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Editorial
. 2025 Sep;35(9):5226-5227.
doi: 10.1007/s00330-025-11538-9. Epub 2025 Mar 27.

Sample size estimation for radiomics studies: an overlooked problem

Michail E Klontzas  1   2   3 , Burak Kocak  4 , Renato Cuocolo  5
Affiliations
Editorial

Sample size estimation for radiomics studies: an overlooked problem

Michail E Klontzas et al. Eur Radiol. 2025 Sep.
No abstract available

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Conflict of interest statement

Compliance with ethical standards. Guarantor: The scientific guarantor of this publication is Michail E. Klontzas. Conflict of interest: The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article. All authors are members of the editorial board of European Radiology (sections: M.K.; musculoskeletal, B.K. and R.C.; imaging informatics and artificial intelligence) but have not participated in the review or decision process for this manuscript. Statistics and biometry: No complex statistical methods were necessary for this paper. Informed consent: Written informed consent was not required for this study because it is an editorial. Ethical approval: Institutional Review Board approval was not required because it is an editorial comment. Study subjects or cohorts overlap: Not applicable. Methodology: Commentary

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References

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    1. Riley RD, Snell KIE, Ensor J et al (2019) Minimum sample size for developing a multivariable prediction model: Part I—Continuous outcomes. Stat Med 38:1262–1275 - PubMed
    1. Riley RD, Snell KI, Ensor J et al (2019) Minimum sample size for developing a multivariable prediction model: PART II—Binary and time-to-event outcomes. Stat Med 38:1276–1296 - PubMed
    1. Archer L, Snell KIE, Ensor J et al (2021) Minimum sample size for external validation of a clinical prediction model with a continuous outcome. Stat Med 40:133–146 - PubMed

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