Interpretation of METhodological RadiomICs Score (METRICS)_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 ^9,10 ,  YAO Weiwu ^1,2 , METRICS 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, Massachusetts 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. Institution of Magnetic Resonance and Molecular Imaging in Medicine, East China Normal University, Shanghai 200062, P. R. China;
10. 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; Quality evaluation; Interpretation

DOI：

10.7507/1672-2531.202504047

Video：

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Abstract Full text Figures/Tables Video References Cited by

Radiomics extracts high-throughput quantitative features from medical images and converts into minable data, in order to utilizes the analysis results of these data to support clinical diagnosis and treatment decisions. In recent years, radiomics has emerged as a significant research method in medical imaging field, while their methodological quality varies. To improve the methodological quality of radiomics research, the METhodological RadiomICs Score (METRICS) was developed by the METRICS working group using an expert consensus process. This tool, which was published in January 2024, comprises 30 items and has been endorsed by the European Society for Medical Imaging Informatics (EuSoMII). With authorization from the METRICS working group, this article introduces and interprets the content of this tool, to promote the understanding and application of METRICS among radiomics researchers in China, and to improve the methodological quality of radiomics research.

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.	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.
11.	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.
12.	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.
13.	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.
14.	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.
15.	Spadarella G, Stanzione A, Akinci D'Antonoli T, et al. Systematic review of the radiomics quality score applications: an EuSoMII radiomics auditing group initiative. Eur Radiol, 2023, 33(3): 1884-1894.
16.	Akinci D'Antonoli T, Cavallo AU, Vernuccio F, et al. Reproducibility of radiomics quality score: an intra- and inter-rater reliability study. Eur Radiol, 2024, 34(4): 2791-2804.
17.	Kocak B, Akinci D'Antonoli T, Ates Kus E, et al. Self-reported checklists and quality scoring tools in radiomics: a meta-research. Eur Radiol, 2024, 34(8): 5028-5040.
18.	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.
19.	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.
20.	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.
21.	Cuocolo R, Comelli A, Stefano A, et al. Deep learning whole-gland and zonal prostate segmentation on a public MRI dataset. J Magn Reson Imaging, 2021, 54(2): 452-459.
22.	Gitto S, Cuocolo R, van Langevelde K, et al. MRI radiomics-based machine learning classification of atypical cartilaginous tumour and grade II chondrosarcoma of long bones. EBioMedicine, 2022, 75: 103757.
23.	Kobayashi K, Miyake M, Takahashi M, et al. Observing deep radiomics for the classification of glioma grades. Sci Rep, 2021, 11(1): 10942.
24.	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.
25.	Cerdá-Alberich L, Solana J, Mallol P, et al. MAIC-10 brief quality checklist for publications using artificial intelligence and medical images. Insights Imaging, 2023, 14(1): 11.
26.	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.
27.	Tejani AS, Klontzas ME, Gatti AA, et al. Checklist for artificial intelligence in medical imaging (CLAIM): 2024 update. Radiol Artif Intell, 2024, 6(4): e240300.
28.	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.
29.	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 Jan 31.
30.	Whiting PF, Rutjes AW, Westwood ME, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med, 2011, 155(8): 529-536.
31.	Wolff RF, Moons KGM, Riley RD, et al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med, 2019, 170(1): 51-58.
32.	Zhong J, Hu Y, Zhang G, et al. An updated systematic review of radiomics in osteosarcoma: utilizing CLAIM to adapt the increasing trend of deep learning application in radiomics. Insights Imaging, 2022, 13(1): 138.
33.	Jiang P, Ergu D , Liu F , et al. A review of yolo algorithm developments. Procedia Comput Sci, 2022, 199: 1066-1073.
34.	He K, Gkioxari G, Dollar P, et al. Mask R-CNN. IEEE Trans Pattern Anal Mach Intell, 2020, 42(2): 386-397.
35.	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.
36.	Yu Y, Li GF, Tan WX, et al. Towards automatical tumor segmentation in radiomics: a comparative analysis of various methods and radiologists for both region extraction and downstream diagnosis. BMC Med Imaging, 2025, 25(1): 63.
37.	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.
38.	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.
39.	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.
40.	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.
41.	Zhang X, Iqbal Bin Saripan M, Wu Y, et al. The impact of the combat method on radiomics feature compensation and analysis of scanners from different manufacturers. BMC Med Imaging, 2024, 24(1): 137.
42.	Fornacon-Wood I, Mistry H, Ackermann CJ, et al. Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform. Eur Radiol, 2020, 30(11): 6241-6250.
43.	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.
44.	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.
45.	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.
46.	Goldenholz DM, Sun H, Ganglberger W, et al. Sample size analysis for machine learning clinical validation studies. Biomedicines, 2023, 11(3): 685.
47.	Moskowitz CS, Welch ML, Jacobs MA, et al. Radiomic analysis: study design, statistical analysis, and other bias mitigation strategies. Radiology, 2022, 304(2): 265-273.
48.	Kocak B, Ponsiglione A, Stanzione A, et al. CLEAR guideline for radiomics: early insights into current reporting practices endorsed by EuSoMII. Eur J Radiol, 2024, 181: 111788.
49.	Akinci D'Antonoli T, Mercaldo ND. Obsolescence of nomograms in radiomics research. Eur Radiol, 2023, 33(11): 7477-7478.
50.	Hameed M, Yeung J, Boone D, et al. Meta-research: How many diagnostic or prognostic models published in radiological journals are evaluated externally. Eur Radiol, 2024, 34(4): 2524-2533.
51.	Diamandis EP. Cancer biomarkers: can we turn recent failures into success. J Natl Cancer Inst, 2010, 102(19): 1462-1467.
52.	Hwang EJ, Park CM, Ryu Y, et al. Pulmonary adenocarcinomas appearing as part-solid ground-glass nodules: is measuring solid component size a better prognostic indicator. Eur Radiol, 2015, 25(2): 558-567.
53.	Kim H, Goo JM, Suh YJ, et al. Measurement of multiple solid portions in part-solid nodules for T categorization: evaluation of prognostic implication. J Thorac Oncol, 2018, 13(12): 1864-1872.
54.	Steiger P, Sood R. How can radiomics be consistently applied across imagers and institutions. Radiology, 2019, 291(1): 60-61.
55.	Stüber AT, Heimer MM, Ta J, et al. Replication study of PD-L1 status prediction in NSCLC using PET/CT radiomics. Eur J Radiol, 2025, 183: 111825.
56.	Lambin P. Radiomics quality score - RQS 2.0 (under development). 2025.
57.	Lekadir K, Frangi AF, Porras AR, et al. FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare. BMJ, 2025, 388: e081554.
58.	Evangelista L, Fiz F, Laudicella R, et al. PET radiomics and response to immunotherapy in lung cancer: a systematic review of the literature. Cancers (Basel), 2023, 15(12): 3258.
59.	Kocak B, Mese I, Ates Kus E. Radiomics for differentiating radiation-induced brain injury from recurrence in gliomas: systematic review, meta-analysis, and methodological quality evaluation using METRICS and RQS. Eur Radiol, 2025.
60.	Akinci D'Antonoli T, Cavallo AU, Kocak B, et al. Reproducibility of methodological radiomics score (METRICS): an intra- and inter-rater reliability study endorsed by EuSoMII. Eur Radiol, 2025.
61.	Mese I, Kocak B. ChatGPT as an effective tool for quality evaluation of radiomics research. Eur Radiol, 2025, 35(4): 2030-2042.
62.	Mese I, Kocak B. Large language models in methodological quality evaluation of radiomics research based on METRICS: ChatGPT vs NotebookLM vs radiologist. Eur J Radiol, 2025, 184: 111960.
63.	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.
64.	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.
65.	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.
66.	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.
67.	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.

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. 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.
11. 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.
12. 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.
13. 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.
14. 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.
15. Spadarella G, Stanzione A, Akinci D'Antonoli T, et al. Systematic review of the radiomics quality score applications: an EuSoMII radiomics auditing group initiative. Eur Radiol, 2023, 33(3): 1884-1894.
16. Akinci D'Antonoli T, Cavallo AU, Vernuccio F, et al. Reproducibility of radiomics quality score: an intra- and inter-rater reliability study. Eur Radiol, 2024, 34(4): 2791-2804.
17. Kocak B, Akinci D'Antonoli T, Ates Kus E, et al. Self-reported checklists and quality scoring tools in radiomics: a meta-research. Eur Radiol, 2024, 34(8): 5028-5040.
18. 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.
19. 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.
20. 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.
21. Cuocolo R, Comelli A, Stefano A, et al. Deep learning whole-gland and zonal prostate segmentation on a public MRI dataset. J Magn Reson Imaging, 2021, 54(2): 452-459.
22. Gitto S, Cuocolo R, van Langevelde K, et al. MRI radiomics-based machine learning classification of atypical cartilaginous tumour and grade II chondrosarcoma of long bones. EBioMedicine, 2022, 75: 103757.
23. Kobayashi K, Miyake M, Takahashi M, et al. Observing deep radiomics for the classification of glioma grades. Sci Rep, 2021, 11(1): 10942.
24. 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.
25. Cerdá-Alberich L, Solana J, Mallol P, et al. MAIC-10 brief quality checklist for publications using artificial intelligence and medical images. Insights Imaging, 2023, 14(1): 11.
26. 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.
27. Tejani AS, Klontzas ME, Gatti AA, et al. Checklist for artificial intelligence in medical imaging (CLAIM): 2024 update. Radiol Artif Intell, 2024, 6(4): e240300.
28. 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.
29. 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 Jan 31.
30. Whiting PF, Rutjes AW, Westwood ME, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med, 2011, 155(8): 529-536.
31. Wolff RF, Moons KGM, Riley RD, et al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med, 2019, 170(1): 51-58.
32. Zhong J, Hu Y, Zhang G, et al. An updated systematic review of radiomics in osteosarcoma: utilizing CLAIM to adapt the increasing trend of deep learning application in radiomics. Insights Imaging, 2022, 13(1): 138.
33. Jiang P, Ergu D , Liu F , et al. A review of yolo algorithm developments. Procedia Comput Sci, 2022, 199: 1066-1073.
34. He K, Gkioxari G, Dollar P, et al. Mask R-CNN. IEEE Trans Pattern Anal Mach Intell, 2020, 42(2): 386-397.
35. 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.
36. Yu Y, Li GF, Tan WX, et al. Towards automatical tumor segmentation in radiomics: a comparative analysis of various methods and radiologists for both region extraction and downstream diagnosis. BMC Med Imaging, 2025, 25(1): 63.
37. 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.
38. 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.
39. 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.
40. 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.
41. Zhang X, Iqbal Bin Saripan M, Wu Y, et al. The impact of the combat method on radiomics feature compensation and analysis of scanners from different manufacturers. BMC Med Imaging, 2024, 24(1): 137.
42. Fornacon-Wood I, Mistry H, Ackermann CJ, et al. Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform. Eur Radiol, 2020, 30(11): 6241-6250.
43. 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.
44. 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.
45. 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.
46. Goldenholz DM, Sun H, Ganglberger W, et al. Sample size analysis for machine learning clinical validation studies. Biomedicines, 2023, 11(3): 685.
47. Moskowitz CS, Welch ML, Jacobs MA, et al. Radiomic analysis: study design, statistical analysis, and other bias mitigation strategies. Radiology, 2022, 304(2): 265-273.
48. Kocak B, Ponsiglione A, Stanzione A, et al. CLEAR guideline for radiomics: early insights into current reporting practices endorsed by EuSoMII. Eur J Radiol, 2024, 181: 111788.
49. Akinci D'Antonoli T, Mercaldo ND. Obsolescence of nomograms in radiomics research. Eur Radiol, 2023, 33(11): 7477-7478.
50. Hameed M, Yeung J, Boone D, et al. Meta-research: How many diagnostic or prognostic models published in radiological journals are evaluated externally. Eur Radiol, 2024, 34(4): 2524-2533.
51. Diamandis EP. Cancer biomarkers: can we turn recent failures into success. J Natl Cancer Inst, 2010, 102(19): 1462-1467.
52. Hwang EJ, Park CM, Ryu Y, et al. Pulmonary adenocarcinomas appearing as part-solid ground-glass nodules: is measuring solid component size a better prognostic indicator. Eur Radiol, 2015, 25(2): 558-567.
53. Kim H, Goo JM, Suh YJ, et al. Measurement of multiple solid portions in part-solid nodules for T categorization: evaluation of prognostic implication. J Thorac Oncol, 2018, 13(12): 1864-1872.
54. Steiger P, Sood R. How can radiomics be consistently applied across imagers and institutions. Radiology, 2019, 291(1): 60-61.
55. Stüber AT, Heimer MM, Ta J, et al. Replication study of PD-L1 status prediction in NSCLC using PET/CT radiomics. Eur J Radiol, 2025, 183: 111825.
56. Lambin P. Radiomics quality score - RQS 2.0 (under development). 2025.
57. Lekadir K, Frangi AF, Porras AR, et al. FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare. BMJ, 2025, 388: e081554.
58. Evangelista L, Fiz F, Laudicella R, et al. PET radiomics and response to immunotherapy in lung cancer: a systematic review of the literature. Cancers (Basel), 2023, 15(12): 3258.
59. Kocak B, Mese I, Ates Kus E. Radiomics for differentiating radiation-induced brain injury from recurrence in gliomas: systematic review, meta-analysis, and methodological quality evaluation using METRICS and RQS. Eur Radiol, 2025.
60. Akinci D'Antonoli T, Cavallo AU, Kocak B, et al. Reproducibility of methodological radiomics score (METRICS): an intra- and inter-rater reliability study endorsed by EuSoMII. Eur Radiol, 2025.
61. Mese I, Kocak B. ChatGPT as an effective tool for quality evaluation of radiomics research. Eur Radiol, 2025, 35(4): 2030-2042.
62. Mese I, Kocak B. Large language models in methodological quality evaluation of radiomics research based on METRICS: ChatGPT vs NotebookLM vs radiologist. Eur J Radiol, 2025, 184: 111960.
63. 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.
64. 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.
65. 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.
66. 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.
67. 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.

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