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<p>Over the past decade, radiomics has seen exponential growth, with over ten thousand publications in PubMed and a steady increase in related studies in journals like <i>Abdominal Radiology</i>. Despite the potential of radiomics, a major challenge lies in validating radiomics models, as most studies rely on single-center datasets with fixed-ratio splits, which can lead to variability in performance due to randomness in data splitting. Therefore, researchers should adopt more robust cross-validation methods rather than relying solely on the fixed-ratio holdout method to ensure robust and reliable radiomics model performance evaluation.</p>

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The pitfalls of fixed-ratio data splitting in radiomics model performance evaluation

  • 期刊论文

  • 发表日期：

    2025年4月10日

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  • Haoru Wang

摘要

Over the past decade, radiomics has seen exponential growth, with over ten thousand publications in PubMed and a steady increase in related studies in journals like Abdominal Radiology. Despite the potential of radiomics, a major challenge lies in validating radiomics models, as most studies rely on single-center datasets with fixed-ratio splits, which can lead to variability in performance due to randomness in data splitting. Therefore, researchers should adopt more robust cross-validation methods rather than relying solely on the fixed-ratio holdout method to ensure robust and reliable radiomics model performance evaluation.