{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T17:50:20Z","timestamp":1776880220489,"version":"3.51.2"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T00:00:00Z","timestamp":1773360000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:00:00Z","timestamp":1776816000000},"content-version":"vor","delay-in-days":40,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100016105","name":"Quzhou Municipal Science and Technology Bureau","doi-asserted-by":"publisher","award":["2022k65"],"award-info":[{"award-number":["2022k65"]}],"id":[{"id":"10.13039\/501100016105","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Cancer"],"DOI":"10.1186\/s12885-026-15853-2","type":"journal-article","created":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T09:54:23Z","timestamp":1773395663000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An explainable radiomics model based on multiparametric magnetic resonance for differentiating benign and malignant orbital tumors"],"prefix":"10.1186","volume":"26","author":[{"given":"Guozheng","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingjian","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rujian","family":"Hong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaowei","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weitao","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,13]]},"reference":[{"issue":"5","key":"15853_CR1","doi-asserted-by":"publisher","first-page":"997","DOI":"10.1016\/j.ophtha.2003.01.002","volume":"111","author":"JA Shields","year":"2004","unstructured":"Shields JA, Shields CL, Scartozzi R. Survey of 1264 patients with orbital tumors and simulating lesions: The 2002 Montgomery Lecture, part 1. Ophthalmology. 2004;111(5):997\u20131008.","journal-title":"Ophthalmology"},{"issue":"10","key":"15853_CR2","doi-asserted-by":"publisher","first-page":"e9039","DOI":"10.1016\/j.crad.2017.04.007","volume":"72","author":"XQ Xu","year":"2017","unstructured":"Xu XQ, Qian W, Ma G, Hu H, Su GY, Liu H, et al. Combined diffusion-weighted imaging and dynamic contrast-enhanced MRI for differentiating radiologically indeterminate malignant from benign orbital masses. Clin Radiol. 2017;72(10):e9039\u201390315.","journal-title":"Clin Radiol"},{"issue":"9","key":"15853_CR3","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1016\/j.diii.2022.03.007","volume":"103","author":"A Lecler","year":"2022","unstructured":"Lecler A, Duron L, Charlson E, Kolseth C, Kossler AL, Wintermark M, et al. Comparison between 7 Tesla and 3 Tesla MRI for characterizing orbital lesions. Diagn Interv Imaging. 2022;103(9):433\u20139.","journal-title":"Diagn Interv Imaging"},{"issue":"4","key":"15853_CR4","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1097\/IIO.0000000000000288","volume":"59","author":"LM Cohen","year":"2019","unstructured":"Cohen LM, Yoon MK. Update on Current Aspects of Orbital Imaging: CT, MRI, and Ultrasonography. Int Ophthalmol Clin. 2019;59(4):69\u201379.","journal-title":"Int Ophthalmol Clin"},{"issue":"8","key":"15853_CR5","doi-asserted-by":"publisher","first-page":"1329","DOI":"10.1038\/s41433-018-0078-3","volume":"32","author":"A Koukkoulli","year":"2018","unstructured":"Koukkoulli A, Pilling JD, Patatas K, El-Hindy N, Chang B, Kalantzis G. How accurate is the clinical and radiological evaluation of orbital lesions in comparison to surgical orbital biopsy? Eye. 2018;32(8):1329\u201333.","journal-title":"Eye"},{"issue":"5","key":"15853_CR6","doi-asserted-by":"publisher","first-page":"856","DOI":"10.1016\/j.survophthal.2021.01.008","volume":"66","author":"A Martel","year":"2021","unstructured":"Martel A, Baillif S, Nahon-Esteve S, Gastaud L, Bertolotto C, Lassalle S, et al. Orbital exenteration: an updated review with perspectives. Surv Ophthalmol. 2021;66(5):856\u201376.","journal-title":"Surv Ophthalmol"},{"issue":"4","key":"15853_CR7","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.ejca.2011.11.036","volume":"48","author":"P Lambin","year":"2012","unstructured":"Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48(4):441\u20136.","journal-title":"Eur J Cancer"},{"issue":"2","key":"15853_CR8","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1148\/radiol.2020191145","volume":"295","author":"A Zwanenburg","year":"2020","unstructured":"Zwanenburg A, Valli\u00e8res M, Abdalah MA, Aerts HJWL, Andrearczyk V, Apte A, et al. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology. 2020;295(2):328\u201338.","journal-title":"Radiology"},{"issue":"3","key":"15853_CR9","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1097\/RLI.0000000000000722","volume":"56","author":"L Duron","year":"2021","unstructured":"Duron L, Heraud A, Charbonneau F, Zmuda M, Savatovsky J, Fournier L, et al. A Magnetic Resonance Imaging Radiomics Signature to Distinguish Benign From Malignant Orbital Lesions. Invest Radiol. 2021;56(3):173\u201380.","journal-title":"Invest Radiol"},{"issue":"2","key":"15853_CR10","first-page":"563","volume":"278","author":"RJ Gillies","year":"2015","unstructured":"Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures. They Are Data Radiol. 2015;278(2):563\u201377.","journal-title":"They Are Data Radiol"},{"issue":"12","key":"15853_CR11","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1038\/nrclinonc.2017.141","volume":"14","author":"P Lambin","year":"2017","unstructured":"Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14(12):749\u201362.","journal-title":"Nat Rev Clin Oncol"},{"issue":"2","key":"15853_CR12","doi-asserted-by":"publisher","first-page":"596","DOI":"10.1002\/jmri.27098","volume":"52","author":"Q Zhang","year":"2020","unstructured":"Zhang Q, Peng Y, Liu W, Bai J, Zheng J, Yang X, et al. Radiomics Based on Multimodal MRI for the Differential Diagnosis of Benign and Malignant Breast Lesions. J Magn Reson Imaging. 2020;52(2):596\u2013607.","journal-title":"J Magn Reson Imaging"},{"issue":"10","key":"15853_CR13","doi-asserted-by":"publisher","first-page":"2464","DOI":"10.1111\/os.14148","volume":"16","author":"W Geng","year":"2024","unstructured":"Geng W, Zhu J, Li M, Pi B, Wang X, Xing J, et al. Radiomics Based on Multimodal magnetic resonance imaging for the Differential Diagnosis of Benign and Malignant Vertebral Compression Fractures. Orthop Surg. 2024;16(10):2464\u201374.","journal-title":"Orthop Surg"},{"issue":"Suppl 1","key":"15853_CR14","doi-asserted-by":"publisher","first-page":"668","DOI":"10.1136\/annrheumdis-2022-eular.3452","volume":"81","author":"S Tay","year":"2022","unstructured":"Tay S, Stephenson M, Allameen N, Narayanan S, Lee B, Mak A. A Multimodal Magnetic Resonance Imaging Study of Cognitive Function in Systemic Lupus Erythematosus: A Machine Learning Approach. Ann Rheum Dis. 2022;81(Suppl 1):668.","journal-title":"Ann Rheum Dis"},{"issue":"1","key":"15853_CR15","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1186\/s40644-022-00492-0","volume":"22","author":"J Nakagawa","year":"2022","unstructured":"Nakagawa J, Fujima N, Hirata K, Tang M, Tsuneta S, Suzuki J, et al. Utility of the deep learning technique for the diagnosis of orbital invasion on CT in patients with a nasal or sinonasal tumor. Cancer Imaging. 2022;22(1):52.","journal-title":"Cancer Imaging"},{"issue":"10","key":"15853_CR16","first-page":"737","volume":"59","author":"E O\u2019Shaughnessy","year":"2024","unstructured":"O\u2019Shaughnessy E, Senicourt L, Mambour N, Savatovsky J, Duron L, &Lecler A. Toward Precision Diagnosis: Machine Learning in Identifying Malignant Orbital Tumors With Multiparametric 3 T MRI. Invest. Radiol. 2024;59(10):737\u201345.","journal-title":"Radiol"},{"issue":"10","key":"15853_CR17","doi-asserted-by":"publisher","first-page":"6922","DOI":"10.1007\/s00330-022-08857-6","volume":"32","author":"X Xie","year":"2022","unstructured":"Xie X, Yang L, Zhao F, Wang D, Zhang H, He X, et al. A deep learning model combining multimodal radiomics, clinical and imaging features for differentiating ocular adnexal lymphoma from idiopathic orbital inflammation. Eur Radiol. 2022;32(10):6922\u201332.","journal-title":"Eur Radiol"},{"key":"15853_CR18","doi-asserted-by":"crossref","unstructured":"Tooley AA, Tailor P, Tran AQ, Garrity JA, Eckel L, Link MJ. Differentiating intradiploic orbital dermoid and epidermoid cysts utilizing clinical features and machine learning. Indian J Ophthalmol.2022;70 (6).","DOI":"10.4103\/ijo.IJO_52_22"},{"issue":"1","key":"15853_CR19","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1002\/jmri.26058","volume":"48","author":"I Dregely","year":"2018","unstructured":"Dregely I, Prezzi D, Kelly-Morland C, Roccia E, Neji R, Goh V. Imaging biomarkers in oncology: Basics and application to MRI. J Magn Reson Imaging. 2018;48(1):13\u201326.","journal-title":"J Magn Reson Imaging"},{"issue":"3","key":"15853_CR20","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1038\/nrclinonc.2016.162","volume":"14","author":"JP O\u2019Connor","year":"2016","unstructured":"O\u2019Connor JP, Aboagye EO, Adams JE, Aerts HJ, Barrington SF, Beer AJ, et al. Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol. 2016;14(3):169\u201386.","journal-title":"Nat Rev Clin Oncol"},{"issue":"12","key":"15853_CR21","doi-asserted-by":"publisher","first-page":"8737","DOI":"10.1007\/s00330-022-08887-0","volume":"32","author":"Y Wang","year":"2022","unstructured":"Wang Y, Lang J, Zuo JZ, Dong Y, Hu Z, Xu X, et al. The radiomic-clinical model using the SHAP method for assessing the treatment response of whole-brain radiotherapy: a multicentric study. Eur Radiol. 2022;32(12):8737\u201347.","journal-title":"Eur Radiol"},{"issue":"1","key":"15853_CR22","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1007\/s00330-020-07110-2","volume":"31","author":"Y Hou","year":"2020","unstructured":"Hou Y, Xie X, Chen J, Lv P, Jiang S, He X, et al. Bag-of-features-based radiomics for differentiation of ocular adnexal lymphoma and idiopathic orbital inflammation from contrast-enhanced MRI. Eur Radiol. 2020;31(1):24\u201333.","journal-title":"Eur Radiol"},{"issue":"3","key":"15853_CR23","doi-asserted-by":"publisher","first-page":"814","DOI":"10.1097\/SCS.0000000000008446","volume":"33","author":"Q Han","year":"2022","unstructured":"Han Q, Du L, Mo Y, Huang C, Yuan Q. Machine Learning Based Non-Enhanced CT Radiomics for the Identification of Orbital Cavernous Venous Malformations: An Innovative Tool. J Craniofac Surg. 2022;33(3):814\u201320.","journal-title":"J Craniofac Surg"},{"issue":"1","key":"15853_CR24","doi-asserted-by":"publisher","first-page":"560","DOI":"10.1007\/s00330-023-10031-5","volume":"34","author":"J Ren","year":"2023","unstructured":"Ren J, Yuan Y, Qi M, Tao X. MRI-based radiomics nomogram for distinguishing solitary fibrous tumor from schwannoma in the orbit: a two-center study. Eur Radiol. 2023;34(1):560\u20138.","journal-title":"Eur Radiol"},{"issue":"2","key":"15853_CR25","doi-asserted-by":"publisher","first-page":"324","DOI":"10.1016\/j.ejrad.2015.11.041","volume":"85","author":"SR Ro","year":"2015","unstructured":"Ro SR, Asbach P, Siebert E, Bertelmann E, Hamm B, Erb-Eigner K. Characterization of orbital masses by multiparametric. MRI Eur J Radiol. 2015;85(2):324\u201336.","journal-title":"MRI Eur J Radiol"},{"issue":"1","key":"15853_CR26","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1002\/jmri.25349","volume":"45","author":"XQ Xu","year":"2016","unstructured":"Xu XQ, Hu H, Liu H, Wu JF, Cao P, Shi HB, et al. Benign and malignant orbital lymphoproliferative disorders: Differentiating using multiparametric MRI at 3.0T. J Magn Reson Imaging. 2016;45(1):167\u201376.","journal-title":"J Magn Reson Imaging"},{"issue":"5","key":"15853_CR27","doi-asserted-by":"publisher","first-page":"1594","DOI":"10.1002\/jmri.28404","volume":"57","author":"L Yang","year":"2022","unstructured":"Yang L, Zhang H, Xie X, Jiang S, Zhang H, Cao X, et al. MRI-Based Radiomics Nomogram for Preoperative Differentiation Between Ocular Adnexal Lymphoma and Idiopathic Orbital Inflammation. J Magn Reson Imaging. 2022;57(5):1594\u2013604.","journal-title":"J Magn Reson Imaging"},{"issue":"1","key":"15853_CR28","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1097\/IIO.0000000000000298","volume":"60","author":"GW Armstrong","year":"2020","unstructured":"Armstrong GW, Lorch AC. A(eye): A Review of Current Applications of Artificial Intelligence and Machine Learning in Ophthalmology. Int Ophthalmol Clin. 2020;60(1):57\u201371.","journal-title":"Int Ophthalmol Clin"},{"key":"15853_CR29","doi-asserted-by":"publisher","first-page":"105590","DOI":"10.1016\/j.ijmedinf.2024.105590","volume":"191","author":"CC Huang","year":"2024","unstructured":"Huang CC, Kuo WY, Shen YT, Chen CJ, Lin HJ, Hsu CC, et al. Artificial intelligence prediction of In-Hospital mortality in patients with dementia: A multi-center study. Int J Med Inf. 2024;191:105590.","journal-title":"Int J Med Inf"},{"key":"15853_CR30","doi-asserted-by":"crossref","unstructured":"Ma H, Kang X, Duan S, Li Y. Efficient Structural Optimization under Transient Impact Loads Using Multilayer Perceptron and Genetic Algorithms. Int J Nonlin Mech. 2024;104950.","DOI":"10.1016\/j.ijnonlinmec.2024.104950"},{"key":"15853_CR31","doi-asserted-by":"publisher","first-page":"105641","DOI":"10.1016\/j.archger.2024.105641","volume":"129","author":"M Fu","year":"2024","unstructured":"Fu M, Liu Y, Hou Z, Wang Z. Interpretable prediction of acute ischemic stroke after hip fracture in patients 65 years and older based on machine learning and SHAP. Arch Gerontol Geriat. 2024;129:105641.","journal-title":"Arch Gerontol Geriat"},{"issue":"16","key":"15853_CR32","doi-asserted-by":"publisher","first-page":"8761","DOI":"10.1021\/acs.jmedchem.9b01101","volume":"63","author":"R Rodr\u00edguez-P\u00e9rez","year":"2019","unstructured":"Rodr\u00edguez-P\u00e9rez R, Bajorath J. Interpretation of Compound Activity Predictions from Complex Machine Learning Models Using Local Approximations and Shapley Values. J Med Chem. 2019;63(16):8761\u201377.","journal-title":"J Med Chem"},{"key":"15853_CR33","doi-asserted-by":"crossref","unstructured":"Giraud P, Giraud P, Nicolas E, Boisselier P, Alfonsi M, Rives M et al. Interpretable Machine Learning Model for Locoregional Relapse Prediction in Oropharyngeal Cancers. Cancers (Basel). 2020;13 (1).","DOI":"10.3390\/cancers13010057"},{"key":"15853_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.future.2020.04.038","volume":"112","author":"L Ma","year":"2020","unstructured":"Ma L, Xiao Z, Li K, Li S, Li J, Yi X. Game theoretic interpretability for learning based preoperative gliomas grading. Future Gener Comp Sy. 2020;112:1\u201310.","journal-title":"Future Gener Comp Sy"},{"key":"15853_CR35","first-page":"637","volume":"4","author":"R Li","year":"2020","unstructured":"Li R, et al. Machine Learning-Based Interpretation and Visualization of Nonlinear Interactions in Prostate Cancer Survival. Jco Clin Cancer Inf. 2020;4:637\u201346.","journal-title":"Jco Clin Cancer Inf"},{"key":"15853_CR36","doi-asserted-by":"crossref","unstructured":"Verma S, Kumar M. A hybrid machine learning model for skin disease classification using discrete wavelet transform and gray level co-occurrence matrix (GLCM). Multimed Tools. 2024.","DOI":"10.1007\/s11042-024-19449-5"},{"issue":"2","key":"15853_CR37","doi-asserted-by":"publisher","first-page":"E256","DOI":"10.1148\/radiol.2021219005","volume":"299","author":"MR Tomaszewski","year":"2021","unstructured":"Tomaszewski MR, Gillies RJ. The Biological Meaning of Radiomic Features. Radiology. 2021;299(2):E256.","journal-title":"Radiology"},{"issue":"32","key":"15853_CR38","doi-asserted-by":"publisher","first-page":"53664","DOI":"10.18632\/oncotarget.15399","volume":"8","author":"Y Liu","year":"2017","unstructured":"Liu Y, Liu S, Qu F, Li Q, Cheng R, Ye Z. Tumor heterogeneity assessed by texture analysis on contrast-enhanced CT in lung adenocarcinoma: association with pathologic grade. Oncotarget. 2017;8(32):53664\u201374.","journal-title":"Oncotarget"},{"issue":"9","key":"15853_CR39","doi-asserted-by":"publisher","first-page":"1506","DOI":"10.1016\/j.ejrad.2013.03.001","volume":"82","author":"Y Yuan","year":"2013","unstructured":"Yuan Y, Kuai XP, Chen XS, Tao XF. Assessment of dynamic contrast-enhanced magnetic resonance imaging in the differentiation of malignant from benign orbital masses. Eur J Radiol. 2013;82(9):1506\u201311.","journal-title":"Eur J Radiol"},{"issue":"1","key":"15853_CR40","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1186\/s12880-025-01771-5","volume":"25","author":"J Li","year":"2025","unstructured":"Li J, Zhou C, Qu X, Du L, Yuan Q, Han Q, et al. Perilesional dominance: radiomics of multiparametric MRI enhances differentiation of IgG4-Related ophthalmic disease and orbital MALT lymphoma. BMC Med Imaging. 2025;25(1):238.","journal-title":"BMC Med Imaging"},{"issue":"12","key":"15853_CR41","doi-asserted-by":"publisher","first-page":"8099","DOI":"10.1007\/s00330-022-08943-9","volume":"32","author":"Z He","year":"2022","unstructured":"He Z, Mao Y, Lu S, Tan L, Xiao J, Tan P, et al. Machine learning-based radiomics for histological classification of parotid tumors using morphological MRI: a comparative study. Eur Radiol. 2022;32(12):8099\u2013110.","journal-title":"Eur Radiol"},{"key":"15853_CR42","doi-asserted-by":"publisher","first-page":"1388156","DOI":"10.3389\/fonc.2024.1388156","volume":"14","author":"S Ou","year":"2024","unstructured":"Ou S, Lin Y, Zhang Y, Shi K, Wu H. Epidemiology and tumor microenvironment of ocular surface and orbital tumors on growth and malignant transformation. Front Oncol. 2024;14:1388156.","journal-title":"Front Oncol"},{"issue":"10","key":"15853_CR43","doi-asserted-by":"publisher","first-page":"3853","DOI":"10.21037\/tcr-22-1626","volume":"11","author":"C Ladbury","year":"2022","unstructured":"Ladbury C, Zarinshenas R, Semwal H, Tam A, Vaidehi N, Rodin AS, et al. Utilization of model-agnostic explainable artificial intelligence frameworks in oncology: a narrative review. Transl Cancer Res. 2022;11(10):3853\u201368.","journal-title":"Transl Cancer Res"},{"issue":"2","key":"15853_CR44","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1148\/radiol.2018180946","volume":"290","author":"HC Kniep","year":"2018","unstructured":"Kniep HC, Madesta F, Schneider T, Hanning U, Sch\u00f6nfeld MH, Sch\u00f6n G, et al. Radiomics of Brain MRI: Utility in Prediction of Metastatic Tumor Type. Radiology. 2018;290(2):479\u201387.","journal-title":"Radiology"},{"issue":"2","key":"15853_CR45","doi-asserted-by":"publisher","first-page":"e231319","DOI":"10.1148\/radiol.231319","volume":"310","author":"P Whybra","year":"2024","unstructured":"Whybra P, Zwanenburg A, Andrearczyk V, Schaer R, Apte AP, Ayotte A, et al. The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights. Radiology. 2024;310(2):e231319.","journal-title":"Radiology"}],"container-title":["BMC Cancer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12885-026-15853-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12885-026-15853-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12885-026-15853-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T17:01:59Z","timestamp":1776877319000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12885-026-15853-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,13]]},"references-count":45,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["15853"],"URL":"https:\/\/doi.org\/10.1186\/s12885-026-15853-2","relation":{},"ISSN":["1471-2407"],"issn-type":[{"value":"1471-2407","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,13]]},"assertion":[{"value":"18 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study complied with the declaration of Helsinki and was approved by the Human Research Ethics Committee of the Quzhou People\u2019s Hospital. Informed consent was waived owing to the retrospective nature of the study and approved by the Human Research Ethics Committee of the Quzhou People\u2019s Hospital.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not Applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"507"}}