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Minimum sample size calculation for radiomics-based binary outcome prediction models: Theoretical framework and

Qian Cao1, Zhaoyu Jiang1, Zhixiang Wang2

  • 1Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China; School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China.

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Summary

This study introduces a structured method and online tool for calculating optimal sample sizes in radiomics research. This approach minimizes overfitting and enhances the reliability of binary outcome prediction models.

Keywords:
Binary outcomeLogistic regressionPrediction modelRadiomicsSample size

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Area of Science:

  • Medical Imaging
  • Biostatistics
  • Machine Learning

Background:

  • Determining adequate sample size for radiomics models is crucial but challenging.
  • Identifying the maximum number of predictors for a fixed dataset size is also critical.
  • Current methods often rely on heuristic approaches, lacking rigor.

Purpose of the Study:

  • To propose and demonstrate a structured method for sample size calculation in radiomics.
  • To address challenges in developing robust radiomics-based binary outcome prediction models.
  • To enhance the methodological rigor and practicality of radiomics research.

Main Methods:

  • Introduced a sample size calculation framework for binary outcome prediction models.
  • Integrated three criteria: global shrinkage factor (S) ≥ 0.9, minimal performance metric difference, and outcome risk estimation.
  • Developed an accessible online tool for determining minimum sample size or maximum predictors.

Main Results:

  • The method systematically addresses model overfitting using a global shrinkage factor.
  • Provided robust estimates compared to traditional heuristic approaches.
  • Demonstrated effective balancing of predictive accuracy and generalizability through practical examples.

Conclusions:

  • Justifying sample size decisions is essential for reliable radiomics predictive models.
  • The structured method minimizes overfitting and ensures accurate risk estimation.
  • Adopting this rigorous approach enhances the reliability and validity of radiomics models.