用器官特征+轻量模型自动区分CT造影阶段，跨机构效果稳定。
Segment-and-Classify: ROI-Guided Generalizable Contrast Phase Classification in CT Using XGBoost
- 基于器官分割特征与XGBoost分类器实现造影相位识别
- 在多个数据集上AUC超0.937，动脉期F1达0.937
- 模型轻量且泛化性强，适合临床部署
目的：利用来自常用分割工具的器官特异性特征，结合轻量级决策树分类器，自动化CT造影相位分类。方法：本回顾性研究使用三个独立机构的公开CT数据集。相位预测模型在WAW-TACE数据集（中位年龄66岁[60,73]；185名男性）上训练，并在VinDr-Multiphase（146名男性；63名女性；56名未知）和C4KC-KiTS（中位年龄61岁[50.68]；123名男性）数据集上进行外部验证。通过TotalSegmentator提取器官特异性特征，再使用梯度提升决策树分类器进行预测。结果：在VinDr-Multiphase数据集上，模型在所有相位的AUC均超过0.937，非对比期（F1=0.994）、动脉期（F1=0.937）和延迟期（F1=0.718）表现优异；统计检验显示动脉期与延迟期性能差异显著（p<0.05）。在C4KC-KiTS数据集上，所有相位的AUC均超过0.991，动脉/静脉期（F1=0.968）和延迟期（F1=0.935）优于基线模型，且差异显著（p<0.05）。非对比相位性能各模型间无显著差异（p>0.05）。结论：该轻量模型相较所有基线模型表现更优，且在不同机构数据集上具备强泛化能力。
原文摘要 · Abstract (English)
Purpose: To automate contrast phase classification in CT using organ-specific features extracted from a widely used segmentation tool with a lightweight decision tree classifier. Materials and Methods: This retrospective study utilized three public CT datasets from separate institutions. The phase prediction model was trained on the WAW-TACE (median age: 66 [60,73]; 185 males) dataset, and externally validated on the VinDr-Multiphase (146 males; 63 females; 56 unk) and C4KC-KiTS (median age: 61 [50.68; 123 males) datasets. Contrast phase classification was performed using organ-specific features extracted by TotalSegmentator, followed by prediction using a gradient-boosted decision tree classifier. Results: On the VinDr-Multiphase dataset, the phase prediction model achieved the highest or comparable AUCs across all phases (>0.937), with superior F1-scores in the non-contrast (0.994), arterial (0.937), and delayed (0.718) phases. Statistical testing indicated significant performance differences only in the arterial and delayed phases (p<0.05). On the C4KC-KiTS dataset, the phase prediction model achieved the highest AUCs across all phases (>0.991), with superior F1-scores in arterial/venous (0.968) and delayed (0.935) phases. Statistical testing confirmed significant improvements over all baseline models in these two phases (p<0.05). Performance in the non-contrast class, however, was comparable across all models, with no statistically significant differences observed (p>0.05). Conclusion: The lightweight model demonstrated strong performance relative to all baseline models, and exhibited robust generalizability across datasets from different institutions.
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