{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T01:00:16Z","timestamp":1775696416753,"version":"3.50.1"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T00:00:00Z","timestamp":1746662400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T00:00:00Z","timestamp":1746662400000},"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":["Eur Radiol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate estimation of treatment response can help clinicians identify patients who would potentially benefit from systemic therapy. This study aimed to develop and externally validate a model for predicting treatment response to systemic therapy in advanced gallbladder cancer (GBC).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We recruited 399 eligible GBC patients across four institutions. Multivariable logistic regression analysis was performed to identify independent clinical factors related to therapeutic efficacy. This deep learning (DL) radiomics signature was developed for predicting treatment response using multiphase enhanced CT images. Then, the DL radiomic-clinical (DLRSC) model was built by combining the DL signature and significant clinical factors, and its predictive performance was evaluated using area under the curve (AUC). Gradient-weighted class activation mapping analysis was performed to help clinicians better understand the predictive results. Furthermore, patients were stratified into low- and high-score groups by the DLRSC model. The progression-free survival (PFS) and overall survival (OS) between the two different groups were compared.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Multivariable analysis revealed that tumor size was a significant predictor of efficacy. The DLRSC model showed great predictive performance, with AUCs of 0.86 (95% CI, 0.82\u20130.89) and 0.84 (95% CI, 0.80\u20130.87) in the internal and external test datasets, respectively. This model showed great discrimination, calibration, and clinical utility. Moreover, Kaplan\u2013Meier survival analysis revealed that low-score group patients who were insensitive to systemic therapy predicted by the DLRSC model had worse PFS and OS.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The DLRSC model allows for predicting treatment response in advanced GBC patients receiving systemic therapy. The survival benefit provided by the DLRSC model was also assessed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Question<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>No effective tools exist for identifying patients who would potentially benefit from systemic therapy in clinical practice.<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Findings<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Our combined model allows for predicting treatment response to systemic therapy in advanced gallbladder cancer.<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Clinical relevance<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>With the help of this model, clinicians could inform patients of the risk of potential ineffective treatment. Such a strategy can reduce unnecessary adverse events and effectively help reallocate societal healthcare resources.<\/jats:italic>\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Graphical Abstract<\/jats:title>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-025-11645-7","type":"journal-article","created":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T12:48:14Z","timestamp":1746708494000},"page":"7410-7420","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Predicting treatment response to systemic therapy in advanced gallbladder cancer using multiphase enhanced CT images"],"prefix":"10.1007","volume":"35","author":[{"given":"Ji","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhigang","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiping","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2967-4844","authenticated-orcid":false,"given":"Bo","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,8]]},"reference":[{"key":"11645_CR1","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1038\/s41572-022-00398-y","volume":"8","author":"JC Roa","year":"2022","unstructured":"Roa JC, Garc\u00eda P, Kapoor VK, Maithel SK, Javle M, Koshiol J (2022) Gallbladder cancer. Nat Rev Dis Prim 8:69","journal-title":"Nat Rev Dis Prim"},{"key":"11645_CR2","doi-asserted-by":"publisher","first-page":"1294","DOI":"10.1001\/jama.2018.11815","volume":"320","author":"I Baiu","year":"2018","unstructured":"Baiu I, Visser B (2018) Gallbladder cancer. JAMA 320:1294","journal-title":"JAMA"},{"key":"11645_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2024.115138","volume":"214","author":"A Blandino","year":"2025","unstructured":"Blandino A, Scherer D, Boekstegers F et al (2025) Small-RNA sequencing reveals potential serum biomarkers for gallbladder cancer: results from a three-stage collaborative study of large European prospective cohorts. Eur J Cancer 214:115138","journal-title":"Eur J Cancer"},{"key":"11645_CR4","doi-asserted-by":"crossref","unstructured":"Feo CF, Ginesu GC, Fancellu A et al (2022) Current management of incidental gallbladder cancer: a review. Int J Surg 98:106234","DOI":"10.1016\/j.ijsu.2022.106234"},{"key":"11645_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2024.113564","volume":"199","author":"BH Ten Haaft","year":"2024","unstructured":"Ten Haaft BH, Pedregal M, Prato J, Kl\u00fcmpen HJ, Moreno V, Lamarca A (2024) Revolutionizing anti-HER2 therapies for extrahepatic cholangiocarcinoma and gallbladder cancer: current advancements and future perspectives. Eur J Cancer 199:113564","journal-title":"Eur J Cancer"},{"key":"11645_CR6","doi-asserted-by":"publisher","first-page":"103328","DOI":"10.1016\/j.critrevonc.2021.103328","volume":"163","author":"AA Azizi","year":"2021","unstructured":"Azizi AA, Lamarca A, McNamara MG, Valle JW (2021) Chemotherapy for advanced gallbladder cancer (GBC): a systematic review and meta-analysis. Crit Rev Oncol Hematol 163:103328","journal-title":"Crit Rev Oncol Hematol"},{"key":"11645_CR7","doi-asserted-by":"publisher","DOI":"10.1186\/s12967-023-04589-3","volume":"21","author":"X He","year":"2023","unstructured":"He X, Peng Y, He G et al (2023) Increased co-expression of PD1 and TIM3 is associated with poor prognosis and immune microenvironment heterogeneity in gallbladder cancer. J Transl Med 21:717","journal-title":"J Transl Med"},{"key":"11645_CR8","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2022.946861","volume":"13","author":"C Shi","year":"2022","unstructured":"Shi C, Li Y, Yang C et al (2022) Lenvatinib plus programmed cell death protein-1 inhibitor beyond first-line systemic therapy in refractory advanced biliary tract cancer: a real-world retrospective study in China. Front Immunol 13:946861","journal-title":"Front Immunol"},{"key":"11645_CR9","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1007\/s00262-024-03831-1","volume":"73","author":"Y Tan","year":"2024","unstructured":"Tan Y, Liu K, Zhu C et al (2024) Efficacy and safety of lenvatinib combined with anti-PD-1 antibodies plus GEMOX chemotherapy as non-first-line systemic therapy in advanced gallbladder cancer. Cancer Immunol Immunother 73:240","journal-title":"Cancer Immunol Immunother"},{"key":"11645_CR10","doi-asserted-by":"crossref","unstructured":"Frega G, Cossio FP, Banales JM et al (2023) Lacking immunotherapy biomarkers for biliary tract cancer: a comprehensive systematic literature review and meta-analysis. Cells 12:2098","DOI":"10.3390\/cells12162098"},{"key":"11645_CR11","doi-asserted-by":"publisher","first-page":"1979","DOI":"10.1111\/cas.16142","volume":"115","author":"Z Cheng","year":"2024","unstructured":"Cheng Z, Yang C, Zhao Q et al (2024) Efficacy and predictors of immune checkpoint inhibitors in patients with gallbladder cancer. Cancer Sci 115:1979\u20131988","journal-title":"Cancer Sci"},{"key":"11645_CR12","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.1043479","volume":"12","author":"D Tan","year":"2022","unstructured":"Tan D, An J, Gong M et al (2022) Screening of an individualized treatment strategy for an advanced gallbladder cancer using patient-derived tumor xenograft and organoid models. Front Oncol 12:1043479","journal-title":"Front Oncol"},{"key":"11645_CR13","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms5006","volume":"5","author":"HJ Aerts","year":"2014","unstructured":"Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006","journal-title":"Nat Commun"},{"key":"11645_CR14","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1186\/s13058-022-01516-0","volume":"24","author":"X Wang","year":"2022","unstructured":"Wang X, Xie T, Luo J, Zhou Z, Yu X, Guo X (2022) Radiomics predicts the prognosis of patients with locally advanced breast cancer by reflecting the heterogeneity of tumor cells and the tumor microenvironment. Breast Cancer Res 24:20","journal-title":"Breast Cancer Res"},{"key":"11645_CR15","doi-asserted-by":"publisher","first-page":"7627","DOI":"10.1002\/cam4.5449","volume":"12","author":"H He","year":"2023","unstructured":"He H, Jin Z, Dai J, Wang H, Sun J, Xu D (2023) Computed tomography-based radiomics prediction of CTLA4 expression and prognosis in clear cell renal cell carcinoma. Cancer Med 12:7627\u20137638","journal-title":"Cancer Med"},{"key":"11645_CR16","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva A, Kuprel B, Novoa RA et al (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542:115\u2013118","journal-title":"Nature"},{"key":"11645_CR17","doi-asserted-by":"publisher","first-page":"2860","DOI":"10.1109\/TIP.2019.2953587","volume":"29","author":"W Song","year":"2020","unstructured":"Song W, Li S, Chang T, Hao A, Zhao Q, Qin H (2020) Context-interactive CNN for person re-identification. IEEE Trans Image Process 29:2860\u20132874","journal-title":"IEEE Trans Image Process"},{"key":"11645_CR18","doi-asserted-by":"publisher","first-page":"106165","DOI":"10.1016\/j.ijsu.2021.106165","volume":"96","author":"G Mathew","year":"2021","unstructured":"Mathew G, Agha R, Albrecht J et al (2021) STROCSS 2021: strengthening the reporting of cohort, cross-sectional and case-control studies in surgery. Int J Surg 96:106165","journal-title":"Int J Surg"},{"key":"11645_CR19","doi-asserted-by":"publisher","first-page":"100763","DOI":"10.1016\/j.jhepr.2023.100763","volume":"5","author":"QP Liu","year":"2023","unstructured":"Liu QP, Tang J, Chen YZ et al (2023) Immuno-genomic-radiomics to predict response of biliary tract cancer to camrelizumab plus GEMOX in a single-arm phase II trial. JHEP Rep 5:100763","journal-title":"JHEP Rep"},{"key":"11645_CR20","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.ejca.2016.03.081","volume":"62","author":"LH Schwartz","year":"2016","unstructured":"Schwartz LH, Liti\u00e8re S, de Vries E et al (2016) RECIST 1.1\u2014update and clarification: from the RECIST committee. Eur J Cancer 62:132\u2013137","journal-title":"Eur J Cancer"},{"key":"11645_CR21","doi-asserted-by":"publisher","DOI":"10.14309\/ctg.0000000000000425","volume":"12","author":"X Zhang","year":"2021","unstructured":"Zhang X, Zhou Y, Wu Z et al (2021) Double-negative \u03b1-fetoprotein and carbohydrate antigen 19-9 predict a good prognosis in intrahepatic cholangiocarcinoma: a propensity score matching analysis. Clin Transl Gastroenterol 12:e00425","journal-title":"Clin Transl Gastroenterol"},{"key":"11645_CR22","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-45056-9","volume":"13","author":"Y Lv","year":"2023","unstructured":"Lv Y, Liu H, He P et al (2023) A novel model for predicting the prognosis of postoperative intrahepatic cholangiocarcinoma patients. Sci Rep 13:19267","journal-title":"Sci Rep"},{"key":"11645_CR23","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1080\/07853890.2022.2160008","volume":"55","author":"SN Zhou","year":"2023","unstructured":"Zhou SN, Jv DW, Meng XF et al (2023) Feasibility of machine learning-based modeling and prediction using multiple centers data to assess intrahepatic cholangiocarcinoma outcomes. Ann Med 55:215\u2013223","journal-title":"Ann Med"},{"key":"11645_CR24","doi-asserted-by":"publisher","first-page":"7742","DOI":"10.1002\/mp.15980","volume":"49","author":"MFR Abdul Hadi","year":"2022","unstructured":"Abdul Hadi MFR, Abdullah AN, Hashikin NAA et al (2022) Utilizing 3D Slicer to incorporate tomographic images into GATE Monte Carlo simulation for personalized dosimetry in yttrium-90 radioembolization. Med Phys 49:7742\u20137753","journal-title":"Med Phys"},{"key":"11645_CR25","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1016\/j.jhepr.2019.06.003","volume":"1","author":"E Loeuillard","year":"2019","unstructured":"Loeuillard E, Conboy CB, Gores GJ, Ilyas SI (2019) Immunobiology of cholangiocarcinoma. JHEP Rep 1:297\u2013311","journal-title":"JHEP Rep"},{"key":"11645_CR26","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1002\/hep.32150","volume":"75","author":"F Carapeto","year":"2022","unstructured":"Carapeto F, Bozorgui B, Shroff RT et al (2022) The immunogenomic landscape of resected intrahepatic cholangiocarcinoma. Hepatology 75:297\u2013308","journal-title":"Hepatology"},{"key":"11645_CR27","doi-asserted-by":"publisher","first-page":"4177","DOI":"10.1007\/s00330-018-5986-x","volume":"29","author":"S Chen","year":"2019","unstructured":"Chen S, Feng S, Wei J et al (2019) Pretreatment prediction of immunoscore in hepatocellular cancer: a radiomics-based clinical model based on Gd-EOB-DTPA-enhanced MRI imaging. Eur Radiol 29:4177\u20134187","journal-title":"Eur Radiol"},{"key":"11645_CR28","doi-asserted-by":"publisher","first-page":"545","DOI":"10.2147\/JHC.S309570","volume":"8","author":"H Chong","year":"2021","unstructured":"Chong H, Gong Y, Pan X et al (2021) Peritumoral dilation radiomics of gadoxetate disodium-enhanced MRI excellently predicts early recurrence of hepatocellular carcinoma without macrovascular invasion after hepatectomy. J Hepatocell Carcinoma 8:545\u2013563","journal-title":"J Hepatocell Carcinoma"},{"key":"11645_CR29","doi-asserted-by":"publisher","first-page":"1039","DOI":"10.1097\/JS9.0000000000000881","volume":"110","author":"Z Liu","year":"2024","unstructured":"Liu Z, Luo C, Chen X et al (2024) Noninvasive prediction of perineural invasion in intrahepatic cholangiocarcinoma by clinicoradiological features and computed tomography radiomics based on interpretable machine learning: a multicenter cohort study. Int J Surg 110:1039\u20131051","journal-title":"Int J Surg"},{"key":"11645_CR30","doi-asserted-by":"publisher","first-page":"3426","DOI":"10.1118\/1.2335487","volume":"33","author":"A Madabhushi","year":"2006","unstructured":"Madabhushi A, Udupa JK (2006) New methods of MR image intensity standardization via generalized scale. Med Phys 33:3426\u20133434","journal-title":"Med Phys"},{"key":"11645_CR31","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1109\/TPAMI.2019.2932062","volume":"43","author":"P Luo","year":"2021","unstructured":"Luo P, Zhang R, Ren J, Peng Z, Li J (2021) Switchable normalization for learning-to-normalize deep representation. IEEE Trans Pattern Anal Mach Intell 43:712\u2013728","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11645_CR32","doi-asserted-by":"publisher","first-page":"e763","DOI":"10.1016\/S2589-7500(21)00180-1","volume":"3","author":"M Bilal","year":"2021","unstructured":"Bilal M, Raza SEA, Azam A et al (2021) Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study. Lancet Digit Health 3:e763\u2013e772","journal-title":"Lancet Digit Health"},{"key":"11645_CR33","doi-asserted-by":"crossref","unstructured":"Chen Z, Jiang Y, Zhang X et al (2022) ResNet18DNN: prediction approach of drug-induced liver injury by deep neural network with ResNet18. Brief Bioinform 23:bbab503","DOI":"10.1093\/bib\/bbab503"},{"key":"11645_CR34","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1177\/0272989X19832881","volume":"39","author":"P Capogrosso","year":"2019","unstructured":"Capogrosso P, Vickers AJ (2019) A systematic review of the literature demonstrates some errors in the use of decision curve analysis but generally correct interpretation of findings. Med Decis Making 39:493\u2013498","journal-title":"Med Decis Making"},{"key":"11645_CR35","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1016\/j.eururo.2024.09.035","volume":"87","author":"JJ Twilt","year":"2025","unstructured":"Twilt JJ, Saha A, Bosma JS et al (2025) Evaluating biparametric versus multiparametric magnetic resonance imaging for diagnosing clinically significant prostate cancer: an international, paired, noninferiority, confirmatory observer study. Eur Urol 87:240\u2013250","journal-title":"Eur Urol"},{"key":"11645_CR36","doi-asserted-by":"publisher","first-page":"m441","DOI":"10.1136\/bmj.m441","volume":"368","author":"RD Riley","year":"2020","unstructured":"Riley RD, Ensor J, Snell KIE et al (2020) Calculating the sample size required for developing a clinical prediction model. BMJ 368:m441","journal-title":"BMJ"},{"key":"11645_CR37","doi-asserted-by":"publisher","first-page":"102352","DOI":"10.1016\/j.eclinm.2023.102352","volume":"66","author":"H Wang","year":"2023","unstructured":"Wang H, Zhang M, Miao J et al (2023) Deep learning signature based on multiphase enhanced CT for bladder cancer recurrence prediction: a multi-center study. EClinicalMedicine 66:102352","journal-title":"EClinicalMedicine"},{"key":"11645_CR38","doi-asserted-by":"publisher","first-page":"102271","DOI":"10.1016\/j.eclinm.2023.102271","volume":"65","author":"S Zhou","year":"2023","unstructured":"Zhou S, Sun D, Mao W et al (2023) Deep radiomics-based fusion model for prediction of bevacizumab treatment response and outcome in patients with colorectal cancer liver metastases: a multicentre cohort study. EClinicalMedicine 65:102271","journal-title":"EClinicalMedicine"},{"key":"11645_CR39","doi-asserted-by":"publisher","first-page":"101146","DOI":"10.1016\/j.xcrm.2023.101146","volume":"4","author":"Y Jiang","year":"2023","unstructured":"Jiang Y, Zhou K, Sun Z et al (2023) Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics. Cell Rep Med 4:101146","journal-title":"Cell Rep Med"},{"key":"11645_CR40","doi-asserted-by":"publisher","first-page":"5574","DOI":"10.1158\/1078-0432.CCR-18-0848","volume":"24","author":"Y Jiang","year":"2018","unstructured":"Jiang Y, Xie J, Han Z et al (2018) Immunomarker support vector machine classifier for prediction of gastric cancer survival and adjuvant chemotherapeutic benefit. Clin Cancer Res 24:5574\u20135584","journal-title":"Clin Cancer Res"},{"key":"11645_CR41","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1186\/s40644-024-00768-7","volume":"24","author":"J Wu","year":"2024","unstructured":"Wu J, Li J, Huang B et al (2024) Radiomics predicts the prognosis of patients with clear cell renal cell carcinoma by reflecting the tumor heterogeneity and microenvironment. Cancer Imaging 24:124","journal-title":"Cancer Imaging"},{"key":"11645_CR42","doi-asserted-by":"publisher","first-page":"117080","DOI":"10.1016\/j.biopha.2024.117080","volume":"177","author":"S Hua","year":"2024","unstructured":"Hua S, Gu X, Jin H, Zhang X, Liu Q, Yang J (2024) Tumor-infiltrating T lymphocytes: a promising immunotherapeutic target for preventing immune escape in cholangiocarcinoma. Biomed Pharmacother 177:117080","journal-title":"Biomed Pharmacother"}],"container-title":["European Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-025-11645-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00330-025-11645-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-025-11645-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T11:09:51Z","timestamp":1761908991000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00330-025-11645-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,8]]},"references-count":42,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["11645"],"URL":"https:\/\/doi.org\/10.1007\/s00330-025-11645-7","relation":{},"ISSN":["1432-1084"],"issn-type":[{"value":"1432-1084","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,8]]},"assertion":[{"value":"13 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 March 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 April 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 May 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The scientific guarantor of this publication is Bo Huang.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Guarantor"}},{"value":"The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"No complex statistical methods were necessary for this paper.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and biometry"}},{"value":"Written informed consent was waived by the Institutional Review Board.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Institutional Review Board approval was obtained.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Study subjects or cohorts overlap"}},{"order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}