Whole body CT attenuation and volume charts from routine clinical scans via LLM report filtering.

Authors

Christian Wachinger, Bernhard Renger, Christopher Späth, Jan Kirschke, Marcus Makowski

Published in

NPJ digital medicine. Volume 9. Issue 1. Jul 03, 2026. Epub Jul 03, 2026.

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.

PMID:
42399663
Bibliographic data and abstract were imported from PubMed on 04 Jul 2026.

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