Whole-body CT attenuation and volume charts from routine clinical scans via evidence-grounded LLM report filtering

• URL: http://arxiv.org/abs/2605.05933v1
• Date: Thu, 07 May 2026 09:40:01 GMT
• Title: Whole-body CT attenuation and volume charts from routine clinical scans via evidence-grounded LLM report filtering
• Abstract summary: We develop an evidence-grounded, cross-verified large language model ensemble to filter pathological findings from radiology reports.<n>Five LLMs, first, flag structure-level abnormality candidates grounded in verbatim report evidence.<n>We establish comprehensive whole-body reference charts for 106 anatomical structures across adulthood, accounting for age, sex, contrast enhancement, and acquisition parameters.
• Score: 5.261511162432881
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Interpreting quantitative CT biomarkers, such as organ volume and tissue attenuation, requires large-scale healthy reference distributions. However, creating these is challenging because clinical datasets are often heavily enriched with pathology. Here, we develop an evidence-grounded, cross-verified large language model (LLM) ensemble to filter pathological findings from radiology reports, enabling the construction of pathology-reduced cohorts from over 350,000 CT examinations. Five LLMs, first, flag structure-level abnormality candidates grounded in verbatim report evidence and, second, resolve disagreements via cross-verification. Using distribution-aware generalized additive models for location, scale, and shape, we establish comprehensive whole-body reference charts for 106 anatomical structures (volumes and attenuation) across adulthood, accounting for age, sex, contrast enhancement, and acquisition parameters. Longitudinal analyses reveal structure- and contrast-dependent changes distinct from cross-sectional trends. These resources facilitate covariate-adjusted centile scoring from routine CT, supporting standardized quantitative phenotyping, multi-site imaging studies, and scalable opportunistic screening research.

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