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
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Radiomics is a quantitative image analysis method that enhances disease diagnosis, tumour characterization and prediction of treatment response, supporting its application in precision oncology.
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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.
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Radiomics approaches can be broadly categorized into handcrafted and deep learning-based methods, each with distinct workflows, challenges and requirements for clinical translation.
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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.
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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.
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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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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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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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DOI: https://doi.org/10.1038/s41571-025-01067-1