筛选抗协议差异的影像组学特征，提升临床模型在真实场景下的稳定性。
Machine Learning based Analysis for Radiomics Features Robustness in Real-World Deployment Scenarios
- 基于16种水果幻影数据，测试五种MRI序列与分割方式的影响。
- 用不变特征训练模型，跨协议仍保持F1>0.85，全特征模型降40%性能。
- 数据增强显著改善不确定性估计，降低35%校准误差，适合临床部署。
基于影像组学的机器学习模型在临床决策支持中展现潜力，但易受成像协议、定位和分割差异导致的分布偏移影响。本研究系统评估了五种MRI序列下影像组学模型的鲁棒性。通过16种水果幻影，考察了T2-HASTE、T2-TSE、T2-MAP、T1-TSE、T2-FLAIR序列间的协议变化、分割方式（全、部分、旋转）及观察者间差异。使用XGBoost分类器对比8个稳定特征与序列特异性特征，在域内与域外条件下测试表现。结果表明，基于协议无关特征训练的模型在分布偏移下仍保持F1-score >0.85，而使用全部特征的模型在协议变化时性能下降40%。数据增强显著提升不确定性估计质量，使预期校准误差（ECE）降低35%，且不损失准确率。温度缩放仅带来微弱校准改善，验证了XGBoost的内在可靠性。研究揭示，协议感知特征选择与受控幻影实验可有效预测模型在分布偏移下的行为，为构建抗真实世界协议变化的鲁棒影像组学模型提供框架。
原文摘要 · Abstract (English)
Radiomics-based machine learning models show promise for clinical decision support but are vulnerable to distribution shifts caused by variations in imaging protocols, positioning, and segmentation. This study systematically investigates the robustness of radiomics-based machine learning models under distribution shifts across five MRI sequences. We evaluated how different acquisition protocols and segmentation strategies affect model reliability in terms of predictive power and uncertainty-awareness. Using a phantom of 16 fruits, we evaluated distribution shifts through: (1) protocol variations across T2-HASTE, T2-TSE, T2-MAP, T1-TSE, and T2-FLAIR sequences; (2) segmentation variations (full, partial, rotated); and (3) inter-observer variability. We trained XGBoost classifiers on 8 consistent robust features versus sequence-specific features, testing model performance under in-domain and out-of-domain conditions. Results demonstrate that models trained on protocol-invariant features maintain F1-scores >0.85 across distribution shifts, while models using all features showed 40% performance degradation under protocol changes. Dataset augmentation substantially improved the quality of uncertainty estimates and reduced the expected calibration error (ECE) by 35% without sacrificing accuracy. Temperature scaling provided minimal calibration benefits, confirming XGBoost's inherent reliability. Our findings reveal that protocol-aware feature selection and controlled phantom studies effectively predict model behavior under distribution shifts, providing a framework for developing robust radiomics models resilient to real-world protocol variations.
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