Whole body CT attenuation and volume charts from routine clinical scans via LLM report filtering.
Authors
Affiliations (5)
Affiliations (5)
- Institute of Radiology, School of Medicine and Health, Technical University of Munich, Munich, Germany. [email protected].
- Munich Data Science Institute (MDSI), Technical University of Munich, Munich, Germany. [email protected].
- Munich Center for Machine Learning (MCML), Munich, Germany. [email protected].
- Institute of Radiology, School of Medicine and Health, Technical University of Munich, Munich, Germany.
- Institute of Neuroradiology, School of Medicine and Health, Technical University of Munich, Munich, Germany.
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.