Demircioğlu Aydin
Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany.
Eur Radiol Exp. 2025 Sep 4;9(1):85. doi: 10.1186/s41747-025-00629-3.
In radiomics, features are often linked to biomarkers and are generally expected to be reproducible, as reproducibility is considered a prerequisite for developing predictive models in clinical applications. However, this perspective overlooks feature interactions and may underestimate the potential value of nonreproducible features. Through experiments simulating a test-retest scenario, we demonstrate that even non-reproducible features can contribute significantly to predictive performance. Removing these features can lower model accuracy. These findings suggest that the emphasis on feature reproducibility should be reconsidered and that features should not be evaluated in isolation. Underlying information can be spread across multiple features. Focusing on individual features ignores feature interactions and may limit the model's predictive power. Ultimately, radiomics must prioritize prediction and clinical relevance. KEY POINTS: Feature reproducibility assessments often ignore feature interactions, overlooking predictive performance. Feature reproducibility depends on subjective thresholds, chosen metrics, and sample size. Nonreproducible features can be more predictive than reproducible ones. Predictive information may be distributed across multiple features rather than confined to individual ones.
在放射组学中，特征通常与生物标志物相关联，并且一般认为具有可重复性，因为可重复性被视为在临床应用中开发预测模型的先决条件。然而，这种观点忽略了特征间的相互作用，可能低估了不可重复特征的潜在价值。通过模拟重测场景的实验，我们证明即使是不可重复的特征也能对预测性能做出显著贡献。去除这些特征会降低模型准确性。这些发现表明，应重新考虑对特征可重复性的强调，并且不应孤立地评估特征。潜在信息可能分布在多个特征中。关注单个特征会忽略特征间的相互作用，并可能限制模型的预测能力。最终，放射组学必须优先考虑预测性和临床相关性。要点：特征可重复性评估常常忽略特征间的相互作用，从而忽视预测性能。特征可重复性取决于主观阈值、所选指标和样本大小。不可重复的特征可能比可重复的特征更具预测性。预测信息可能分布在多个特征中，而非局限于单个特征。
