{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T23:17:44Z","timestamp":1788304664059,"version":"build-2803163510"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T00:00:00Z","timestamp":1781568000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T00:00:00Z","timestamp":1788307200000},"content-version":"vor","delay-in-days":78,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004281","name":"Narodowe Centrum Nauki","doi-asserted-by":"publisher","award":["2025\/56\/C\/NZ7\/00276 (DEC-2025\/56\/C\/NZ7\/00276"],"award-info":[{"award-number":["2025\/56\/C\/NZ7\/00276 (DEC-2025\/56\/C\/NZ7\/00276"]}],"id":[{"id":"10.13039\/501100004281","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Transarterial chemoembolization (TACE) is a standard treatment for patients with unresectable hepatocellular carcinoma (HCC), yet existing models provide limited individualized risk stratification. Automated CT-derived body composition analysis has emerged as an objective marker of patient physiological reserve, but its value in prognostication in TACE patients is insufficiently studied. Therefore, the aim of the study was to evaluate the prognostic value of a fully automated, open-source pipeline for CT-based body composition analysis in predicting overall survival (OS) in patients with HCC undergoing TACE. In this study, we used two independent cohorts of treatment-naive patients undergoing TACE: the WAW-TACE cohort (development;\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u2009230, OS: 28.6 months) and the HCC-TACE-Seg cohort (validation;\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u2009100, OS: 24.0 months). Skeletal muscle and fat metrics were extracted from pre-treatment CTs using a standardized deep learning pipeline and normalized by sex. Survival analyses were performed using Cox proportional hazards (CoxPH) models and random survival forests (RSF). Skeletal muscle density (SMD) at the L3 level was the strongest and independent predictor of OS across both cohorts (HR: development, 0.84;\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u20090.029; validation, 0.79;\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u20090.028). This association remained significant after adjustment for the best-performing clinical composite scores: mHAP-2 in the development (adjusted HR\u2009=\u20090.68;\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u20090.049) and CLIP in the validation cohort (adjusted HR\u2009=\u20090.43;\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u20090.003). In CoxPH, the addition of SMD metrics resulted in only modest improvements in discrimination (\u0394C-index 0.011\u20130.037) that did not reach statistical significance. In contrast, RSF analysis demonstrated a statistically significant improvement in model discrimination when muscle-based variables were added to clinical features (\u0394C-index\u2009=\u20090.023;\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.001). In both cohorts, SMD showed a reproducible independent prognostic association with overall survival. While adding SMD to traditional clinical models resulted in only modest, and in Cox-based analyses not statistically significant, improvements in discrimination, SMD provided complementary prognostic information. This suggests that the primary value of these automated CT-derived body composition metrics lies not in their performance as standalone predictors, but in their ability to provide an additional layer of objective biological data that may contribute to risk stratification in a complementary and exploratory manner within multivariable frameworks. Notably, in internally cross-validated RSF analyses, statistically significant increases in model discrimination were observed when muscle-based features were integrated into the model, highlighting their potential complementary value within machine learning frameworks.\n                  <\/jats:p>","DOI":"10.1038\/s41598-026-56245-7","type":"journal-article","created":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T05:33:57Z","timestamp":1781588037000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Fully automated CT-based quantitative body composition analysis for predicting survival in patients with HCC undergoing TACE: a dual-cohort study"],"prefix":"10.1038","volume":"16","author":[{"given":"Krzysztof","family":"Bartnik","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Belgutei","family":"Tsevegmed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kamil","family":"Ksi\u0105\u017cek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zuzanna","family":"Wojtczak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Przemys\u0142aw","family":"Biecek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,16]]},"reference":[{"issue":"2","key":"56245_CR1","doi-asserted-by":"publisher","first-page":"723","DOI":"10.1002\/HEP.29913","volume":"68","author":"JA Marrero","year":"2018","unstructured":"Marrero, J. A. et al. Diagnosis, Staging, and Management of Hepatocellular Carcinoma: 2018 Practice Guidance by the American Association for the Study of Liver Diseases. Hepatol. 68 (2), 723\u2013750. https:\/\/doi.org\/10.1002\/HEP.29913 (2018).","journal-title":"Hepatol. Hepatol."},{"key":"56245_CR2","doi-asserted-by":"crossref","unstructured":"Heimbach, J. K. et al. AASLD guidelines for the treatment of hepatocellular carcinoma. Hepatology Vol. 67, 358\u2013380 (John Wiley and Sons Inc., 2018).","DOI":"10.1002\/hep.29086"},{"issue":"5","key":"56245_CR3","doi-asserted-by":"publisher","first-page":"893","DOI":"10.1016\/J.JHEP.2019.01.013","volume":"70","author":"Q Wang","year":"2019","unstructured":"Wang, Q. et al. Development of a prognostic score for recommended TACE candidates with hepatocellular carcinoma: A multicentre observational study. J. Hepatol. 70 (5), 893\u2013903. https:\/\/doi.org\/10.1016\/J.JHEP.2019.01.013 (2019).","journal-title":"J. Hepatol. J. Hepatol."},{"issue":"6","key":"56245_CR4","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1200\/JCO.2014.57.9151","volume":"33","author":"PJ Johnson","year":"2015","unstructured":"Johnson, P. J. et al. Assessment of liver function in patients with hepatocellular carcinoma: A new evidence-based approach - The albi grade. J. Clin. Oncol. 33 (6), 550\u2013558. https:\/\/doi.org\/10.1200\/JCO.2014.57.9151 (2015).","journal-title":"J. Clin. Oncol. Am. Soc. Clin. Oncol."},{"issue":"28","key":"56245_CR5","doi-asserted-by":"publisher","first-page":"44705","DOI":"10.18632\/oncotarget.9604","volume":"7","author":"DJ Pinato","year":"2016","unstructured":"Pinato, D. J. et al. Combined sequential use of HAP and ART scores to predict survival outcome and treatment failure following chemoembolization in hepatocellular carcinoma: A multi-center comparative study. Oncotarget Impact Journals LLC. 7 (28), 44705\u201344718. https:\/\/doi.org\/10.18632\/oncotarget.9604 (2016).","journal-title":"Oncotarget Impact Journals LLC"},{"issue":"3","key":"56245_CR6","first-page":"280","volume":"385","author":"RK Kelley","year":"2021","unstructured":"Kelley, R. K. & Greten, T. F. Hepatocellular Carcinoma - Origins and Outcomes. New England Journal of Medicine. Mass. Med. Soc. 385 (3), 280\u2013282 (2021).","journal-title":"Mass. Med. Soc."},{"key":"56245_CR7","doi-asserted-by":"publisher","first-page":"2206","DOI":"10.1002\/HEP.31187","volume-title":"Official Journal of the American Association for the Study of Liver Diseases","author":"A Vitale","year":"2020","unstructured":"Vitale, A., Trevisani, F., Farinati, F. & Cillo, U. Treatment of Hepatocellular Carcinoma in the Precision Medicine Era: From Treatment Stage Migration to Therapeutic Hierarchy. Hepatology;72(6):2206\u20132218. doi: https:\/\/doi.org\/10.1002\/HEP.31187. (2020)."},{"issue":"7","key":"56245_CR8","doi-asserted-by":"publisher","first-page":"e312","DOI":"10.1016\/S1470-2045(23)00186-9","volume":"24","author":"A Vitale","year":"2023","unstructured":"Vitale, A. et al. Personalised management of patients with hepatocellular carcinoma: a multiparametric therapeutic hierarchy concept. Lancet Oncol. Elsevier Ltd. 24 (7), e312\u2013e322. https:\/\/doi.org\/10.1016\/S1470-2045(23)00186-9 (2023).","journal-title":"Lancet Oncol. Elsevier Ltd."},{"issue":"10","key":"56245_CR9","doi-asserted-by":"publisher","first-page":"e437","DOI":"10.1016\/S1470-2045(12)70259-0","volume":"13","author":"ME Hamaker","year":"2012","unstructured":"Hamaker, M. E. et al. Frailty screening methods for predicting outcome of a comprehensive geriatric assessment in elderly patients with cancer: A systematic review. Lancet Oncol. Elsevier. 13 (10), e437\u2013e444. https:\/\/doi.org\/10.1016\/S1470-2045(12)70259-0 (2012).","journal-title":"Lancet Oncol. Elsevier"},{"issue":"5","key":"56245_CR10","first-page":"362","volume":"67","author":"CG Ethun","year":"2017","unstructured":"Ethun, C. G. et al. Frailty and cancer: Implications for oncology surgery, medical oncology, and radiation oncology. CA Cancer J. Clin. Wiley. 67 (5), 362\u2013377 (2017).","journal-title":"CA Cancer J. Clin. Wiley"},{"issue":"6","key":"56245_CR11","doi-asserted-by":"publisher","first-page":"e13752","DOI":"10.1111\/ECC.13752","volume":"31","author":"J Welford","year":"2022","unstructured":"Welford, J. et al. The Clinical Frailty Scale can indicate prognosis and care requirements on discharge in oncology and haemato-oncology inpatients: A cohort study. Eur. J. Cancer Care (Engl) John Wiley Sons Inc. 31 (6), e13752. https:\/\/doi.org\/10.1111\/ECC.13752 (2022).","journal-title":"Eur. J. Cancer Care (Engl) John Wiley Sons Inc"},{"issue":"9","key":"56245_CR12","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1080\/14737140.2022.2096594","volume":"22","author":"D Shah","year":"2022","unstructured":"Shah, D. et al. Use of the Rockwood Clinical Frailty Scale in patients with advanced hepatopancreaticobiliary malignancies. Expert Rev. Anticancer Ther. Expert Rev. Anticancer Ther. 22 (9), 1009\u20131015. https:\/\/doi.org\/10.1080\/14737140.2022.2096594 (2022).","journal-title":"Expert Rev. Anticancer Ther. Expert Rev. Anticancer Ther."},{"issue":"November 2025","key":"56245_CR13","doi-asserted-by":"publisher","first-page":"581","DOI":"10.59275\/J.MELBA.2025-AG5G","volume":"3","author":"Y Chen","year":"2025","unstructured":"Chen, Y. et al. Automated Muscle and Fat Segmentation in Computed Tomography for Comprehensive Body Composition Analysis. Machine Learning for Biomedical Imaging. Mach. Learn. Biomed. Imaging. 3 (November 2025), 581\u2013617. https:\/\/doi.org\/10.59275\/J.MELBA.2025-AG5G (2025).","journal-title":"Mach. Learn. Biomedical Imaging"},{"key":"56245_CR14","unstructured":"Gu, H. et al. Improving Surgical Risk Prediction Through Integrating Automated Body Composition Analysis: a Retrospective Trial on Colectomy Surgery. https:\/\/arxiv.org\/pdf\/2506.11996. (2025)."},{"key":"56245_CR15","doi-asserted-by":"crossref","unstructured":"Wang, A. et al. Impact of body composition on the prognosis of hepatocellular carcinoma patients treated with transarterial chemoembolization: A systematic review and meta-analysis. Heliyon Vol. 10, e25237 (Elsevier Ltd, 2024).","DOI":"10.1016\/j.heliyon.2024.e25237"},{"issue":"8","key":"56245_CR16","doi-asserted-by":"publisher","first-page":"1210","DOI":"10.14309\/AJG.0000000000000662","volume":"115","author":"NC Wang","year":"2020","unstructured":"Wang, N. C. et al. Automated Measurements of Muscle Mass Using Deep Learning can Predict Clinical Outcomes in Patients with Liver Disease. Am. J. Gastroenterol. Wolters Kluwer Health. 115 (8), 1210. https:\/\/doi.org\/10.14309\/AJG.0000000000000662 (2020).","journal-title":"Am. J. Gastroenterol. Wolters Kluwer Health"},{"key":"56245_CR17","unstructured":"GitHub - mazurowski-lab\/CT-Muscle-and-Fat-Segmentation. https:\/\/github.com\/mazurowski-lab\/CT-Muscle-and-Fat-Segmentation. (2025)."},{"key":"56245_CR18","doi-asserted-by":"publisher","unstructured":"Bartnik, K. et al. WAW-TACE: A Hepatocellular Carcinoma Multiphase CT Dataset with Segmentations, Radiomics Features, and Clinical Data. Radiol. Artif. Intell. Radiological Soc. North. Am. Inc. 6 (6). https:\/\/doi.org\/10.1148\/RYAI.240296\/ASSET (2024).","DOI":"10.1148\/RYAI.240296\/ASSET"},{"key":"56245_CR19","doi-asserted-by":"publisher","DOI":"10.1038\/S41597-023-01928-3","author":"AW Moawad","year":"2023","unstructured":"Moawad, A. W. et al. Multimodality annotated hepatocellular carcinoma data set including pre- and post-TACE with imaging segmentation. Sci. Data Nat. Publishing Group.10(1). doi: https:\/\/doi.org\/10.1038\/S41597-023-01928-3. (2023)."},{"issue":"10","key":"56245_CR20","doi-asserted-by":"publisher","first-page":"2565","DOI":"10.1093\/annonc\/mdt247","volume":"24","author":"L Kadalayil","year":"2013","unstructured":"Kadalayil, L. et al. A simple prognostic scoring system for patients receiving transarterial embolisation for hepatocellular cancer. Annals of Oncology. Ann. Oncol. 24 (10), 2565\u20132570. https:\/\/doi.org\/10.1093\/annonc\/mdt247 (2013).","journal-title":"Ann. Oncol."},{"issue":"1","key":"56245_CR21","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1111\/LIV.12878","volume":"36","author":"Y Park","year":"2016","unstructured":"Park, Y. et al. Addition of tumor multiplicity improves the prognostic performance of the hepatoma arterial-embolization prognostic score. Liver Int. Liver Int. 36 (1), 100\u2013107. https:\/\/doi.org\/10.1111\/LIV.12878 (2016).","journal-title":"Liver Int. Liver Int."},{"issue":"9","key":"56245_CR22","doi-asserted-by":"publisher","first-page":"1704","DOI":"10.1111\/LIV.14194","volume":"39","author":"IC Lee","year":"2019","unstructured":"Lee, I. C. et al. A new ALBI-based model to predict survival after transarterial chemoembolization for BCLC stage B hepatocellular carcinoma. Liver Int. Liver Int. 39 (9), 1704\u20131712. https:\/\/doi.org\/10.1111\/LIV.14194 (2019).","journal-title":"Liver Int. Liver Int."},{"key":"56245_CR23","doi-asserted-by":"crossref","unstructured":"Daniele, B. et al. Cancer of the Liver Italian Program (CLIP) score for staging hepatocellular carcinoma Vol. 37, S206\u2013S209 (Hepatology Research. John Wiley & Sons, Ltd, 2007).","DOI":"10.1111\/j.1872-034X.2007.00186.x"},{"issue":"3","key":"56245_CR24","doi-asserted-by":"publisher","first-page":"406","DOI":"10.4254\/WJH.V7.I3.406","volume":"7","author":"A Kinoshita","year":"2015","unstructured":"Kinoshita, A. et al. Staging systems for hepatocellular carcinoma: Current status and future perspectives. World J. Hepatol. Baishideng Publishing Group. Co. 7 (3), 406. https:\/\/doi.org\/10.4254\/WJH.V7.I3.406 (2015).","journal-title":"World J. Hepatol. Baishideng Publishing Group. Co."},{"key":"56245_CR25","doi-asserted-by":"publisher","unstructured":"Meister, F. A. et al. The Role of Sarcopenia and Myosteatosis in Short- and Long-Term Outcomes Following Curative-Intent Surgery for Hepatocellular Carcinoma in a European Cohort. Cancers (Basel) Cancers (Basel). 14 (3). https:\/\/doi.org\/10.3390\/CANCERS14030720 (2022).","DOI":"10.3390\/CANCERS14030720"},{"issue":"3","key":"56245_CR26","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1016\/S0167-9473(98)00096-6","volume":"30","author":"C Contal","year":"1999","unstructured":"Contal, C. & O\u2019Quigley, J. An application of changepoint methods in studying the effect of age on survival in breast cancer. Comput. Stat. Data Anal. N.-Holl. 30 (3), 253\u2013270. https:\/\/doi.org\/10.1016\/S0167-9473(98)00096-6 (1999).","journal-title":"Comput. Stat. Data Anal. N.-Holl."},{"key":"56245_CR27","doi-asserted-by":"publisher","unstructured":"Bartnik, K. et al. A novel radiomics approach for predicting TACE outcomes in hepatocellular carcinoma patients using deep learning for multi-organ segmentation. Sci. Rep. Nat. Publishing Grouphttps:\/\/doi.org\/10.1038\/s41598-024-65630-z (2024).","DOI":"10.1038\/s41598-024-65630-z"},{"issue":"8","key":"56245_CR28","doi-asserted-by":"publisher","first-page":"1016","DOI":"10.1002\/UEG2.12627","volume":"12","author":"A Surov","year":"2024","unstructured":"Surov, A. et al. Skeletal muscle quality predicts overall survival in advanced liver hepatocellular carcinoma treated with SIRT and sorafenib: A subanalysis of the SORAMIC trial. United Eur. Gastroenterol. J. 12 (8), 1016\u20131027. https:\/\/doi.org\/10.1002\/UEG2.12627 (2024).","journal-title":"United Eur. Gastroenterol. J. United Eur. Gastroenterol. J."},{"key":"56245_CR29","doi-asserted-by":"publisher","unstructured":"Surov, A. et al. Albumin-muscle density score predicts overall survival in patients with hepatocellular cancer undergoing treatment with transarterial chemoembolization. J. Cancer Res. Clin. Oncol. 150 (12). https:\/\/doi.org\/10.1007\/S00432-024-06043-3 (2024).","DOI":"10.1007\/S00432-024-06043-3"},{"issue":"5","key":"56245_CR30","doi-asserted-by":"publisher","first-page":"e239","DOI":"10.1016\/S1470-2045(18)30284-5","volume":"19","author":"E Gourd","year":"2018","unstructured":"Gourd, E. Sarcopenia and adiposity linked to overall survival. Lancet Oncol. NLM (Medline). 19 (5), e239. https:\/\/doi.org\/10.1016\/S1470-2045(18)30284-5 (2018).","journal-title":"Lancet Oncol. NLM (Medline)"},{"issue":"6","key":"56245_CR31","doi-asserted-by":"publisher","first-page":"e2417115","DOI":"10.1001\/JAMANETWORKOPEN.2024.17115","volume":"7","author":"C Liu","year":"2024","unstructured":"Liu, C. et al. Sarcopenic Obesity and Outcomes for Patients With Cancer. JAMA Netw. Open. Am. Med. Association. 7 (6), e2417115\u2013e2417115. https:\/\/doi.org\/10.1001\/JAMANETWORKOPEN.2024.17115 (2024).","journal-title":"JAMA Netw. Open. Am. Med. Association"},{"key":"56245_CR32","doi-asserted-by":"publisher","unstructured":"Molwitz, I. et al. Muscle quality determined by computed tomography predicts short-term and long-term survival after liver transplantation. Sci. Rep. 13 (1). https:\/\/doi.org\/10.1038\/S41598-023-33349-Y (2023).","DOI":"10.1038\/S41598-023-33349-Y"},{"key":"56245_CR33","doi-asserted-by":"publisher","unstructured":"Westenberg, L. B. et al. Reference values for low muscle mass and myosteatosis using tomographic muscle measurements in living kidney donors. Sci. Rep. 13 (1). https:\/\/doi.org\/10.1038\/S41598-023-33041-1 (2023).","DOI":"10.1038\/S41598-023-33041-1"},{"issue":"5","key":"56245_CR34","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1038\/S41430-024-01551-4","volume":"79","author":"A Sabatino","year":"2025","unstructured":"Sabatino, A. et al. Myosteatosis is associated with adiposity, metabolic derangements and mortality in patients with chronic kidney disease. Eur. J. Clin. Nutr. 79 (5), 475\u2013483. https:\/\/doi.org\/10.1038\/S41430-024-01551-4 (2025).","journal-title":"Eur. J. Clin. Nutr. Eur. J. Clin. Nutr."},{"issue":"6","key":"56245_CR35","doi-asserted-by":"publisher","first-page":"2705","DOI":"10.1002\/jcsm.13615","volume":"15","author":"ZC Jin","year":"2024","unstructured":"Jin, Z. C. et al. Longitudinal Body Composition Identifies Hepatocellular Carcinoma With Cachexia Following Combined Immunotherapy and Target Therapy (CHANCE2213). J. Cachexia Sarcopenia Muscle. 15 (6), 2705\u20132716. https:\/\/doi.org\/10.1002\/jcsm.13615 (2024).","journal-title":"J. Cachexia Sarcopenia Muscle"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-56245-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-56245-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-56245-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T22:59:04Z","timestamp":1788303544000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-56245-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,16]]},"references-count":35,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["56245"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-56245-7","relation":{"references":[{"id-type":"doi","id":"10.1038\/S41597-023-01928-3","asserted-by":"subject"}],"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-8713867\/v1","asserted-by":"object"}]},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,16]]},"assertion":[{"value":"27 January 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 June 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","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"27444"}}