{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T14:22:19Z","timestamp":1783088539697,"version":"3.54.6"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T00:00:00Z","timestamp":1783036800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T00:00:00Z","timestamp":1783036800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>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.<\/jats:p>","DOI":"10.1038\/s41746-026-02938-2","type":"journal-article","created":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T13:26:07Z","timestamp":1783085167000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Whole body CT attenuation and volume charts from routine clinical scans via LLM report filtering"],"prefix":"10.1038","volume":"9","author":[{"given":"Christian","family":"Wachinger","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bernhard","family":"Renger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher","family":"Sp\u00e4th","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jan","family":"Kirschke","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcus","family":"Makowski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,3]]},"reference":[{"key":"2938_CR1","doi-asserted-by":"publisher","first-page":"e230024","DOI":"10.1148\/ryai.230024","volume":"5","author":"J Wasserthal","year":"2023","unstructured":"Wasserthal, J. et al. TotalSegmentator: robust segmentation of 104 anatomic structures in CT images. Radiol. Artif. Intell. 5, e230024 (2023).","journal-title":"Radiol. Artif. Intell."},{"key":"2938_CR2","doi-asserted-by":"publisher","first-page":"623","DOI":"10.2214\/AJR.09.2590","volume":"194","author":"CJ Boyce","year":"2010","unstructured":"Boyce, C. J. et al. Hepatic Steatosis (Fatty liver disease) in asymptomatic adults identified by unenhanced low-dose CT. Am. J. Roentgenol. 194, 623\u2013628 (2010).","journal-title":"Am. J. Roentgenol."},{"key":"2938_CR3","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1148\/radiol.2021204288","volume":"301","author":"J Starekova","year":"2021","unstructured":"Starekova, J., Hernando, D., Pickhardt, P. J. & Reeder, S. B. Quantification of liver fat content with CT and MRI: State of the art. Radiology 301, 250\u2013262 (2021).","journal-title":"Radiology"},{"key":"2938_CR4","doi-asserted-by":"publisher","first-page":"20220937","DOI":"10.1259\/bjr.20220937","volume":"96","author":"M Tanabe","year":"2023","unstructured":"Tanabe, M. et al. Automated whole-volume measurement of CT fat fraction of the pancreas: Correlation with Dixon MR imaging. Br. J. Radiol. 96, 20220937 (2023).","journal-title":"Br. J. Radiol."},{"key":"2938_CR5","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1016\/j.gastha.2022.01.007","volume":"1","author":"S Bhalla","year":"2022","unstructured":"Bhalla, S., Kuchel, G. A., Pandol, S. & Bishehsari, F. Association of pancreatic fatty infiltration with age and metabolic syndrome is sex-dependent. Gastro Hep Adv. 1, 344\u2013349 (2022).","journal-title":"Gastro Hep Adv."},{"key":"2938_CR6","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1148\/radiol.2019181648","volume":"291","author":"S Jang","year":"2019","unstructured":"Jang, S. et al. Opportunistic Osteoporosis screening at routine abdominal and thoracic CT: Normative L1 trabecular attenuation values in more than 20,000 adults. Radiology 291, 360\u2013367 (2019).","journal-title":"Radiology"},{"key":"2938_CR7","doi-asserted-by":"publisher","first-page":"582","DOI":"10.2214\/AJR.20.22874","volume":"215","author":"RD Boutin","year":"2020","unstructured":"Boutin, R. D. & Lenchik, L. Value-added opportunistic CT: Insights Into Osteoporosis and Sarcopenia. Am. J. Roentgenol. 215, 582\u2013594 (2020).","journal-title":"Am. J. Roentgenol."},{"key":"2938_CR8","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1148\/radiol.211561","volume":"303","author":"PJ Pickhardt","year":"2022","unstructured":"Pickhardt, P. J. Value-added opportunistic CT screening: state of the art. Radiology 303, 241\u2013254 (2022).","journal-title":"Radiology"},{"key":"2938_CR9","doi-asserted-by":"publisher","first-page":"e222044","DOI":"10.1148\/radiol.222044","volume":"307","author":"PJ Pickhardt","year":"2023","unstructured":"Pickhardt, P. J. et al. Opportunistic screening: Radiology Scientific Expert Panel. Radiology 307, e222044 (2023).","journal-title":"Radiology"},{"key":"2938_CR10","first-page":"45","volume":"44","author":"TJ Cole","year":"1990","unstructured":"Cole, T. J. The LMS method for constructing normalized growth standards. Eur. J. Clin. Nutr. 44, 45\u201360 (1990).","journal-title":"Eur. J. Clin. Nutr."},{"key":"2938_CR11","doi-asserted-by":"publisher","first-page":"382","DOI":"10.3109\/03014460.2012.694475","volume":"39","author":"T Cole","year":"2012","unstructured":"Cole, T. The development of growth references and growth charts. Ann. Hum. Biol. 39, 382\u2013394 (2012).","journal-title":"Ann. Hum. Biol."},{"key":"2938_CR12","unstructured":"Bethlehem, R. A. et al. Brain charts for the human lifespan. Nature. 604, 525\u2013533 (2022)."},{"key":"2938_CR13","doi-asserted-by":"publisher","first-page":"1711","DOI":"10.1038\/s41596-022-00696-5","volume":"17","author":"S Rutherford","year":"2022","unstructured":"Rutherford, S. et al. The normative modeling framework for computational psychiatry. Nat. Protoc. 17, 1711\u20131734 (2022).","journal-title":"Nat. Protoc."},{"key":"2938_CR14","doi-asserted-by":"crossref","unstructured":"Wachinger, C., Renger, B., Sp\u00e4th, C. & Makowski, M. R. Body charts from CT segmentations across the adult lifespan: large-scale cross-sectional and longitudinal analyses. Radiol. Artif. Intell. 8, e250506 (2025).","DOI":"10.1148\/ryai.250506"},{"key":"2938_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-020-15948-9","volume":"11","author":"TJ Littlejohns","year":"2020","unstructured":"Littlejohns, T. J. et al. The UK Biobank imaging enhancement of 100,000 participants: Rationale, data collection, management and future directions. Nat. Commun. 11, 1\u201312 (2020).","journal-title":"Nat. Commun."},{"key":"2938_CR16","doi-asserted-by":"crossref","unstructured":"Bamberg, F. et al. Whole-body MR imaging in the German National Cohort: rationale, design, and technical background. Radiology 277, 206\u2013220 (2015).","DOI":"10.1148\/radiol.2015142272"},{"key":"2938_CR17","doi-asserted-by":"crossref","unstructured":"of Radiology (ESR) https:\/\/www.myESR.orgcommunications@ myESR.org, E. S. Good practice for radiological reporting. Guidelines from the European Society of Radiology (ESR). Insights Imag. 2, 93\u201396 (2011).","DOI":"10.1007\/s13244-011-0066-7"},{"key":"2938_CR18","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1148\/rg.2016150080","volume":"36","author":"T Cai","year":"2016","unstructured":"Cai, T. et al. Natural language processing technologies in radiology research and clinical applications. Radiographics 36, 176\u2013191 (2016).","journal-title":"Radiographics"},{"key":"2938_CR19","unstructured":"Zhou, H. et al. A survey of large language models in medicine: Progress, application, and challenge. arXiv preprint arXiv:2311.05112 (2023)."},{"key":"2938_CR20","doi-asserted-by":"crossref","unstructured":"Woo, B. F. Y., Cato, K., Cho, H., You, S. B. & Song, J. The use of large language models in clinical documentation: a scoping review. Int. J. Nurs. Studies. 105322 (2025).","DOI":"10.1016\/j.ijnurstu.2025.105322"},{"key":"2938_CR21","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1111\/j.1467-9876.2005.00510.x","volume":"54","author":"RA Rigby","year":"2005","unstructured":"Rigby, R. A. & Stasinopoulos, D. M. Generalized additive models for location, scale and shape. J. R. Stat. Soc. Ser. C: Appl. Stat. 54, 507\u2013554 (2005).","journal-title":"J. R. Stat. Soc. Ser. C: Appl. Stat."},{"key":"2938_CR22","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1002\/sim.2227","volume":"25","author":"E Borghi","year":"2006","unstructured":"Borghi, E. et al. Construction of the World Health Organization child growth standards: Selection of methods for attained growth curves. Stat. Med. 25, 247\u2013265 (2006).","journal-title":"Stat. Med."},{"key":"2938_CR23","doi-asserted-by":"crossref","unstructured":"Hamamci, I. E. et al. Generalist foundation models from a multimodal dataset for 3D computed tomography. Nature Biomed. Eng. 12, 1-9 (2026).","DOI":"10.1038\/s41551-025-01599-y"},{"key":"2938_CR24","doi-asserted-by":"publisher","first-page":"17742","DOI":"10.52202\/075280-0779","volume":"36","author":"S-C Huang","year":"2023","unstructured":"Huang, S.-C. et al. Inspect: a multimodal dataset for patient outcome prediction of pulmonary embolisms. Adv. Neural Inf. Process. Syst. 36, 17742\u201317772 (2023).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"2938_CR25","unstructured":"Blankemeier, L. et al. Merlin: a computed tomography vision\u2013language foundation model and dataset. Nature. 4, 1-1 (2026)."},{"key":"2938_CR26","doi-asserted-by":"publisher","first-page":"1046","DOI":"10.2214\/AJR.16.16387","volume":"207","author":"RD Boutin","year":"2016","unstructured":"Boutin, R. D., Kaptuch, J. M., Bateni, C. P., Chalfant, J. S. & Yao, L. Influence of IV contrast administration on CT measures of muscle and bone attenuation: implications for Sarcopenia and Osteoporosis Evaluation. Ajr. Am. J. Roentgenol. 207, 1046\u20131054 (2016).","journal-title":"Ajr. Am. J. Roentgenol."},{"key":"2938_CR27","doi-asserted-by":"publisher","first-page":"e0277111","DOI":"10.1371\/journal.pone.0277111","volume":"17","author":"SA Holcombe","year":"2022","unstructured":"Holcombe, S. A. et al. Variation in aorta attenuation in contrast-enhanced CT and its implications for calcification thresholds. PLOS ONE 17, e0277111 (2022).","journal-title":"PLOS ONE"},{"key":"2938_CR28","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-72707-y","volume":"10","author":"X Zheng","year":"2020","unstructured":"Zheng, X. et al. Body size and tube voltage dependent corrections for Hounsfield unit in medical X-ray computed tomography: theory and experiments. Sci. Rep. 10, 15696 (2020).","journal-title":"Sci. Rep."},{"key":"2938_CR29","doi-asserted-by":"crossref","unstructured":"Samei, E. et al. Performance evaluation of computed tomography systems: Summary of AAPM Task Group 233. Med. Phys. Rep. 46, e735-e756 (2019).","DOI":"10.1002\/mp.13763"},{"key":"2938_CR30","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1053\/j.semnuclmed.2007.02.001","volume":"37","author":"JM Meier","year":"2007","unstructured":"Meier, J. M. et al. Assessment of age-related changes in abdominal organ structure and function with computed tomography and Positron Emission Tomography. Semin. Nucl. Med. 37, 154\u2013172 (2007).","journal-title":"Semin. Nucl. Med."},{"key":"2938_CR31","doi-asserted-by":"publisher","first-page":"1167","DOI":"10.2214\/AJR.15.14724","volume":"205","author":"L Hahn","year":"2015","unstructured":"Hahn, L., Reeder, S. B., Mu\u00f1oz del Rio, A. & Pickhardt, P. J. Longitudinal changes in liver fat content in asymptomatic adults: hepatic attenuation on unenhanced CT as an imaging biomarker for steatosis. Ajr. Am. J. Roentgenol. 205, 1167\u20131172 (2015).","journal-title":"Ajr. Am. J. Roentgenol."},{"key":"2938_CR32","doi-asserted-by":"publisher","first-page":"111306","DOI":"10.1016\/j.exger.2021.111306","volume":"149","author":"P Figueiredo","year":"2021","unstructured":"Figueiredo, P. et al. Computed tomography-based skeletal muscle and adipose tissue attenuation: Variations by age, sex, and muscle. Exp. Gerontol. 149, 111306 (2021).","journal-title":"Exp. Gerontol."},{"key":"2938_CR33","doi-asserted-by":"publisher","first-page":"20190327","DOI":"10.1259\/bjr.20190327","volume":"92","author":"PM Graffy","year":"2019","unstructured":"Graffy, P. M. et al. Deep learning-based muscle segmentation and quantification at abdominal CT: Application to a longitudinal adult screening cohort for sarcopenia assessment. Br. J. Radiol. 92, 20190327 (2019).","journal-title":"Br. J. Radiol."},{"key":"2938_CR34","unstructured":"Yang, A. et al. Qwen3 technical report. arXiv preprint arXiv:2505.09388 (2025)."},{"key":"2938_CR35","unstructured":"Yang, A. et al. Qwen2.5 technical report arXiv:2412.15115 (2024)."},{"key":"2938_CR36","unstructured":"Meta AI. Llama 3.3 model card https:\/\/www.llama.com\/docs\/model-cards-and-prompt-formats\/llama3_3\/ Model card for the Llama 3.3 70B instruction-tuned model (2025)."},{"key":"2938_CR37","unstructured":"Grattafiori, A. et al. The llama 3 herd of models arXiv:2407.21783 (2024). https:\/\/arxiv.org\/abs\/2407.21783."},{"key":"2938_CR38","unstructured":"Saama AI Labs. Openbiollm-70b: An open-source biomedical large language model https:\/\/huggingface.co\/aaditya\/Llama3-OpenBioLLM-70B Original model card for the OpenBioLLM-70B biomedical LLM (2024)."},{"key":"2938_CR39","unstructured":"Google. Medgemma-27b-it model card https:\/\/huggingface.co\/google\/medgemma-27b-it Model card for the MedGemma 27B text-only instruction-tuned model (2026)."},{"key":"2938_CR40","unstructured":"Sellergren, A. et al. MedGemma Technical Report, arXiv:2507.05201 (2025)."},{"key":"2938_CR41","doi-asserted-by":"crossref","unstructured":"Royston, P. & Altman, D. G. Regression using fractional polynomials of continuous covariates: Parsimonious parametric modelling. Appl. Stat. 2, 429\u2013467 (1994).","DOI":"10.2307\/2986270"},{"key":"2938_CR42","first-page":"171","volume":"12","author":"A Azzalini","year":"1985","unstructured":"Azzalini, A. A class of distributions which includes the normal ones. Scand. J. Stat. 12, 171\u2013178 (1985).","journal-title":"Scand. J. Stat."},{"key":"2938_CR43","doi-asserted-by":"crossref","unstructured":"Wachinger, C., Nho, K., Saykin, A. J., Reuter, M. & Rieckmann, A. A Longitudinal Imaging Genetics Study of Neuroanatomical Asymmetry in Alzheimer\u2019s Disease. Biological psychiatry (2018).","DOI":"10.1016\/j.biopsych.2018.04.017"},{"key":"2938_CR44","unstructured":"Wood, S. N.Generalized Additive Models (Chapman and Hall\/CRC, 2017)."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02938-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02938-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02938-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T13:44:45Z","timestamp":1783086285000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02938-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,3]]},"references-count":44,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2938"],"URL":"https:\/\/doi.org\/10.1038\/s41746-026-02938-2","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,3]]},"assertion":[{"value":"10 March 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 June 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 July 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"505"}}