Abstract
Most papers caution against using predictive models for disease stratification based on unselected radiomic features, as these features are affected by contouring variability. Instead, they advocate for the use of the Intraclass Correlation Coefficient (ICC) as a measure of stability for feature selection. However, the direct effect of segmentation variability on the predictive models is rarely studied. This study investigates the impact of segmentation variability on feature stability and predictive performance in radiomics-based prediction of Triple-Negative Breast Cancer (TNBC) subtype using Magnetic Resonance Imaging. A total of 244 images from the Duke dataset were used, with segmentation variability introduced through modifications of manual segmentations. For each mask, explainable radiomic features were selected using the Shapley Additive exPlanations method and used to train logistic regression models. Feature stability across segmentations was assessed via ICC, Pearson's correlation, and reliability scores quantifying the relationship between feature stability and segmentation variability. Results indicate that segmentation accuracy does not significantly impact predictive performance. While incorporating peritumoral information may reduce feature reproducibility, it does not diminish feature predictive capability. Moreover, feature selection in predictive models is not inherently tied to feature stability with respect to segmentation, suggesting that an overreliance on ICC or reliability scores for feature selection might exclude valuable predictive features.
Abstract (translated)
大多数论文警告不要基于未选择的放射组学特征来使用预测模型进行疾病分层，因为这些特征会受到轮廓变异的影响。相反，它们提倡将组内相关系数（Intraclass Correlation Coefficient, ICC）作为特征选择稳定性的衡量标准。然而，分割变异性对预测模型的直接影响却很少被研究。这项研究探讨了分割变异性对基于放射影像预测三阴性乳腺癌（TNBC）亚型时特性稳定性和预测性能的影响。总共使用了杜克数据集中244张图像，并通过修改手动分割引入了分割变异性。对于每种掩模，利用Shapley Additive exPlanations方法选择可解释的放射组学特征并用于训练逻辑回归模型。通过对ICC、Pearson相关性以及量化特性稳定性和分割变异性的关系可靠评分进行评估，来评估不同分割下的特征稳定性。结果显示，分割准确性对预测性能没有显著影响。虽然纳入肿瘤周围的组织信息可能会降低特征再现性，但这并不会削弱其预测能力。此外，在预测模型中选择的特征并不必然与针对分割的稳定性相关联，这表明过于依赖ICC或可靠评分进行特征选择可能排除了有价值的预测特性。
URL
https://arxiv.org/abs/2504.01692
