CT-IDP: Segmentation-Derived Quantitative Phenotypes for Interpretable Abdominal CT Disease Classification

• URL: http://arxiv.org/abs/2605.09002v1
• Date: Sat, 09 May 2026 15:39:02 GMT
• Title: CT-IDP: Segmentation-Derived Quantitative Phenotypes for Interpretable Abdominal CT Disease Classification
• Abstract summary: A quantitative phenotyping framework, CT-IDP (CT Image-Derived Phenotypes) was developed.<n>CT-IDP was externally evaluated on two independent dataset: Duke-Abdomen (2,000) and AMOS (1,107)<n>Performance was compared against a DINOv3-based vision-transformer baseline using AUC and average precision (AP)
• Score: 1.0408458647051342
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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