{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T10:51:03Z","timestamp":1782903063619,"version":"3.54.5"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T00:00:00Z","timestamp":1760745600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T00:00:00Z","timestamp":1760745600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"MEXT\/JSPS WISE Program: Advanced Graduate Program for Future Medicine and Health Care, Tohoku University"},{"name":"CREST Grant","award":["No. JPMJCR15D1"],"award-info":[{"award-number":["No. JPMJCR15D1"]}]},{"name":"JSPS KAKENHI","award":["JP20 K16687"],"award-info":[{"award-number":["JP20 K16687"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Surg Today"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study aimed to assess the performance of a deep learning model using multimodal imaging for detecting lymph node metastasis in esophageal cancer in comparison to expert assessments.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A retrospective analysis was performed for 521 lymph nodes from 167 patients with esophageal cancer who underwent esophagectomy. Deep learning models were developed based on multimodal imaging, including non-contrast-enhanced computed tomography, contrast-enhanced computed tomography, and positron emission tomography imaging. The diagnostic performance was evaluated and compared with expert assessments using a receiver operating characteristic curve analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The area under the receiver operating characteristic curve values for the deep learning model were 0.81 with multimodal imaging, 0.73 with non-contrast-enhanced computed tomography, 0.72 with contrast-enhanced computed tomography, and 0.75 with positron emission tomography were calculated. The area under the curve of the deep learning model (0.81) demonstrated diagnostic performance comparable to that of experienced experts (area under the curve, 0.84;\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.62, DeLong\u2019s test).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The multimodal deep learning model using computed tomography and positron emission tomography demonstrated performance comparable to that of experts in diagnosing the presence of lymph node metastasis, a key prognostic factor in esophageal cancer, suggesting its potential clinical utility.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00595-025-03152-5","type":"journal-article","created":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T17:51:25Z","timestamp":1760809885000},"page":"462-469","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Utility of multimodal deep learning model to diagnose lymph node metastasis in esophageal cancer using computed tomography and positron emission tomography images"],"prefix":"10.1007","volume":"56","author":[{"given":"Yasuharu","family":"Shinozaki","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hirotaka","family":"Ishida","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomofumi","family":"Kaneno","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eichi","family":"Takaya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomoya","family":"Kobayashi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yusuke","family":"Taniyama","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chiaki","family":"Sato","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroshi","family":"Okamoto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yohei","family":"Ozawa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takuya","family":"Ueda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takashi","family":"Kamei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,18]]},"reference":[{"key":"3152_CR1","first-page":"209","volume":"71","author":"H Sung","year":"2021","unstructured":"Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209\u201349.","journal-title":"CA Cancer J Clin"},{"key":"3152_CR2","doi-asserted-by":"publisher","first-page":"1010","DOI":"10.1007\/s12328-020-01237-x","volume":"13","author":"DJ Uhlenhopp","year":"2020","unstructured":"Uhlenhopp DJ, Then EO, Sunkara T, Gaduputi V. Epidemiology of esophageal cancer: update in global trends, etiology and risk factors. Clin J Gastroenterol. 2020;13:1010\u201321.","journal-title":"Clin J Gastroenterol"},{"key":"3152_CR3","doi-asserted-by":"publisher","first-page":"1410","DOI":"10.1007\/s00595-024-02867-1","volume":"54","author":"N Hokamura","year":"2024","unstructured":"Hokamura N, Fukagawa T, Fukushima R, Kiyokawa T, Horikawa M, Kumata Y, et al. Pembrolizumab plus cisplatin and fluorouracil as induction chemotherapy followed by definitive chemoradiotherapy for patients with cT4 and\/or supraclavicular lymph node metastasis (M1Lym) of esophageal squamous cell carcinoma. Surg Today. 2024;54:1410\u20133.","journal-title":"Surg Today"},{"key":"3152_CR4","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1097\/SLA.0b013e31815aaadf","volume":"247","author":"C Mariette","year":"2008","unstructured":"Mariette C, Piessen G, Briez N, Triboulet JP. The number of metastatic lymph nodes and the ratio between metastatic and examined lymph nodes are independent prognostic factors in esophageal cancer regardless of neoadjuvant chemoradiation or lymphadenectomy extent. Ann Surg. 2008;247:365\u201371.","journal-title":"Ann Surg"},{"key":"3152_CR5","doi-asserted-by":"publisher","first-page":"724","DOI":"10.1111\/dote.12520","volume":"29","author":"TW Rice","year":"2016","unstructured":"Rice TW, Chen LQ, Hofstetter WL, Smithers BM, Rusch VW, Wijnhoven BP, et al. Worldwide Esophageal Cancer Collaboration: pathologic staging data. Dis Esophagus. 2016;29:724\u201333.","journal-title":"Dis Esophagus"},{"key":"3152_CR6","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1097\/SLA.0000000000002824","volume":"269","author":"E Visser","year":"2019","unstructured":"Visser E, Markar SR, Ruurda JP, Hanna GB, van Hillegersberg R. Prognostic value of lymph node yield on overall survival in esophageal cancer patients: a systematic review and meta-analysis. Ann Surg. 2019;269:261\u20138.","journal-title":"Ann Surg"},{"key":"3152_CR7","doi-asserted-by":"publisher","first-page":"1090","DOI":"10.1016\/S1470-2045(15)00040-6","volume":"16","author":"J Shapiro","year":"2015","unstructured":"Shapiro J, van Lanschot JJB, Hulshof MCCM, van Hagen P, van Berge Henegouwen MI, Wijnhoven BPL, et al. Neoadjuvant chemoradiotherapy plus surgery versus surgery alone for oesophageal or junctional cancer (CROSS): long-term results of a randomised controlled trial. Lancet Oncol. 2015;16:1090\u20138.","journal-title":"Lancet Oncol"},{"key":"3152_CR8","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1245\/s10434-011-2049-9","volume":"19","author":"N Ando","year":"2012","unstructured":"Ando N, Kato H, Igaki H, Shinoda M, Ozawa S, Shimizu H, et al. A randomized trial comparing postoperative adjuvant chemotherapy with cisplatin and 5-fluorouracil versus preoperative chemotherapy for localized advanced squamous cell carcinoma of the thoracic esophagus (JCOG9907). Ann Surg Oncol. 2012;19:68\u201374.","journal-title":"Ann Surg Oncol"},{"key":"3152_CR9","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/S0140-6736(24)00745-1","volume":"404","author":"K Kato","year":"2024","unstructured":"Kato K, Machida R, Ito Y, Daiko H, Ozawa S, Ogata T, et al. Doublet chemotherapy, triplet chemotherapy, or doublet chemotherapy combined with radiotherapy as neoadjuvant treatment for locally advanced oesophageal cancer (JCOG1109 NExT): a randomised, controlled, open-label, phase 3 trial. Lancet. 2024;404:55\u201366.","journal-title":"Lancet"},{"key":"3152_CR10","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1007\/s12149-008-0209-1","volume":"23","author":"M Okada","year":"2009","unstructured":"Okada M, Murakami T, Kumano S, Kuwabara M, Shimono T, Hosono M, et al. Integrated FDG-PET\/CT compared with intravenous contrast-enhanced CT for evaluation of metastatic regional lymph nodes in patients with resectable early stage esophageal cancer. Ann Nucl Med. 2009;23:73\u201380.","journal-title":"Ann Nucl Med"},{"key":"3152_CR11","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1186\/s40644-019-0225-5","volume":"19","author":"JY Lee","year":"2019","unstructured":"Lee JY, Kim YH, Park YJ, Park SB, Chung HW, Zo JI, et al. Improved detection of metastatic lymph nodes in oesophageal squamous cell carcinoma by combined interpretation of fluorine-18-fluorodeoxyglucose positron-emission tomography\/computed tomography. Cancer Imaging. 2019;19:40.","journal-title":"Cancer Imaging"},{"key":"3152_CR12","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436\u201344.","journal-title":"Nature"},{"key":"3152_CR13","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, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542:115\u20138.","journal-title":"Nature"},{"key":"3152_CR14","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1001\/jama.2016.17216","volume":"316","author":"V Gulshan","year":"2016","unstructured":"Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316:2402\u201310.","journal-title":"JAMA"},{"key":"3152_CR15","doi-asserted-by":"publisher","first-page":"E1004","DOI":"10.1055\/a-1475-3624","volume":"9","author":"Y Mori","year":"2021","unstructured":"Mori Y, Kudo SE, Misawa M, Hotta K, Kazuo O, Saito S, et al. Artificial intelligence-assisted colonic endocytoscopy for cancer recognition: a multicenter study. Endosc Int Open. 2021;9:E1004\u201311.","journal-title":"Endosc Int Open"},{"key":"3152_CR16","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1007\/s10120-018-0793-2","volume":"21","author":"T Hirasawa","year":"2018","unstructured":"Hirasawa T, Aoyama K, Tanimoto T, Ishihara S, Shichijo S, Ozawa T, et al. Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric Cancer. 2018;21:653\u201360.","journal-title":"Gastric Cancer"},{"key":"3152_CR17","doi-asserted-by":"publisher","first-page":"612","DOI":"10.1007\/s10388-021-00826-0","volume":"18","author":"M Takeuchi","year":"2021","unstructured":"Takeuchi M, Seto T, Hashimoto M, Ichihara N, Morimoto Y, Kawakubo H, et al. Performance of a deep learning-based identification system for esophageal cancer from CT images. Esophagus. 2021;18:612\u201320.","journal-title":"Esophagus"},{"key":"3152_CR18","doi-asserted-by":"crossref","unstructured":"Pham T D. 2020 Classification of benign and metastatic lymph nodes in lung cancer with deep learning. 2020 IEEE 20th international conference on bioinformatics and bioengineering (BIBE).;728\u201333.","DOI":"10.1109\/BIBE50027.2020.00124"},{"key":"3152_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104715","volume":"136","author":"Z Liu","year":"2021","unstructured":"Liu Z, Ni S, Yang C, Sun W, Huang D, Su H, et al. Axillary lymph node metastasis prediction by contrast-enhanced computed tomography images for breast cancer patients based on deep learning. Comput Biol Med. 2021;136:104715.","journal-title":"Comput Biol Med"},{"key":"3152_CR20","doi-asserted-by":"publisher","first-page":"382","DOI":"10.3390\/genes9080382","volume":"9","author":"S Liang","year":"2018","unstructured":"Liang S, Zhang R, Liang D, Song T, Ai T, Xia C, et al. Multimodal 3D DenseNet for IDH genotype prediction in gliomas. Genes (Basel). 2018;9:382.","journal-title":"Genes (Basel)"},{"key":"3152_CR21","doi-asserted-by":"publisher","first-page":"18800","DOI":"10.1038\/s41598-021-98408-8","volume":"11","author":"S Joo","year":"2021","unstructured":"Joo S, Ko ES, Kwon S, Jeon E, Jung H, Kim JY, et al. Multimodal deep learning models for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer. Sci Rep. 2021;11:18800.","journal-title":"Sci Rep"},{"key":"3152_CR22","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1007\/s10388-024-01054-y","volume":"21","author":"S Mine","year":"2024","unstructured":"Mine S, Tanaka K, Kawachi H, Shirakawa Y, Kitagawa Y, Toh Y, et al. Japanese classification of esophageal cancer, 12th edition: part I. Esophagus. 2024;21:179\u2013215.","journal-title":"Esophagus"},{"key":"3152_CR23","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1007\/s10388-024-01048-w","volume":"21","author":"Y Doki","year":"2024","unstructured":"Doki Y, Tanaka K, Kawachi H, Shirakawa Y, Kitagawa Y, Toh Y, et al. Japanese classification of esophageal cancer, 12th edition: part II. Esophagus. 2024;21:216\u201369.","journal-title":"Esophagus"},{"key":"3152_CR24","doi-asserted-by":"publisher","first-page":"954","DOI":"10.1245\/ASO.2003.12.002","volume":"10","author":"JV R\u00e4s\u00e4nen","year":"2003","unstructured":"R\u00e4s\u00e4nen JV, Sihvo EI, Knuuti MJ, Minn HR, Luostarinen ME, Laippala P, et al. Prospective analysis of accuracy of positron emission tomography, computed tomography, and endoscopic ultrasonography in staging of adenocarcinoma of the esophagus and the esophagogastric junction. Ann Surg Oncol. 2003;10:954\u201360.","journal-title":"Ann Surg Oncol"},{"key":"3152_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10388-018-0641-9","volume":"16","author":"Y Kitagawa","year":"2019","unstructured":"Kitagawa Y, Uno T, Oyama T, Kato K, Kato H, Kawakubo H, et al. Esophageal cancer practice guidelines 2017 edited by the Japan Esophageal Society: part 1. Esophagus. 2019;16:1\u201324.","journal-title":"Esophagus"},{"key":"3152_CR26","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1002\/ags3.12754","volume":"8","author":"H Ishida","year":"2024","unstructured":"Ishida H, Fukutomi T, Taniyama Y, Sato C, Okamoto H, Ozawa Y, et al. Blood flow ratio in the gastric conduit measured by laser Doppler flowmetry: a predictor of anastomotic leakage after esophagectomy. Ann Gastroenterol Surg. 2024;8:234\u201342.","journal-title":"Ann Gastroenterol Surg"},{"key":"3152_CR27","doi-asserted-by":"publisher","first-page":"1663","DOI":"10.1007\/s00268-016-3454-9","volume":"40","author":"Y Ozawa","year":"2016","unstructured":"Ozawa Y, Kamei T, Nakano T, Taniyama Y, Miyagi S, Ohuchi N. Characteristics of postoperative recurrence in lymph node-negative superficial esophageal carcinoma. World J Surg. 2016;40:1663\u201371.","journal-title":"World J Surg"},{"key":"3152_CR28","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1093\/icvts\/ivu336","volume":"20","author":"Y Taniyama","year":"2015","unstructured":"Taniyama Y, Miyata G, Kamei T, Nakano T, Abe S, Katsura K, et al. Complications following recurrent laryngeal nerve lymph node dissection in oesophageal cancer surgery. Interact Cardiovasc Thorac Surg. 2015;20:41\u20136.","journal-title":"Interact Cardiovasc Thorac Surg"},{"key":"3152_CR29","doi-asserted-by":"publisher","first-page":"584","DOI":"10.1007\/s00268-001-0271-5","volume":"26","author":"W Schr\u00f6der","year":"2002","unstructured":"Schr\u00f6der W, Baldus SE, M\u00f6nig SP, Beckurts TK, Dienes HP, H\u00f6lscher AH. Lymph node staging of esophageal squamous cell carcinoma in patients with and without neoadjuvant radiochemotherapy: histomorphologic analysis. World J Surg. 2002;26:584\u20137.","journal-title":"World J Surg"},{"key":"3152_CR30","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1007\/s00330-017-4935-4","volume":"28","author":"J Liu","year":"2018","unstructured":"Liu J, Wang Z, Shao H, Qu D, Liu J, Yao L. Improving CT detection sensitivity for nodal metastases in oesophageal cancer with combination of smaller size and lymph node axial ratio. Eur Radiol. 2018;28:188\u201395.","journal-title":"Eur Radiol"},{"key":"3152_CR31","doi-asserted-by":"publisher","first-page":"815","DOI":"10.1016\/j.tranon.2018.04.005","volume":"11","author":"C Shen","year":"2018","unstructured":"Shen C, Liu Z, Wang Z, Guo J, Zhang H, Wang Y, et al. Building CT radiomics based nomogram for preoperative esophageal cancer patients lymph node metastasis prediction. Transl Oncol. 2018;11:815\u201324.","journal-title":"Transl Oncol"},{"key":"3152_CR32","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition. 2016;770\u20138.","DOI":"10.1109\/CVPR.2016.90"},{"key":"3152_CR33","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.acra.2006.09.048","volume":"14","author":"L Hadjiiski","year":"2007","unstructured":"Hadjiiski L, Chan HP, Sahiner B, Helvie MA, Roubidoux MA. Quasi-continuous and discrete confidence rating scales for observer performance studies: effects on ROC analysis. Acad Radiol. 2007;14:38\u201348.","journal-title":"Acad Radiol"},{"key":"3152_CR34","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1186\/s13045-021-01167-2","volume":"14","author":"R Gao","year":"2021","unstructured":"Gao R, Zhao S, Aishanjiang K, Cai H, Wei T, Zhang Y, et al. Deep learning for differential diagnosis of malignant hepatic tumors based on multi-phase contrast-enhanced CT and clinical data. J Hematol Oncol. 2021;14:154.","journal-title":"J Hematol Oncol"},{"key":"3152_CR35","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1007\/s00259-014-2961-x","volume":"42","author":"R Boellaard","year":"2015","unstructured":"Boellaard R, Delgado-Bolton R, Oyen WJ, Giammarile F, Tatsch K, Eschner W, et al. FDG PET\/CT: EANM procedure guidelines for tumour imaging: version 20. Eur J Nucl Med Mol Imaging. 2015;42:328\u201354.","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"3152_CR36","unstructured":"The Japanese Society of Nuclear Medicine. Standard protocol for whole-body PET imaging using 18F-FDG, 4th edition (in Japanese). 2023."},{"key":"3152_CR37","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1186\/s40537-022-00652-w","volume":"9","author":"A Hosna","year":"2022","unstructured":"Hosna A, Merry E, Gyalmo J, Alom Z, Aung Z, Azim MA. Transfer learning: a friendly introduction. J Big Data. 2022;9:102.","journal-title":"J Big Data"},{"key":"3152_CR38","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1111\/den.13897","volume":"33","author":"H Suzuki","year":"2021","unstructured":"Suzuki H, Yoshitaka T, Yoshio T, Tada T. Artificial intelligence for cancer detection of the upper gastrointestinal tract. Dig Endosc. 2021;33:254\u201362.","journal-title":"Dig Endosc"},{"key":"3152_CR39","doi-asserted-by":"publisher","first-page":"2955","DOI":"10.1007\/s10278-024-01114-w","volume":"37","author":"MM Al Hasan","year":"2024","unstructured":"Al Hasan MM, Ghazimoghadam S, Tunlayadechanont P, Mostafiz MT, Gupta M, Roy A, et al. Automated segmentation of lymph nodes on neck CT scans using deep learning. J Imaging Inform Med. 2024;37:2955\u201366.","journal-title":"J Imaging Inform Med"}],"container-title":["Surgery Today"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00595-025-03152-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00595-025-03152-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00595-025-03152-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T07:48:57Z","timestamp":1774338537000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00595-025-03152-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,18]]},"references-count":39,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["3152"],"URL":"https:\/\/doi.org\/10.1007\/s00595-025-03152-5","relation":{},"ISSN":["0941-1291","1436-2813"],"issn-type":[{"value":"0941-1291","type":"print"},{"value":"1436-2813","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,18]]},"assertion":[{"value":"12 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 October 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 conflicts of interest in association with the present study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}