从腹部CT中提取900多个定量表型，提升疾病分类可解释性。
CT-IDP: Segmentation-Derived Quantitative Phenotypes for Interpretable Abdominal CT Disease Classification
- 基于多器官分割生成900+形态、密度和负担特征
- 在3个数据集上表现优于视觉模型，宏AUC最高达0.897
- 适合需要可解释医学影像分析的临床研究者
本回顾性多中心研究在MERLIN腹部CT基准数据集（训练、验证、测试集分别为15,175、5,018、5,082例）上构建了定量表型框架CT-IDP，通过TotalSegmentator进行多器官分割，生成超过900个器官及腔室级描述符，涵盖形态学、衰减度与上下文/负荷发现。采用稀疏疾病特异性逻辑回归结合弹性网络正则化，在MERLIN上训练并以冻结参数外部验证。性能对比基于DINOv3视觉变压器基线，使用AUC与平均精度（AP）评估，并辅以表型分层审计与系数层面检查。在MERLIN上CT-IDP与基线的宏AUC分别为0.897与0.880；在Duke-Abdomen上为0.877与0.857；在AMOS上为0.780与0.756。
原文摘要 · Abstract (English)
In this retrospective multi-institutional study, a quantitative phenotyping framework, CT-IDP (CT Image-Derived Phenotypes) was developed on the MERLIN abdominal CT benchmark (training, validation, and test sets- 15,175, 5,018, and 5,082 studies, respectively) and externally evaluated on two independent dataset: Duke-Abdomen (2,000) and AMOS (1,107). Multi-organ segmentations were generated with TotalSegmentator and used to derive over 900 organ and compartment-level descriptors spanning morphometry, attenuation, and contextual/burden findings. Sparse disease-specific logistic regression with elastic-net regularization was trained on MERLIN and externally validated under a frozen specification. Performance was compared against a DINOv3-based vision-transformer baseline using AUC and average precision (AP), supported by phenotype-stratified audits and coefficient-level inspection. Macro-AUC for CT-IDP versus the baseline was 0.897 versus 0.880 on MERLIN, 0.877 versus 0.857 on the Duke-Abdomen dataset, and 0.780 versus 0.756 on AMOS.
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