{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T23:48:34Z","timestamp":1791848914022,"version":"4.3.4"},"reference-count":38,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T00:00:00Z","timestamp":1754438400000},"content-version":"vor","delay-in-days":5,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J cachexia sarcopenia muscle"],"published-print":{"date-parts":[[2025,8]]},"abstract":"ABSTRACT<\/jats:title>\n \n Background<\/jats:title>\n AI\u2010driven automated body composition analysis (BCA) may provide quantitative prognostic biomarkers derived from routine staging CTs. This two\u2010centre study evaluates the prognostic value of these volumetric markers for overall survival in lung cancer patients.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n Lung cancer cohorts from Hospital A (\n n<\/jats:italic>\n \u2009=\u20093345, median age 65, 86% NSCLC, 40% M1, 40% female) and B (\n n<\/jats:italic>\n \u2009=\u20091364, median age 66, 87% NSCLC, 37% M1, 38% female) underwent automated BCA of abdominal CTs \u00b160\u2009days of primary diagnosis. A deep learning network segmented muscle, bone and adipose tissues (visceral\u2009=\u2009VAT, subcutaneous\u2009=\u2009SAT, intra\u2010\/intermuscular\u2009=\u2009IMAT and total\u2009=\u2009TAT) to derive three markers: Sarcopenia Index (SI\u2009=\u2009Muscle\/Bone), Myosteatotic Fat Index (MFI\u2009=\u2009IMAT\/TAT) and Abdominal Fat Index (AFI\u2009=\u2009VAT\/SAT). Kaplan\u2013Meier survival analysis, Cox proportional hazards modelling and machine learning\u2010based survival prediction were performed. A survival model including clinical data (BMI, ECOG, L3\u2010SMI, \u2010SATI, \u2010VATI and \u2010IMATI) was fitted on Hospital A data and validated on Hospital B data.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n In nonmetastatic NSCLC, high SI predicted longer survival across centres for males (Hospital A: 24.6 vs. 46.0\u2009months; Hospital B: 13.3 vs. 28.9\u2009months; both\n p<\/jats:italic>\n \u2009<\u20090.001) and females (Hospital A: 37.9 vs. 53.6\u2009months,\n p<\/jats:italic>\n \u2009=\u20090.008; Hospital B: 23.0 vs. 28.6\u2009months,\n p<\/jats:italic>\n \u2009=\u20090.018). High MFI indicated reduced survival in males at both hospitals (Hospital A: 43.7 vs. 28.2\u2009months; Hospital B: 28.8 vs. 14.3\u2009months; both\n p<\/jats:italic>\n \u2009\u2264\u20090.001) but showed center\u2010dependent effects in females (significant only in Hospital A,\n p<\/jats:italic>\n \u2009<\u20090.01). In metastatic disease, SI remained prognostic for males at both centres (\n p<\/jats:italic>\n \u2009<\u20090.05), while MFI was significant only in Hospital A (\n p<\/jats:italic>\n \u2009\u2264\u20090.001) and AFI only in Hospital B (\n p<\/jats:italic>\n \u2009=\u20090.042). Multivariate Cox regression confirmed that higher SI was protective (A: HR 0.53, B: 0.59,\n p<\/jats:italic>\n \u2009\u2264\u20090.001), while MFI was associated with shorter survival (A: HR 1.31, B: 1.12,\n p<\/jats:italic>\n \u2009<\u20090.01).\n <\/jats:p>\n \n The multivariate survival model trained on Hospital A's data demonstrated prognostic differentiation of groups in internal (\n n<\/jats:italic>\n \u2009=\u2009209,\n p<\/jats:italic>\n \u2009\u2264\u20090.001) and external (Hospital B,\n n<\/jats:italic>\n \u2009=\u2009361,\n p<\/jats:italic>\n \u2009=\u20090.044) validation, with SI feature importance (0.037) ranking below ECOG (0.082) and M\u2010status (0.078), outperforming all other features including conventional L3\u2010single\u2010slice measurements.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n CT\u2010based volumetric BCA provides prognostic biomarkers in lung cancer with varying significance by sex, disease stage and centre. SI was the strongest prognostic marker, outperforming conventional L3\u2010based measurements, while fat\u2010related markers showed varying associations. Our multivariate model suggests that BCA markers, particularly SI, may enhance risk stratification in lung cancer, pending centre\u2010specific and sex\u2010specific validation. Integration of these markers into clinical workflows could enable personalized care and targeted interventions for high\u2010risk patients.<\/jats:p>\n <\/jats:sec>","DOI":"10.1002\/jcsm.70021","type":"journal-article","created":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T14:55:55Z","timestamp":1754492155000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Exploration of Fully\u2010Automated Body Composition Analysis Using Routine CT\u2010Staging of Lung Cancer Patients for Survival Prognosis"],"prefix":"10.1002","volume":"16","author":[{"given":"Marc\u2010David","family":"K\u00fcnnemann","sequence":"first","affiliation":[{"name":"Clinic for Radiology University of M\u00fcnster and University Hospital M\u00fcnster M\u00fcnster Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4386-9323","authenticated-orcid":false,"given":"Christian","family":"R\u00f6mer","sequence":"additional","affiliation":[{"name":"Clinic for Radiology University of M\u00fcnster and University Hospital M\u00fcnster M\u00fcnster Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anne","family":"Helfen","sequence":"additional","affiliation":[{"name":"Clinic for Radiology University of M\u00fcnster and University Hospital M\u00fcnster M\u00fcnster Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Annalen","family":"Bleckmann","sequence":"additional","affiliation":[{"name":"West German Cancer Center (WTZ) University Hospital M\u00fcnster M\u00fcnster Germany"},{"name":"Department of Medicine A, Hematology, Oncology, and Pneumology University Hospital M\u00fcnster M\u00fcnster Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcel","family":"Kemper","sequence":"additional","affiliation":[{"name":"West German Cancer Center (WTZ) University Hospital M\u00fcnster M\u00fcnster Germany"},{"name":"Department of Medicine A, Hematology, Oncology, and Pneumology University Hospital M\u00fcnster M\u00fcnster Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Walter","family":"Heindel","sequence":"additional","affiliation":[{"name":"Clinic for Radiology University of M\u00fcnster and University Hospital M\u00fcnster M\u00fcnster Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tobias\u00a0J.","family":"Brix","sequence":"additional","affiliation":[{"name":"Institute of Medical Informatics University of M\u00fcnster M\u00fcnster Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Forsting","sequence":"additional","affiliation":[{"name":"Institute of Diagnostic and Interventional Radiology and Neuroradiology University Hospital Essen Essen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johannes","family":"Haubold","sequence":"additional","affiliation":[{"name":"Institute of Diagnostic and Interventional Radiology and Neuroradiology University Hospital Essen Essen Germany"},{"name":"Institute for Artificial Intelligence in Medicine (IKIM) University Hospital Essen Essen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcel","family":"Opitz","sequence":"additional","affiliation":[{"name":"Institute of Diagnostic and Interventional Radiology and Neuroradiology University Hospital Essen Essen Germany"},{"name":"Institute for Artificial Intelligence in Medicine (IKIM) University Hospital Essen Essen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Schuler","sequence":"additional","affiliation":[{"name":"West German Cancer Center (WTZ), Department of Medical Oncology University Hospital Essen Essen Germany"},{"name":"National Center for Tumor Diseases (NCT), NCT West, a Partnership of DKFZ, University of Duisburg\u2010Essen and University Hospital Essen University of K\u00f6ln and University Hospital K\u00f6ln Essen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Felix","family":"Nensa","sequence":"additional","affiliation":[{"name":"Institute of Diagnostic and Interventional Radiology and Neuroradiology University Hospital Essen Essen Germany"},{"name":"Institute for Artificial Intelligence in Medicine (IKIM) University Hospital Essen Essen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6987-6041","authenticated-orcid":false,"given":"Katarzyna","family":"Borys","sequence":"additional","affiliation":[{"name":"Institute of Diagnostic and Interventional Radiology and Neuroradiology University Hospital Essen Essen Germany"},{"name":"Institute for Artificial Intelligence in Medicine (IKIM) University Hospital Essen Essen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1760-2342","authenticated-orcid":false,"given":"Ren\u00e9","family":"Hosch","sequence":"additional","affiliation":[{"name":"Institute of Diagnostic and Interventional Radiology and Neuroradiology University Hospital Essen Essen Germany"},{"name":"Institute for Artificial Intelligence in Medicine (IKIM) University Hospital Essen Essen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,8,6]]},"reference":[{"key":"e_1_2_10_2_1","unstructured":"World Health Organization. \u201cGlobal Cancer Observatory: Cancer Today. 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