{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T16:38:52Z","timestamp":1781368732266,"version":"3.54.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T00:00:00Z","timestamp":1762214400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T00:00:00Z","timestamp":1762214400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Natural Science Foundation of Sichuan Province of China","award":["2024NSFSC1188"],"award-info":[{"award-number":["2024NSFSC1188"]}]},{"name":"Sichuan Provincial Science and Technology Program Project","award":["2024YFHZ0051"],"award-info":[{"award-number":["2024YFHZ0051"]}]},{"name":"Sichuan University\u2019s Innovation Project from 0 to 1","award":["2022SCUH0032"],"award-info":[{"award-number":["2022SCUH0032"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-22391-7","type":"journal-article","created":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T18:53:29Z","timestamp":1762282409000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Interpretable radiomics-based machine learning model for differentiating glioblastoma from primary central nervous system lymphoma using contrast-enhanced T1-weighted imaging"],"prefix":"10.1038","volume":"15","author":[{"given":"Xueming","family":"Xia","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiaoyue","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxin","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjun","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiheng","family":"Gou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,4]]},"reference":[{"key":"22391_CR1","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1001\/jama.2023.0023","volume":"329","author":"LR Schaff","year":"2023","unstructured":"Schaff, L. R. & Mellinghoff, I. K. Glioblastoma and other primary brain malignancies in adults: A review. JAMA 329, 574\u2013587. https:\/\/doi.org\/10.1001\/jama.2023.0023 (2023).","journal-title":"JAMA"},{"key":"22391_CR2","doi-asserted-by":"publisher","unstructured":"Ferreri, A. J. M. et al. Primary central nervous system lymphoma. Nat. Rev. Dis. Primers 9 https:\/\/doi.org\/10.1038\/s41572-023-00439-0 (2023).","DOI":"10.1038\/s41572-023-00439-0"},{"key":"22391_CR3","doi-asserted-by":"publisher","unstructured":"Obrador, E. et al. Glioblastoma therapy: Past, present and future. Int. J. Mol. Sci. 25 https:\/\/doi.org\/10.3390\/ijms25052529 (2024).","DOI":"10.3390\/ijms25052529"},{"key":"22391_CR4","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1093\/neuonc\/noac196","volume":"25","author":"K Hoang-Xuan","year":"2023","unstructured":"Hoang-Xuan, K. et al. European association of Neuro-Oncology (EANO) guidelines for treatment of primary central nervous system lymphoma (PCNSL). Neuro Oncol. 25, 37\u201353. https:\/\/doi.org\/10.1093\/neuonc\/noac196 (2023).","journal-title":"Neuro Oncol."},{"key":"22391_CR5","doi-asserted-by":"publisher","first-page":"12","DOI":"10.6004\/jnccn.2023.0002","volume":"21","author":"C Horbinski","year":"2023","unstructured":"Horbinski, C. et al. NCCN Guidelines\u00ae insights: central nervous system Cancers, version 2.2022. J. Natl. Compr. Canc Netw. 21, 12\u201320. https:\/\/doi.org\/10.6004\/jnccn.2023.0002 (2023).","journal-title":"J. Natl. Compr. Canc Netw."},{"key":"22391_CR6","doi-asserted-by":"publisher","first-page":"1287725","DOI":"10.3389\/fonc.2024.1287725","volume":"14","author":"X Chen","year":"2024","unstructured":"Chen, X., Cui, Y. & Zou, L. Treatment advances in high-grade gliomas. Front. Oncol. 14, 1287725. https:\/\/doi.org\/10.3389\/fonc.2024.1287725 (2024).","journal-title":"Front. Oncol."},{"key":"22391_CR7","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1055\/s-0043-1776783","volume":"43","author":"T Shah","year":"2023","unstructured":"Shah, T. & Venur, V. A. Central nervous system lymphoma. Semin Neurol. 43, 825\u2013832. https:\/\/doi.org\/10.1055\/s-0043-1776783 (2023).","journal-title":"Semin Neurol."},{"key":"22391_CR8","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1001\/jama.2023.8587","volume":"330","author":"AP Wang","year":"2023","unstructured":"Wang, A. P. A review of glioblastoma and other primary brain malignancies. JAMA 330, 188\u2013189. https:\/\/doi.org\/10.1001\/jama.2023.8587 (2023).","journal-title":"JAMA"},{"key":"22391_CR9","doi-asserted-by":"publisher","first-page":"867","DOI":"10.1055\/s-0043-1776765","volume":"43","author":"A Campion","year":"2023","unstructured":"Campion, A. & Iv, M. Brain tumor imaging: review of conventional and advanced techniques. Semin Neurol. 43, 867\u2013888. https:\/\/doi.org\/10.1055\/s-0043-1776765 (2023).","journal-title":"Semin Neurol."},{"key":"22391_CR10","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1007\/s10072-023-07111-1","volume":"45","author":"Y Liu","year":"2024","unstructured":"Liu, Y., Lin, H. T. & Zeng, W. Extensive intraventricular glioblastoma mimicking intracranial lymphoma. Neurol. Sci. 45, 375\u2013376. https:\/\/doi.org\/10.1007\/s10072-023-07111-1 (2024).","journal-title":"Neurol. Sci."},{"key":"22391_CR11","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1212\/CON.0000000000001202","volume":"29","author":"JT Jordan","year":"2023","unstructured":"Jordan, J. T. & Gerstner, E. R. Imaging of brain tumors. Continuum (Minneap Minn). 29, 171\u2013193. https:\/\/doi.org\/10.1212\/CON.0000000000001202 (2023).","journal-title":"Continuum (Minneap Minn)"},{"key":"22391_CR12","doi-asserted-by":"publisher","first-page":"100965","DOI":"10.1016\/j.currproblcancer.2023.100965","volume":"47","author":"O Raslan","year":"2023","unstructured":"Raslan, O. et al. Imaging cancer in neuroradiology. Curr. Probl. Cancer. 47, 100965. https:\/\/doi.org\/10.1016\/j.currproblcancer.2023.100965 (2023).","journal-title":"Curr. Probl. Cancer"},{"key":"22391_CR13","doi-asserted-by":"publisher","first-page":"1493","DOI":"10.1001\/jamaoncol.2022.2844","volume":"8","author":"TR Berger","year":"2022","unstructured":"Berger, T. R., Wen, P. Y., Lang-Orsini, M. & Chukwueke, U. N. World health organization 2021 classification of central nervous system tumors and implications for therapy for Adult-Type gliomas: A review. JAMA Oncol. 8, 1493\u20131501. https:\/\/doi.org\/10.1001\/jamaoncol.2022.2844 (2022).","journal-title":"JAMA Oncol."},{"key":"22391_CR14","doi-asserted-by":"publisher","unstructured":"Lan, Z., Li, X., Zhang, X. & Glioblastoma An update in Pathology, molecular mechanisms and biomarkers. Int. J. Mol. Sci. 25 https:\/\/doi.org\/10.3390\/ijms25053040 (2024).","DOI":"10.3390\/ijms25053040"},{"key":"22391_CR15","doi-asserted-by":"publisher","first-page":"249","DOI":"10.2463\/mrms.e.2024-1000","volume":"23","author":"A Hagiwara","year":"2024","unstructured":"Hagiwara, A. & Takahashi, M. Advanced techniques for MR neuroimaging. Magn. Reson. Med. Sci. 23, 249\u2013251. https:\/\/doi.org\/10.2463\/mrms.e.2024-1000 (2024).","journal-title":"Magn. Reson. Med. Sci."},{"key":"22391_CR16","doi-asserted-by":"publisher","unstructured":"Manan, A. A., Yahya, N. A., Taib, N. H. M., Idris, Z. & Manan, H. A. The assessment of white matter integrity alteration pattern in patients with brain tumor utilizing diffusion tensor imaging: A systematic review. Cancers 15 https:\/\/doi.org\/10.3390\/cancers15133326 (2023).","DOI":"10.3390\/cancers15133326"},{"key":"22391_CR17","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1111\/jon.13080","volume":"33","author":"A Aseel","year":"2023","unstructured":"Aseel, A., McCarthy, P. & Mohammed, A. Brain magnetic resonance spectroscopy to differentiate recurrent neoplasm from radiation necrosis: A systematic review and meta-analysis. J. Neuroimaging. 33, 189\u2013201. https:\/\/doi.org\/10.1111\/jon.13080 (2023).","journal-title":"J. Neuroimaging"},{"key":"22391_CR18","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.3390\/curroncol30030203","volume":"30","author":"M C\u00e8","year":"2023","unstructured":"C\u00e8, M. et al. Artificial intelligence in brain tumor imaging: A step toward personalized medicine. Curr. Oncol. 30, 2673\u20132701. https:\/\/doi.org\/10.3390\/curroncol30030203 (2023).","journal-title":"Curr. Oncol."},{"key":"22391_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2025.05.043","author":"X Xia","year":"2025","unstructured":"Xia, X., Qiu, J., Tan, Q., Du, W. & Gou, Q. Radiomics-Based differentiation of primary central nervous system lymphoma and solitary brain metastasis using Contrast-Enhanced T1-Weighted imaging: A retrospective machine learning study. Acad. Radiol. https:\/\/doi.org\/10.1016\/j.acra.2025.05.043 (2025).","journal-title":"Acad. Radiol."},{"key":"22391_CR20","doi-asserted-by":"publisher","first-page":"107063","DOI":"10.1016\/j.compbiomed.2023.107063","volume":"163","author":"U Raghavendra","year":"2023","unstructured":"Raghavendra, U. et al. Brain tumor detection and screening using artificial intelligence techniques: current trends and future perspectives. Comput. Biol. Med. 163, 107063. https:\/\/doi.org\/10.1016\/j.compbiomed.2023.107063 (2023).","journal-title":"Comput. Biol. Med."},{"key":"22391_CR21","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1002\/jmri.27344","volume":"53","author":"W Xia","year":"2020","unstructured":"Xia, W. et al. Multiparametric-MRI\u2010Based radiomics model for differentiating primary central nervous system lymphoma from glioblastoma: development and Cross\u2010Vendor validation. J. Magn. Reson. Imaging. 53, 242\u2013250. https:\/\/doi.org\/10.1002\/jmri.27344 (2020).","journal-title":"J. Magn. Reson. Imaging"},{"key":"22391_CR22","doi-asserted-by":"publisher","first-page":"8703","DOI":"10.1007\/s00330-021-07845-6","volume":"31","author":"G Bathla","year":"2021","unstructured":"Bathla, G. et al. Radiomics-based differentiation between glioblastoma and primary central nervous system lymphoma: a comparison of diagnostic performance across different MRI sequences and machine learning techniques. Eur. Radiol. 31, 8703\u20138713. https:\/\/doi.org\/10.1007\/s00330-021-07845-6 (2021).","journal-title":"Eur. Radiol."},{"key":"22391_CR23","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1016\/j.neurad.2022.11.001","volume":"50","author":"B Joo","year":"2023","unstructured":"Joo, B. et al. Fully automated radiomics-based machine learning models for multiclass classification of single brain tumors: Glioblastoma, lymphoma, and metastasis. J. Neuroradiol. 50, 388\u2013395. https:\/\/doi.org\/10.1016\/j.neurad.2022.11.001 (2023).","journal-title":"J. Neuroradiol."},{"key":"22391_CR24","doi-asserted-by":"publisher","unstructured":"Naser, P. V. et al. Deep learning aided preoperative diagnosis of primary central nervous system lymphoma. iScience 27 https:\/\/doi.org\/10.1016\/j.isci.2024.109023 (2024).","DOI":"10.1016\/j.isci.2024.109023"},{"key":"22391_CR25","doi-asserted-by":"publisher","unstructured":"Guha, A. et al. Classifying primary central nervous system lymphoma from glioblastoma using deep learning and radiomics based machine learning approach - a systematic review and meta-analysis. Front. Oncol. 12 https:\/\/doi.org\/10.3389\/fonc.2022.884173 (2022).","DOI":"10.3389\/fonc.2022.884173"},{"key":"22391_CR26","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1016\/j.crad.2024.03.007","volume":"79","author":"A Guha","year":"2024","unstructured":"Guha, A. et al. How does deep learning\/machine learning perform in comparison to radiologists in distinguishing glioblastomas (or grade IV astrocytomas) from primary CNS lymphomas? A meta-analysis and systematic review. Clin. Radiol. 79, 460\u2013472. https:\/\/doi.org\/10.1016\/j.crad.2024.03.007 (2024).","journal-title":"Clin. Radiol."},{"key":"22391_CR27","doi-asserted-by":"publisher","unstructured":"Silva Santana, L. et al. Application of machine learning for classification of brain tumors: A systematic review and meta-analysis. World Neurosurg. 186, 204\u2013218 e202 https:\/\/doi.org\/10.1016\/j.wneu.2024.03.152 (2024).","DOI":"10.1016\/j.wneu.2024.03.152"},{"key":"22391_CR28","doi-asserted-by":"publisher","first-page":"101464","DOI":"10.1016\/j.xcrm.2024.101464","volume":"5","author":"A Garcia-Ruiz","year":"2024","unstructured":"Garcia-Ruiz, A. et al. An accessible deep learning tool for voxel-wise classification of brain malignancies from perfusion MRI. Cell. Rep. Med. 5, 101464. https:\/\/doi.org\/10.1016\/j.xcrm.2024.101464 (2024).","journal-title":"Cell. Rep. Med."},{"key":"22391_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2025.05.016","author":"X Xia","year":"2025","unstructured":"Xia, X., Wu, W., Tan, Q. & Gou, Q. Interpretable machine learning models for differentiating glioblastoma from solitary brain metastasis using radiomics. Acad. Radiol. https:\/\/doi.org\/10.1016\/j.acra.2025.05.016 (2025).","journal-title":"Acad. Radiol."},{"key":"22391_CR30","doi-asserted-by":"publisher","first-page":"1541350","DOI":"10.3389\/fonc.2025.1541350","volume":"15","author":"Y Sun","year":"2025","unstructured":"Sun, Y. et al. Development and validation of a preoperative magnetic resonance imaging-based and machine learning model for the noninvasive differentiation of intracranial glioblastoma, primary central nervous system lymphoma and brain metastases: a retrospective analysis. Front. Oncol. 15, 1541350. https:\/\/doi.org\/10.3389\/fonc.2025.1541350 (2025).","journal-title":"Front. Oncol."},{"key":"22391_CR31","doi-asserted-by":"publisher","first-page":"111509","DOI":"10.1016\/j.ejrad.2024.111509","volume":"176","author":"E Chukwujindu","year":"2024","unstructured":"Chukwujindu, E., Faiz, H., Ai-Douri, S., Faiz, K. & De Sequeira, A. Role of artificial intelligence in brain tumour imaging. Eur. J. Radiol. 176, 111509. https:\/\/doi.org\/10.1016\/j.ejrad.2024.111509 (2024).","journal-title":"Eur. J. Radiol."},{"key":"22391_CR32","doi-asserted-by":"publisher","DOI":"10.1007\/s12094-025-03948-4","author":"PD Delgado-L\u00f3pez","year":"2025","unstructured":"Delgado-L\u00f3pez, P. D. et al. Artificial intelligence in neuro-oncology: methodological bases, practical applications and ethical and regulatory issues. Clin. Transl. Oncol. https:\/\/doi.org\/10.1007\/s12094-025-03948-4 (2025).","journal-title":"Clin. Transl. Oncol."},{"key":"22391_CR33","doi-asserted-by":"publisher","first-page":"470","DOI":"10.25259\/JNRP_50_2022","volume":"14","author":"J Jaruenpunyasak","year":"2023","unstructured":"Jaruenpunyasak, J., Duangsoithong, R. & Tunthanathip, T. Deep learning for image classification between primary central nervous system lymphoma and glioblastoma in corpus callosal tumors. J. Neurosci. Rural Pract. 14, 470\u2013476. https:\/\/doi.org\/10.25259\/JNRP_50_2022 (2023).","journal-title":"J. Neurosci. Rural Pract."},{"key":"22391_CR34","doi-asserted-by":"publisher","first-page":"109023","DOI":"10.1016\/j.isci.2024.109023","volume":"27","author":"PV Naser","year":"2024","unstructured":"Naser, P. V. et al. Deep learning aided preoperative diagnosis of primary central nervous system lymphoma. iScience 27, 109023. https:\/\/doi.org\/10.1016\/j.isci.2024.109023 (2024).","journal-title":"iScience"},{"key":"22391_CR35","doi-asserted-by":"publisher","unstructured":"Raptis, S., Ilioudis, C. & Theodorou, K. From pixels to prognosis: unveiling radiomics models with SHAP and LIME for enhanced interpretability. Biomedical Phys. Eng. Express. 10 https:\/\/doi.org\/10.1088\/2057-1976\/ad34db (2024).","DOI":"10.1088\/2057-1976\/ad34db"},{"key":"22391_CR36","doi-asserted-by":"publisher","first-page":"1893","DOI":"10.1007\/s00234-024-03451-7","volume":"66","author":"YF Yang","year":"2024","unstructured":"Yang, Y. F., Zhao, E., Shi, Y., Zhang, H. & Yang, Y. Y. Multicenter investigation of preoperative distinction between primary central nervous system lymphomas and glioblastomas through interpretable artificial intelligence models. Neuroradiology 66, 1893\u20131906. https:\/\/doi.org\/10.1007\/s00234-024-03451-7 (2024).","journal-title":"Neuroradiology"},{"key":"22391_CR37","doi-asserted-by":"publisher","first-page":"110244","DOI":"10.1016\/j.clinimag.2024.110244","volume":"113","author":"G Ge","year":"2024","unstructured":"Ge, G., Zhang, J. Z. & Zhang, J. The impact of high-order features on performance of radiomics studies in CT non-small cell lung cancer. Clin. Imaging. 113, 110244. https:\/\/doi.org\/10.1016\/j.clinimag.2024.110244 (2024).","journal-title":"Clin. Imaging"},{"key":"22391_CR38","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1186\/s40644-025-00873-1","volume":"25","author":"RF Valenzuela","year":"2025","unstructured":"Valenzuela, R. F. et al. Building a pre-surgical multiparametric-MRI-based morphologic, qualitative, semiquantitative, first and high-order radiomic predictive treatment response model for undifferentiated pleomorphic sarcoma to replace RECIST. Cancer Imaging. 25, 56. https:\/\/doi.org\/10.1186\/s40644-025-00873-1 (2025).","journal-title":"Cancer Imaging"},{"key":"22391_CR39","doi-asserted-by":"publisher","unstructured":"Karim, M. R. et al. Explainable AI for bioinformatics: Methods, tools and applications. Brief. Bioinform. 24 https:\/\/doi.org\/10.1093\/bib\/bbad236 (2023).","DOI":"10.1093\/bib\/bbad236"},{"key":"22391_CR40","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1186\/s12911-025-02944-6","volume":"25","author":"R Alkhanbouli","year":"2025","unstructured":"Alkhanbouli, R., Matar Abdulla Almadhaani, H., Alhosani, F. & Simsekler, M. C. E. The role of explainable artificial intelligence in disease prediction: a systematic literature review and future research directions. BMC Med. Inf. Decis. Mak. 25, 110. https:\/\/doi.org\/10.1186\/s12911-025-02944-6 (2025).","journal-title":"BMC Med. Inf. Decis. Mak."},{"key":"22391_CR41","doi-asserted-by":"publisher","first-page":"497","DOI":"10.5582\/bst.2024.01342","volume":"18","author":"K Karako","year":"2025","unstructured":"Karako, K. & Tang, W. Applications of and issues with machine learning in medicine: bridging the gap with explainable AI. Biosci. Trends. 18, 497\u2013504. https:\/\/doi.org\/10.5582\/bst.2024.01342 (2025).","journal-title":"Biosci. Trends"},{"key":"22391_CR42","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.csbj.2024.12.027","volume":"27","author":"A Budhkar","year":"2025","unstructured":"Budhkar, A., Song, Q., Su, J. & Zhang, X. Demystifying the black box: A survey on explainable artificial intelligence (XAI) in bioinformatics. Comput. Struct. Biotechnol. J. 27, 346\u2013359. https:\/\/doi.org\/10.1016\/j.csbj.2024.12.027 (2025).","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"22391_CR43","doi-asserted-by":"publisher","first-page":"542","DOI":"10.1016\/j.csbj.2024.08.005","volume":"24","author":"D Muhammad","year":"2024","unstructured":"Muhammad, D. & Bendechache, M. Unveiling the black box: A systematic review of explainable artificial intelligence in medical image analysis. Comput. Struct. Biotechnol. J. 24, 542\u2013560. https:\/\/doi.org\/10.1016\/j.csbj.2024.08.005 (2024).","journal-title":"Comput. Struct. Biotechnol. J."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-22391-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-22391-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-22391-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,9]],"date-time":"2025-11-09T07:03:03Z","timestamp":1762671783000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-22391-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,4]]},"references-count":43,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["22391"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-22391-7","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,4]]},"assertion":[{"value":"22 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"All human participants involved in this study have provided their informed consent to participate. Each participant was fully informed about the purpose, procedures, potential risks, and benefits of the research. They were also assured of the confidentiality of their personal information and their right to withdraw from the study at any time without any negative consequences. Written consent was obtained from all participants prior to their involvement in the research.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"This retrospective study was performed in accordance with the Declaration of Helsinki. This study was approved by the Institutional Review Board (IRB) of our institution, and the IRB approval number is 2024402.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Institutional review board statement"}}],"article-number":"38598"}}