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  • Review Article
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Radiomics Quality Score 2.0: towards radiomics readiness levels and clinical translation for personalized medicine

Abstract

Radiomics is a tool for medical imaging analysis that could have a relevant role in precision oncology by offering precise quantitative support for clinical decision-making. The Radiomics Quality Score (RQS) is a tool developed to assess the rigour of radiomics studies that has now been widely adopted by researchers. Although RQS version 1.0 established a benchmark, an updated framework is required to account for evolving knowledge and ensure optimal evaluation of the quality of radiomics studies through the inclusion of fairness, explainability, rigorous quality control and harmonization. In this Review, we introduce the updated RQS 2.0, which maintains the scientific rigour of its predecessor and addresses these contemporary needs, and therefore could potentially accelerate clinical translation. Moreover, we introduce the radiomics readiness levels, inspired by the technology readiness level framework, which are integrated in RQS 2.0 and reflect nine distinct levels of incremental improvement in radiomics research with the ultimate aim of clinical implementation. We also detail anticipated future directions in radiomics, outlining a strategic vision to advance precision oncology, which is the ultimate aim of RQS 2.0.

Key points

  • Radiomics is a quantitative image analysis method that enhances disease diagnosis, tumour characterization and prediction of treatment response, supporting its application in precision oncology.

  • The Radiomics Quality Score, originally introduced in 2017 to improve methodological and reporting rigour, has driven improvements in study quality but lacked sufficient coverage of certain aspects such as deep learning-specific challenges, cost effectiveness, prospective design and real-world feasibility.

  • Radiomics approaches can be broadly categorized into handcrafted and deep learning-based methods, each with distinct workflows, challenges and requirements for clinical translation.

  • Key barriers to clinical translation of radiomics include methodological inconsistencies, limited external and prospective validation, lack of transparency and insufficient consideration of model fairness, robustness, explainability and usability in clinical settings.

  • Radiomics Quality Score 2.0 addresses the limitations of its predecessor by incorporating updated criteria for both handcrafted and deep learning-based radiomics and introducing radiomics readiness levels to guide incremental progress towards clinical deployment.

  • Future advances in radiomics will be driven particularly by the integration of multiomics data, the adoption of foundation models, data standardization and the use of federated learning and synthetic data to enhance model generalizability and data accessibility.

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Fig. 1: Handcrafted and deep learning-based radiomics.
Fig. 2: Incorporation of RRLs in RQS 2.0.
Fig. 3: Assessment of RQS 2.0 for a research study.

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References

  1. Larry Jameson, J. & Longo, D. L. Precision medicine — personalized, problematic, and promising. Obstet. Gynecol. Surv. 70, 612 (2015).

    Article  Google Scholar 

  2. Gillies, R. J., Kinahan, P. E. & Hricak, H. Radiomics: images are more than pictures, they are data. Radiology 278, 563–577 (2016).

    Article  PubMed  Google Scholar 

  3. Lambin, P. et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur. J. Cancer 48, 441–446 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Lambin, P. et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat. Rev. Clin. Oncol. 14, 749–762 (2017).

    Article  PubMed  Google Scholar 

  5. Ding, H. et al. Radiomics in oncology: a 10-year bibliometric analysis. Front. Oncol. 11, 689802 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Hosny, A., Aerts, H. J. & Mak, R. H. Handcrafted versus deep learning radiomics for prediction of cancer therapy response. Lancet Digit. Health 1, e106–e107 (2019).

    Article  PubMed  Google Scholar 

  7. Afshar, P., Mohammadi, A., Plataniotis, K. N., Oikonomou, A. & Benali, H. From handcrafted to deep-learning-based cancer radiomics: challenges and opportunities. IEEE Signal. Process. Mag. 36, 132–160 (2019).

    Article  Google Scholar 

  8. Jiang, X., Hu, Z., Wang, S. & Zhang, Y. Deep learning for medical image-based cancer diagnosis. Cancers 15, 3608 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Barry, N. et al. Evaluating the impact of the radiomics quality score: a systematic review and meta-analysis. Eur. Radiol. 35, 1701–1713 (2025).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Spadarella, G. et al. Systematic review of the radiomics quality score applications: an EuSoMII radiomics auditing group initiative. Eur. Radiol. 33, 1884–1894 (2023).

    Article  PubMed  Google Scholar 

  11. Park, C. J. et al. Quality of radiomics research on brain metastasis: a roadmap to promote clinical translation. Korean J. Radiol. 23, 77–88 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Li, H., El Naqa, I. & Rong, Y. Current status of radiomics for cancer management: challenges versus opportunities for clinical practice. J. Appl. Clin. Med. Phys. 21, 7–10 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Huang, E. P. et al. Criteria for the translation of radiomics into clinically useful tests. Nat. Rev. Clin. Oncol. 20, 69–82 (2023).

    Article  PubMed  Google Scholar 

  14. Mali, S. A. et al. Making radiomics more reproducible across scanner and imaging protocol variations: a review of harmonization methods. J. Pers. Med. 11, 842 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Chen, R. J. et al. Algorithmic fairness in artificial intelligence for medicine and healthcare. Nat. Biomed. Eng. 7, 719–742 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Salahuddin, Z., Woodruff, H. C., Chatterjee, A. & Lambin, P. Transparency of deep neural networks for medical image analysis: a review of interpretability methods. Comput. Biol. Med. 140, 105111 (2021).

    Article  PubMed  Google Scholar 

  17. Roschewitz, M. et al. Automatic correction of performance drift under acquisition shift in medical image classification. Nat. Commun. 14, 6608 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Martínez-Plumed, F., Gómez, E. & Hernández-Orallo, J. Futures of artificial intelligence through technology readiness levels. Telemat. Inform. 58, 101525 (2021).

    Article  Google Scholar 

  19. Hillis, J. M. et al. The lucent yet opaque challenge of regulating artificial intelligence in radiology. NPJ Digit. Med. 7, 69 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Veale, M. & Borgesius, F. Z. Demystifying the draft EU artificial intelligence act — analysing the good, the bad, and the unclear elements of the proposed approach. Comput. Law Rev. Int. 22, 97–112 (2021).

    Article  Google Scholar 

  21. Lekadir, K. et al. FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare. BMJ 388, e081554 (2025).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Mankins, J. C. Technology Readiness Levels: A White Paper (Office of Space Access and Technology, NASA, 6 April 1995).

  23. Kimmel, W. M. et al. Technology Readiness Assessment Best Practices Guide. NASA Special Publication SP-20205003605 (NASA, 30 June 2020).

  24. Lavin, A. et al. Technology readiness levels for machine learning systems. Nat. Commun. 13, 6039 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Kocak, B. 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 14, 75 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Kocak, B. 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. 33, 7556–7560 (2023).

    Article  PubMed  Google Scholar 

  27. Kocak, B. et al. METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII. Insights Imaging 15, 8 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  28. European Commission. Commission Regulation (EC) No 507/2006 of 29 March 2006 on the conditional marketing authorisation for medicinal products for human use falling within the scope of Regulation (EC) No 726/2004 of the European Parliament and of the Council. Off. J. Eur. Union 50, 6–9 (2006).

  29. Ab Latif, R., Mohamed, R., Dahlan, A. & Mat Nor, M. Z. Using Delphi technique: making sense of consensus in concept mapping structure and multiple choice questions (MCQ). Educ. Med. J. 8, 89–98 (2016).

    Article  Google Scholar 

  30. Cobo, M., Menéndez Fernández-Miranda, P., Bastarrika, G. & Lloret Iglesias, L. Enhancing radiomics and deep learning systems through the standardization of medical imaging workflows. Sci. Data 10, 732 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Wilkinson, M. D. et al. The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Sollini, M., Antunovic, L., Chiti, A. & Kirienko, M. Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics. Eur. J. Nucl. Med. Mol. Imaging 46, 2656–2672 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Zou, J. & Schiebinger, L. Ensuring that biomedical AI benefits diverse populations. eBioMedicine 67, 103358 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Galadima, H. et al. Machine learning as a tool for early detection: a focus on late-stage colorectal cancer across socioeconomic spectrums. Cancers 16, 540 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Hatt, M. et al. Joint EANM/SNMMI guideline on radiomics in nuclear medicine: jointly supported by the EANM physics committee and the SNMMI physics, instrumentation and data sciences council. Eur. J. Nucl. Med. Mol. Imaging 50, 352–375 (2023).

    Article  PubMed  CAS  Google Scholar 

  36. Chen, Y. et al. Robustness of CT radiomics features: consistency within and between single-energy CT and dual-energy CT. Eur. Radiol. 32, 5480–5490 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Haarburger, C. et al. Radiomics feature reproducibility under inter-rater variability in segmentations of CT images. Sci. Rep. 10, 12688 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Alsyed, E., Smith, R., Bartley, L., Marshall, C. & Spezi, E. A heterogeneous phantom study for investigating the stability of PET images radiomic features with varying reconstruction settings. Front. Nucl. Med. 3, 1078536 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Schwier, M. et al. Repeatability of multiparametric prostate MRI radiomics features. Sci. Rep. 9, 9441 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Zhang, J. et al. Comparing effectiveness of image perturbation and test retest imaging in improving radiomic model reliability. Sci. Rep. 13, 18263 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Tixier, F., Um, H., Young, R. J. & Veeraraghavan, H. Reliability of tumor segmentation in glioblastoma: impact on the robustness of MRI-radiomic features. Med. Phys. 46, 3582–3591 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Mahmood, U. et al. Quality control of radiomic features using 3D-printed CT phantoms. J. Med. Imaging 8, 033505 (2021).

    Article  Google Scholar 

  43. Demircioğlu, A. The effect of preprocessing filters on predictive performance in radiomics. Eur. Radiol. Exp. 6, 40 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Heidari, M. et al. Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms. Int. J. Med. Inform. 144, 104284 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Salvi, M., Acharya, U. R., Molinari, F. & Meiburger, K. M. The impact of pre- and post-image processing techniques on deep learning frameworks: a comprehensive review for digital pathology image analysis. Comput. Biol. Med. 128, 104129 (2021).

    Article  PubMed  Google Scholar 

  46. Zwanenburg, A. et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295, 328–338 (2020).

    Article  PubMed  Google Scholar 

  47. Whybra, P. et al. The image biomarker standardization initiative: standardized convolutional filters for reproducible radiomics and enhanced clinical insights. Radiology 310, e231319 (2024).

    Article  PubMed  Google Scholar 

  48. Bashyam, V. M. et al. Deep generative medical image harmonization for improving cross-site generalization in deep learning predictors. J. Magn. Reson. Imaging 55, 908–916 (2022).

    Article  PubMed  Google Scholar 

  49. Yang, S., Kim, E. Y. & Ye, J. C. Continuous conversion of CT kernel using switchable CycleGAN with AdaIN. IEEE Trans. Med. Imaging 40, 3015–3029 (2021).

    Article  PubMed  Google Scholar 

  50. Roca, V. et al. IGUANe: A 3D generalizable CycleGAN for multicenter harmonization of brain MR images. Med. Image Anal. 99, 103388 (2025).

    Article  PubMed  Google Scholar 

  51. Orlhac, F., Frouin, F., Nioche, C., Ayache, N. & Buvat, I. Validation of A method to compensate multicenter effects affecting CT radiomics. Radiology 291, 53–59 (2019).

    Article  PubMed  Google Scholar 

  52. Jin, J. et al. The accuracy and radiomics feature effects of multiple U-net-based automatic segmentation models for transvaginal ultrasound images of cervical cancer. J. Digit. Imaging 35, 983–992 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Primakov, S. P. et al. Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nat. Commun. 13, 3423 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Ma, J. et al. Segment anything in medical images. Nat. Commun. 15, 654 (2024).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Andrearczyk, V. et al. Automatic head and neck tumor segmentation and outcome prediction relying on FDG-PET/CT images: findings from the second edition of the HECKTOR challenge. Med. Image Anal. 90, 102972 (2023).

    Article  PubMed  Google Scholar 

  56. Salahuddin, Z. et al. From head and neck tumour and lymph node segmentation to survival prediction on PET/CT: an end-to-end framework featuring uncertainty, fairness, and multi-region multi-modal radiomics. Cancers 15, 1932 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Abu-Mostafa, Y. S., Magdon-Ismail, M. & Lin, H.-T. Learning from Data: A Short Course (AMLBook.com, 2012).

  58. Hua, J., Xiong, Z., Lowey, J., Suh, E. & Dougherty, E. R. Optimal number of features as a function of sample size for various classification rules. Bioinformatics 21, 1509–1515 (2005).

    Article  PubMed  CAS  Google Scholar 

  59. Roy, S. et al. Optimal co-clinical radiomics: sensitivity of radiomic features to tumour volume, image noise and resolution in co-clinical T1-weighted and T2-weighted magnetic resonance imaging. eBioMedicine 59, 102963 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Volpe, S. et al. Impact of image filtering and assessment of volume-confounding effects on CT radiomic features and derived survival models in non-small cell lung cancer. Transl. Lung Cancer Res. 11, 2452–2463 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Arthur, A. et al. A CT-based radiomics classification model for the prediction of histological type and tumour grade in retroperitoneal sarcoma (RADSARC-R): a retrospective multicohort analysis. Lancet Oncol. 24, 1277–1286 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Refaee, T. et al. Diagnosis of idiopathic pulmonary fibrosis in high-resolution computed tomography scans using a combination of handcrafted radiomics and deep learning. Front. Med. 9, 915243 (2022).

    Article  Google Scholar 

  63. Beuque, M. P. L. et al. Combining deep learning and handcrafted radiomics for classification of suspicious lesions on contrast-enhanced mammograms. Radiology 307, e221843 (2023).

    Article  PubMed  Google Scholar 

  64. Hatamikia, S. et al. Ovarian cancer beyond imaging: integration of AI and multiomics biomarkers. Eur. Radiol. Exp. 7, 50 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Kang, W. et al. Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis. J. Transl. Med. 21, 598 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Youden, W. J. Index for rating diagnostic tests. Cancer 3, 32–35 (1950).

    Article  PubMed  CAS  Google Scholar 

  67. Van Calster, B. et al. Calibration: the Achilles heel of predictive analytics. BMC Med. 17, 230 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Park, J. E., Park, S. Y., Kim, H. J. & Kim, H. S. Reproducibility and generalizability in radiomics modeling: possible strategies in radiologic and statistical perspectives. Korean J. Radiol. 20, 1124–1137 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Armato, S. G. 3rd, Drukker, K. & Hadjiiski, L. AI in medical imaging grand challenges: translation from competition to research benefit and patient care. Br. J. Radiol. 96, 20221152 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Vickers, A. J. & Elkin, E. B. Decision curve analysis: a novel method for evaluating prediction models. Med. Decis. Mak. 26, 565–574 (2006).

    Article  Google Scholar 

  71. Wang, H. et al. Radiomics-clinical model based on 99mTc-MDP SPECT/CT for distinguishing between bone metastasis and benign bone disease in tumor patients. J. Cancer Res. Clin. Oncol. 149, 13353–13361 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  72. Grossmann, P. et al. Defining the biological basis of radiomic phenotypes in lung cancer. eLife 6, e23421 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Zhang, G. et al. Biological and clinical significance of radiomics features obtained from magnetic resonance imaging preceding pre-carbon ion radiotherapy in prostate cancer based on radiometabolomics. Front. Endocrinol. 14, 1272806 (2023).

    Article  Google Scholar 

  74. Tomaszewski, M. R. & Gillies, R. J. The biological meaning of radiomic features. Radiology 299, E256 (2021).

    Article  PubMed  Google Scholar 

  75. Wang, Y. et al. The radiomic-clinical model using the SHAP method for assessing the treatment response of whole-brain radiotherapy: a multicentric study. Eur. Radiol. 32, 8737–8747 (2022).

    Article  PubMed  Google Scholar 

  76. Zhang, Y. et al. Grad-CAM helps interpret the deep learning models trained to classify multiple sclerosis types using clinical brain magnetic resonance imaging. J. Neurosci. Methods 353, 109098 (2021).

    Article  PubMed  Google Scholar 

  77. Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M. & Kim, B. Sanity checks for saliency maps. In Proc. Advances in Neural Information Processing Systems 31 (eds Bengio, S. et al.) (NeurIPS, Canada, 2018).

  78. Rudin, C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1, 206–215 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  79. Singla, S., Eslami, M., Pollack, B., Wallace, S. & Batmanghelich, K. Explaining the black-box smoothly-a counterfactual approach. Med. Image Anal. 84, 102721 (2023).

    Article  PubMed  Google Scholar 

  80. Baeza-Delgado, C. et al. A practical solution to estimate the sample size required for clinical prediction models generated from observational research on data. Eur. Radiol. Exp. 6, 22 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  81. Trofimova, A. V. & Bluemke, D. A. Prospective clinical trial registration: a prerequisite for publishing your results. Radiology 302, 1–2 (2022).

    Article  PubMed  Google Scholar 

  82. Badano, A. In silico imaging clinical trials: cheaper, faster, better, safer, and more scalable. Trials 22, 64 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  83. Abadi, E. et al. Virtual clinical trials in medical imaging: a review. J. Med. Imaging 7, 042805 (2020).

    Article  Google Scholar 

  84. Boverhof, B.-J. et al. Radiology AI deployment and assessment rubric (RADAR) to bring value-based AI into radiological practice. Insights Imaging 15, 34 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  85. Bodén, A. C. S. et al. The human-in-the-loop: an evaluation of pathologists’ interaction with artificial intelligence in clinical practice. Histopathology 79, 210–218 (2021).

    Article  PubMed  Google Scholar 

  86. Lewis, J. R. The system usability scale: past, present, and future. Int. J. Hum. Comput. Interact. 34, 577–590 (2018).

    Article  Google Scholar 

  87. Nielsen, J. Usability Engineering (Morgan Kaufmann, 1994).

  88. Di Pilla, A. et al. A cost-effectiveness analysis of an integrated clinical-radiogenomic screening program for the identification of BRCA 1/2 carriers (e-PROBE study). Sci. Rep. 14, 928 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  89. Jenkins, D. A. et al. Continual updating and monitoring of clinical prediction models: time for dynamic prediction systems? Diagn. Progn. Res. 5, 1 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  90. Feng, J. et al. Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare. NPJ Digit. Med. 5, 66 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  91. Lee, C. S. & Lee, A. Y. Clinical applications of continual learning machine learning. Lancet Digit. Health 2, e279–e281 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  92. Vokinger, K. N., Feuerriegel, S. & Kesselheim, A. S. Continual learning in medical devices: FDA’s action plan and beyond. Lancet Digit. Health 3, e337–e338 (2021).

    Article  PubMed  CAS  Google Scholar 

  93. Perkonigg, M. et al. Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging. Nat. Commun. 12, 1–12 (2021).

    Article  Google Scholar 

  94. Sorin, V. et al. Adversarial attacks in radiology - a systematic review. Eur. J. Radiol. 167, 111085 (2023).

    Article  PubMed  Google Scholar 

  95. Dong, J., Chen, J., Xie, X., Lai, J. & Chen, H. Adversarial attack and defense for medical image analysis: methods and applications. ACM Comput. Surv. 57, 79 (2024).

    Google Scholar 

  96. Sutherland, E. QMS Manual ISO9001 (Eric Sutherland T/A Trog Associates, 2007).

  97. Rust, P., Flood, D. & McCaffery, F. Creation of an IEC 62304 compliant software development plan. J. Softw. Evol. Process. 28, 1005–1010 (2016).

    Article  Google Scholar 

  98. Wu, E. et al. How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals. Nat. Med. 27, 582–584 (2021).

    Article  PubMed  CAS  Google Scholar 

  99. Muehlematter, U. J., Daniore, P. & Vokinger, K. N. Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015–20): a comparative analysis. Lancet Digit. Health 3, e195–e203 (2021).

    Article  PubMed  CAS  Google Scholar 

  100. Vemula, A. EU AI Act Explained: A Guide to the Regulation of Artificial Intelligence in Europe (Anand Vemula, 2024).

  101. Larson, D. B. et al. Regulatory frameworks for development and evaluation of artificial intelligence-based diagnostic imaging algorithms: summary and recommendations. J. Am. Coll. Radiol. 18, 413–424 (2021).

    Article  PubMed  Google Scholar 

  102. Aboy, M., Minssen, T. & Vayena, E. Navigating the EU AI Act: implications for regulated digital medical products. NPJ Digit. Med. 7, 1–6 (2024).

    Article  Google Scholar 

  103. Cohen, I. G., Evgeniou, T., Gerke, S. & Minssen, T. The European artificial intelligence strategy: implications and challenges for digital health. Lancet Digit. Health 2, e376–e379 (2020).

    Article  PubMed  Google Scholar 

  104. Pesapane, F., Volonté, C., Codari, M. & Sardanelli, F. Artificial intelligence as a medical device in radiology: ethical and regulatory issues in Europe and the United States. Insights Imaging 9, 745–753 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  105. Dong, Y. et al. Development and validation of novel radiomics-based nomograms for the prediction of EGFR mutations and Ki-67 proliferation index in non-small cell lung cancer. Quant. Imaging Med. Surg. 12, 2658–2671 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  106. Feng, L. et al. Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study. Lancet Digit. Health 4, e8–e17 (2022).

    Article  PubMed  CAS  Google Scholar 

  107. Cui, S., Ten Haken, R. K. & El Naqa, I. Integrating multiomics information in deep learning architectures for joint actuarial outcome prediction in non-small cell lung cancer patients after radiation therapy. Int. J. Radiat. Oncol. Biol. Phys. 110, 893–904 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  108. Fan, M. et al. Radiogenomic analysis of cellular tumor-stroma heterogeneity as a prognostic predictor in breast cancer. J. Transl. Med. 21, 851 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  109. Li, H. et al. Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set. NPJ Breast Cancer 2, 16012 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  110. Su, G.-H. et al. Radiogenomic-based multiomic analysis reveals imaging intratumor heterogeneity phenotypes and therapeutic targets. Sci. Adv. 9, eadf0837 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  111. Fan, M., Xia, P., Clarke, R., Wang, Y. & Li, L. Radiogenomic signatures reveal multiscale intratumour heterogeneity associated with biological functions and survival in breast cancer. Nat. Commun. 11, 4861 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  112. Wei, L. et al. Artificial intelligence (AI) and machine learning (ML) in precision oncology: a review on enhancing discoverability through multiomics integration. Br. J. Radiol. 96, 20230211 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  113. Jung, K.-H. Uncover this tech term: foundation model. Korean J. Radiol. 24, 1038–1041 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  114. Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J. & Zou, J. A visual-language foundation model for pathology image analysis using medical Twitter. Nat. Med. 29, 2307–2316 (2023).

    Article  PubMed  CAS  Google Scholar 

  115. Tiu, E. et al. Expert-level detection of pathologies from unannotated chest X-ray images via self-supervised learning. Nat. Biomed. Eng. 6, 1399–1406 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  116. Kirillov, A. et al. Segment anything. In Proc. IEEE/CVF International Conference on Computer Vision (ICCV), 3992–4003 (IEEE, France, 2023).

  117. Radford, A. et al. Learning transferable visual models from natural language supervision. In Proc. 38th International Conference on Machine Learning (eds. Meila, M. & Zhang, T.) 139, 8748–8763 (PMLR, 2021).

  118. Zhou, J. et al. Pre-trained multimodal large language model enhances dermatological diagnosis using SkinGPT-4. Nat. Commun. 15, 50043 (2024).

    Google Scholar 

  119. Zhang, K. et al. BiomedGPT: a unified and generalist biomedical generative pre-trained transformer for vision, language, and multimodal tasks. Preprint at https://doi.org/10.48550/arXiv.2305.17100 (2023).

  120. Wu, C., Zhang, X., Zhang, Y., Wang, Y. & Xie, W. Towards generalist foundation model for radiology by leveraging web-scale 2D&3D medical data. Preprint at https://doi.org/10.48550/arXiv.2308.02463 (2023).

  121. Pai, S. et al. Foundation model for cancer imaging biomarkers. Nat. Mach. Intell. 6, 354–367 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  122. Freeman, K. et al. Use of artificial intelligence for image analysis in breast cancer screening programmes: systematic review of test accuracy. BMJ 374, n1872 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  123. Collins, G. S. 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. 11, e048008 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  124. Sounderajah, V. et al. Developing specific reporting guidelines for diagnostic accuracy studies assessing AI interventions: the STARD-AI steering group. Nat. Med. 26, 807–808 (2020).

    Article  PubMed  CAS  Google Scholar 

  125. Vasey, B. et al. Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Nat. Med. 28, 924–933 (2022).

    Article  PubMed  CAS  Google Scholar 

  126. Liu, X. et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Lancet Digit. Health 2, e537–e548 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  127. Kazerouni, A. et al. Diffusion models in medical imaging: a comprehensive survey. Med. Image Anal. 88, 102846 (2023).

    Article  PubMed  Google Scholar 

  128. Amirrajab, S. et al. Label-informed cardiac magnetic resonance image synthesis through conditional generative adversarial networks. Comput. Med. Imaging Graph. 101, 102123 (2022).

    Article  PubMed  Google Scholar 

  129. Al Khalil, Y. et al. On the usability of synthetic data for improving the robustness of deep learning-based segmentation of cardiac magnetic resonance images. Med. Image Anal. 84, 102688 (2023).

    Article  PubMed  Google Scholar 

  130. Li, X., Cui, Z., Wu, Y., Gu, L. & Harada, T. Estimating and improving fairness with adversarial learning. Preprint at https://doi.org/10.48550/arXiv.2103.04243 (2021).

  131. Chen, R. J., Lu, M. Y., Chen, T. Y., Williamson, D. F. K. & Mahmood, F. Synthetic data in machine learning for medicine and healthcare. Nat. Biomed. Eng. 5, 493–497 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  132. Ktena, I. et al. Generative models improve fairness of medical classifiers under distribution shifts. Nat. Med. 30, 1166–1173 (2024).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  133. Onishi, Y. et al. Multiplanar analysis for pulmonary nodule classification in CT images using deep convolutional neural network and generative adversarial networks. Int. J. Comput. Assist. Radiol. Surg. 15, 173–178 (2020).

    Article  PubMed  Google Scholar 

  134. Liu, S. & Yap, P.-T. Learning multi-site harmonization of magnetic resonance images without traveling human phantoms. Commun. Eng. 3, 1–10 (2024).

    Article  Google Scholar 

  135. Osuala, R. et al. Data synthesis and adversarial networks: a review and meta-analysis in cancer imaging. Med. Image Anal. 84, 102704 (2023).

    Article  PubMed  Google Scholar 

  136. Rajotte, J.-F. et al. Synthetic data as an enabler for machine learning applications in medicine. iScience 25, 105331 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  137. Carlini, N. et al. Extracting training data from diffusion models. In Proc. 32nd USENIX Security Symposium 5253–5270 (USENIX Security, USA, 2023).

  138. Sun, C., van Soest, J. & Dumontier, M. Generating synthetic personal health data using conditional generative adversarial networks combining with differential privacy. J. Biomed. Inform. 143, 104404 (2023).

    Article  PubMed  Google Scholar 

  139. Lyu, Q. & Wang, G. Conversion between CT and MRI images using diffusion and score-matching models. Preprint at https://doi.org/10.48550/arXiv.2209.12104 (2022).

  140. Liu, F., Jang, H., Kijowski, R., Bradshaw, T. & McMillan, A. B. Deep learning MR imaging-based attenuation correction for PET/MR imaging. Radiology 286, 676–684 (2018).

    Article  PubMed  Google Scholar 

  141. Huo, Y. et al. SynSeg-net: synthetic segmentation without target modality ground truth. IEEE Trans. Med. Imaging 38, 1016–1025 (2018).

    Article  Google Scholar 

  142. Conte, G. M. et al. Generative adversarial networks to synthesize missing T1 and FLAIR MRI sequences for use in a multisequence brain tumor segmentation model. Radiology 299, 313–323 (2021).

    Article  PubMed  Google Scholar 

  143. Wolterink, J. M., Kamnitsas, K., Ledig, C. & Išgum, I. in Handbook of Medical Image Computing and Computer Assisted Intervention (eds Zhou, S. K., Rueckert, D. & Fichtinger, G.) 547–574 (Academic Press, 2020).

  144. Ibrahim, M. et al. Generative AI for synthetic data across multiple medical modalities: a systematic review of recent developments and challenges. Comput. Biol. Med. 189, 109834 (2025).

    Article  PubMed  Google Scholar 

  145. Kaissis, G. A., Makowski, M. R., Rückert, D. & Braren, R. F. Secure, privacy-preserving and federated machine learning in medical imaging. Nat. Mach. Intell. 2, 305–311 (2020).

    Article  Google Scholar 

  146. Liu, J. et al. From distributed machine learning to federated learning: a survey. Knowl. Inf. Syst. 64, 885–917 (2022).

    Article  Google Scholar 

  147. Darzidehkalani, E., Ghasemi-Rad, M. & van Ooijen, P. M. A. Federated learning in medical imaging: part I: toward multicentral health care ecosystems. J. Am. Coll. Radiol. 19, 969–974 (2022).

    Article  PubMed  Google Scholar 

  148. Dayan, I. et al. Federated learning for predicting clinical outcomes in patients with COVID-19. Nat. Med. 27, 1735–1743 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  149. Sheller, M. J. et al. Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Sci. Rep. 10, 12598 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  150. Zhao, Y. et al. Federated learning with non-IID data. Preprint at https://doi.org/10.48550/arXiv.1806.00582 (2018).

  151. Zhang, Y. et al. The secret revealer: generative model-inversion attacks against deep neural networks. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 250–258 (IEEE, USA, 2020).

  152. Park, R. W. Sharing clinical big data while protecting confidentiality and security: observational health data sciences and informatics. Healthc. Inform. Res. 23, 1–3 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  153. Callahan, T. J. et al. Ontologizing health systems data at scale: making translational discovery a reality. NPJ Digit. Med. 6, 89 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  154. Park, C. et al. Development and validation of the radiology common data model (R-CDM) for the international standardization of medical imaging data. Yonsei Med. J. 63, S74–S83 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  155. Fedorov, A. et al. DICOM re-encoding of volumetrically annotated lung imaging database consortium (LIDC) nodules. Med. Phys. 47, 5953–5965 (2020).

    Article  PubMed  Google Scholar 

  156. Levy, M. A. et al. Informatics methods to enable sharing of quantitative imaging research data. Magn. Reson. Imaging 30, 1249–1256 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  157. Shin, S. J. et al. Genomic common data model for seamless interoperation of biomedical data in clinical practice: retrospective study. J. Med. Internet Res. 21, e13249 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  158. Stanzione, A. et al. Prostate MRI radiomics: a systematic review and radiomic quality score assessment. Eur. J. Radiol. 129, 109095 (2020).

    Article  PubMed  Google Scholar 

  159. Sun, Y. et al. Automatic stratification of prostate tumour aggressiveness using multiparametric MRI: a horizontal comparison of texture features. Acta Oncol. 58, 1118–1126 (2019).

    Article  PubMed  Google Scholar 

  160. Zhong, J. et al. A systematic review of radiomics in osteosarcoma: utilizing radiomics quality score as a tool promoting clinical translation. Eur. Radiol. 31, 1526–1535 (2021).

    Article  PubMed  Google Scholar 

  161. Lin, P. et al. A Delta-radiomics model for preoperative evaluation of Neoadjuvant chemotherapy response in high-grade osteosarcoma. Cancer Imaging 20, 7 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  162. Spadarella, G. et al. MRI based radiomics in nasopharyngeal cancer: systematic review and perspectives using radiomic quality score (RQS) assessment. Eur. J. Radiol. 140, 109744 (2021).

    Article  PubMed  Google Scholar 

  163. Zhang, L.-L. et al. Pretreatment MRI radiomics analysis allows for reliable prediction of local recurrence in non-metastatic T4 nasopharyngeal carcinoma. eBioMedicine 42, 270–280 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  164. Mühlbauer, J. et al. Radiomics in renal cell carcinoma-a systematic review and meta-analysis. Cancers 13, 1348 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  165. Li, Z.-C. et al. Differentiation of clear cell and non-clear cell renal cell carcinomas by all-relevant radiomics features from multiphase CT: a VHL mutation perspective. Eur. Radiol. 29, 3996–4007 (2019).

    Article  PubMed  Google Scholar 

  166. Brancato, V., Cerrone, M., Lavitrano, M., Salvatore, M. & Cavaliere, C. A systematic review of the current status and quality of radiomics for glioma differential diagnosis. Cancers 14, 2731 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  167. Chen, Y. et al. Primary central nervous system lymphoma and glioblastoma differentiation based on conventional magnetic resonance imaging by high-throughput SIFT features. Int. J. Neurosci. 128, 608–618 (2018).

    Article  PubMed  CAS  Google Scholar 

  168. Zhong, X. et al. Radiomics models for preoperative prediction of microvascular invasion in hepatocellular carcinoma: a systematic review and meta-analysis. Abdom. Radiol. 47, 2071–2088 (2022).

    Article  Google Scholar 

  169. He, M. et al. Radiomic feature-based predictive model for microvascular invasion in patients with hepatocellular carcinoma. Front. Oncol. 10, 574228 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  170. Tabnak, P., HajiEsmailPoor, Z., Baradaran, B., Pashazadeh, F. & Aghebati Maleki, L. MRI-based radiomics methods for predicting Ki-67 expression in breast cancer: a systematic review and meta-analysis. Acad. Radiol. 31, 763–787 (2023).

    Article  PubMed  Google Scholar 

  171. Liu, W. et al. Preoperative prediction of Ki-67 status in breast cancer with multiparametric MRI using transfer learning. Acad. Radiol. 28, e44–e53 (2021).

    Article  PubMed  Google Scholar 

  172. Huang, M.-L. et al. A systematic review and meta-analysis of CT and MRI radiomics in ovarian cancer: methodological issues and clinical utility. Insights Imaging 14, 117 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  173. Song, X.-L., Ren, J.-L., Yao, T.-Y., Zhao, D. & Niu, J. Radiomics based on multisequence magnetic resonance imaging for the preoperative prediction of peritoneal metastasis in ovarian cancer. Eur. Radiol. 31, 8438–8446 (2021).

    Article  PubMed  Google Scholar 

  174. Felfli, M. et al. Systematic review, meta-analysis and radiomics quality score assessment of CT radiomics-based models predicting tumor EGFR mutation status in patients with non-small-cell lung cancer. Int. J. Mol. Sci. 24, 11433 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  175. Boca, B. et al. MRI-based radiomics in bladder cancer: a systematic review and radiomics quality score assessment. Diagnostics 13, 2300 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  176. Li, L. et al. An MRI-based radiomics nomogram in predicting histologic grade of non-muscle-invasive bladder cancer. Front. Oncol. 13, 1025972 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  177. Zheng, J. et al. Development of a noninvasive tool to preoperatively evaluate the muscular invasiveness of bladder cancer using a radiomics approach. Cancer 125, 4388–4398 (2019).

    Article  PubMed  Google Scholar 

  178. Jia, L.-L., Zhao, J.-X., Zhao, L.-P., Tian, J.-H. & Huang, G. Current status and quality of radiomic studies for predicting KRAS mutations in colorectal cancer patients: a systematic review and meta-analysis. Eur. J. Radiol. 158, 110640 (2023).

    Article  PubMed  Google Scholar 

  179. Xue, T. et al. Preoperative prediction of KRAS mutation status in colorectal cancer using a CT-based radiomics nomogram. Br. J. Radiol. 95, 20211014 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  180. Cawley, G. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. J. Mach. Learn. Res. 11, 2079–2107 (2010).

    Google Scholar 

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Acknowledgements

Some of the authors acknowledge financial support from the European Union’s Horizon research and innovation programme under the following grant agreements: AIDAVA (HORIZON-HLTH-2021-TOOL-06) (grant no. 101057062 to P.L. and S.A.), CHAIMELEON (grant no. 952172 to P.L., H.C.K., S.A.M. and L.M-B.), EUCAIM (DIGITAL-2022-CLOUD-AI-02) (grant no. 101100633 to P.L., L.M-B. and S.A.), EuCanImage (grant no. 952103 to P.L., H.C.W., S.A.M., H.K., K.L. and Z.S.), GLIOMATCH (grant no. 101136670 to P.L. and Z.S.), IMI-OPTIMA (grant no. 101034347), ImmunoSABR (grant no. 733008) and REALM (HORIZON-HLTH-2022-TOOL-11) (grant no. 101095435 to P.L.), and RADIOVAL (HORIZON-HLTH-2021-DISEASE-04-04) (grant no. 101057699 to P.L., K.L. and S.A.). The research of X.Z. is partially supported by the Guangzhou basic and applied basic research foundation (grant no. SL2023A04J02221). The research of H.C.W. and S.K. is partially supported by the Dutch Cancer Society (KWF Kankerbestrijding) (project no. 2021-PoC/14449). The research of P.E.K. is supported by NIH (grant nos. R01CA258298 and U24CA264044).

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P.L., H.C.W., S.A.M., X.Z., S.K., E.L., H.K., S.A. and Z.S. researched data for the article. All authors contributed substantially to discussion of the content, wrote, reviewed and/or edited the manuscript before submission.

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Correspondence to Philippe Lambin.

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P.L. has received grants and sponsored research agreements from Convert Pharmaceuticals SA, LivingMed Biotech srl and Radiomics SA; has received presenter fees and/or reimbursement of travel costs or consultancy fees (all in cash or in kind) from AstraZeneca, BHV srl and Roche; holds or has held minority shares in Bactam srl, Convert Pharmaceuticals SA, Comunicare SA, LivingMed Biotech srl and Radiomics SA; is a co-inventor on two issued patents with royalties on radiomics (PCT/NL2014/050248 and PCT/NL2014/050728) licensed to Radiomics SA, one issued patent on mtDNA (PCT/EP2014/059089) licensed to ptTheragnostic/DNAmito, one granted patent on LSRT (PCT/P126537PC00, US patent no. 12,102,842) licensed to Varian, one issued patent on prodrugs (WO2019EP64112) without royalties, one non-issued, non-licensed patent on deep learning radiomics (N2024889) and three non-patented inventions (software) licensed to Health Innovation Ventures, ptTheragnostic/DNAmito and Radiomics SA. P.L. confirms that none of these disclosures are related to the current manuscript and none of the above entities were involved in the preparation of this Review. H.C.W. owns minority shares in Radiomics SA, and confirms that this entity was not involved in the preparation of this manuscript. All other authors declare no competing interests.

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Lambin, P., Woodruff, H.C., Mali, S.A. et al. Radiomics Quality Score 2.0: towards radiomics readiness levels and clinical translation for personalized medicine. Nat Rev Clin Oncol (2025). https://doi.org/10.1038/s41571-025-01067-1

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