence and explanation: how, why, and when to explain black boxes. Eur J Radiol, 2024, 173: 111393.
28. Luo Z, Li J, Liao Y, et al. Prediction of response to preoperative neoadjuvant chemotherapy in extremity high-grade osteosarcoma using X-ray and multiparametric MRI radiomics. J Xray Sci Technol, 2023, 31(3): 611-626.
29. Bicego M, Mensi A. Null/no information rate (NIR): a statistical test to assess if a classification accuracy is significant for a given problem. 2023.
30. Collins GS, Moons KGM, Dhiman P, et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ, 2024, 385: e078378.
31. Collins GS, Dhiman P, Andaur Navarro CL, et al. Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open, 2021, 11(7): e048008.
32. Jha AK, Bradshaw TJ, Buvat I, et al. Nuclear medicine and artificial intelligence: best practices for evaluation (the RELAINCE Guidelines). J Nucl Med, 2022, 63(9): 1288-1299.
33. Kocak B, Yardimci AH, Yuzkan S, et al. Transparency in artificial intelligence research: a systematic review of availability items related to open science in radiology and nuclear medicine. Acad Radiol, 2023, 30(10): 2254-2266.
34. Akinci D'Antonoli T, Cuocolo R, Baessler B, et al. Towards reproducible radiomics research: introduction of a database for radiomics studies. Eur Radiol, 2024, 34(1): 436-443.
35. Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol, 2017, 14(12): 749-762.
36. Kocak B, Akinci D'Antonoli T, Mercaldo N, et al. METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII. Insights Imaging, 2024, 15(1): 8.
37. Kocak B, Chepelev LL, Chu LC, et al. Assessment of RadiomIcS rEsearch (ARISE): a brief guide for authors, reviewers, and readers from the Scientific Editorial Board of European Radiology. Eur Radiol, 2023, 33(11): 7556-7560.
38. Liu Y, Wang Y, Hu X, et al. Multimodality deep learning radiomics predicts pathological response after neoadjuvant chemoradiotherapy for esophageal squamous cell carcinoma. Insights Imaging, 2024, 15(1): 277.
39. Wang Z, Zhu L, Liu S, et al. Development and validation of a CT-based radiomic nomogram for predicting surgical resection risk in patients with adhesive small bowel obstruction. BMC Med Imaging, 2025, 25(1): 46.
40. Tran K, Ginzburg D, Hong W, et al. Post-radiotherapy stage III/IV non-small cell lung cancer radiomics research: a systematic review and comparison of CLEAR and RQS frameworks. Eur Radiol, 2024, 34(10): 6527-6543.

Chinese Journal of Evidence-Based Medicine

Latest ArticlesInterpretation of CheckList for EvaluAtion of Radiomics research (CLEAR)

Abstract Full text Figures/Tables Video References Cited by

Format

Content