{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T06:17:14Z","timestamp":1771049834884,"version":"3.50.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2025,2,15]],"date-time":"2025-02-15T00:00:00Z","timestamp":1739577600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,15]],"date-time":"2025-02-15T00:00:00Z","timestamp":1739577600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Zhejiang Provincial Natural Science Foundation of China under Grant","award":["LGF22H165705"],"award-info":[{"award-number":["LGF22H165705"]}]},{"DOI":"10.13039\/501100017532","name":"Medical Technology and Education of Zhejiang Province of China","doi-asserted-by":"publisher","award":["2022KY939"],"award-info":[{"award-number":["2022KY939"]}],"id":[{"id":"10.13039\/501100017532","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Project of Medical Scientific and Technology Program in Hangzhou","award":["A20200432"],"award-info":[{"award-number":["A20200432"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"DOI":"10.1007\/s11548-024-03290-0","type":"journal-article","created":{"date-parts":[[2025,2,15]],"date-time":"2025-02-15T18:57:07Z","timestamp":1739645827000},"page":"935-947","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Breaking barriers: noninvasive AI model for BRAFV600E mutation identification"],"prefix":"10.1007","volume":"20","author":[{"given":"Fan","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangfeng","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuying","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengqian","family":"Ge","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ting","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingjing","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linlin","family":"Mao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gang","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"You","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haitao","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dingcun","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,15]]},"reference":[{"issue":"7","key":"3290_CR1","doi-asserted-by":"publisher","first-page":"988","DOI":"10.1530\/EC-19-0246","volume":"8","author":"C Yan","year":"2019","unstructured":"Yan C, Huang M, Li X, Wang T, Ling R (2019) Relationship between BRAF V600E and clinical features in papillary thyroid carcinoma. Endocr Connect 8(7):988\u2013996","journal-title":"Endocr Connect"},{"issue":"11","key":"3290_CR2","doi-asserted-by":"publisher","first-page":"5560","DOI":"10.1177\/0300060519873481","volume":"47","author":"S Kure","year":"2019","unstructured":"Kure S, Ishino K, Kudo M, Wada R, Saito M, Nagaoka R et al (2019) Incidence of BRAF V600E mutation in patients with papillary thyroid carcinoma: a single-institution experience. J Int Med Res 47(11):5560\u20135572","journal-title":"J Int Med Res"},{"issue":"5","key":"3290_CR3","doi-asserted-by":"publisher","first-page":"495","DOI":"10.21037\/gs.2016.09.09","volume":"5","author":"A Czarniecka","year":"2016","unstructured":"Czarniecka A, Oczko-Wojciechowska M, Barczynski M (2016) BRAF V600E mutation in prognostication of papillary thyroid cancer (PTC) recurrence. Gland Surg 5(5):495\u2013505","journal-title":"Gland Surg"},{"issue":"5","key":"3290_CR4","doi-asserted-by":"publisher","first-page":"1878","DOI":"10.21037\/gs-20-430","volume":"9","author":"FA Rashid","year":"2020","unstructured":"Rashid FA, Munkhdelger J, Fukuoka J, Bychkov A (2020) Prevalence of BRAF (V600E) mutation in Asian series of papillary thyroid carcinoma-a contemporary systematic review. Gland Surg 9(5):1878\u20131900","journal-title":"Gland Surg"},{"issue":"1","key":"3290_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1089\/thy.2015.0020","volume":"26","author":"BR Haugen","year":"2016","unstructured":"Haugen BR, Alexander EK, Bible KC, Doherty GM, Mandel SJ, Nikiforov YE et al (2016) 2015 American Thyroid Association management guidelines for adult patients with thyroid nodules and differentiated thyroid cancer: the american thyroid association guidelines task force on thyroid nodules and differentiated thyroid cancer. Thyroid 26(1):1\u2013133","journal-title":"Thyroid"},{"key":"3290_CR6","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1016\/j.semcancer.2020.04.002","volume":"72","author":"A Conti","year":"2021","unstructured":"Conti A, Duggento A, Indovina I, Guerrisi M, Toschi N (2021) Radiomics in breast cancer classification and prediction. Semin Cancer Biol 72:238\u2013250","journal-title":"Semin Cancer Biol"},{"issue":"23","key":"3290_CR7","doi-asserted-by":"publisher","first-page":"2187","DOI":"10.1016\/j.jacc.2022.09.036","volume":"80","author":"QA Hathaway","year":"2022","unstructured":"Hathaway QA, Yanamala N, Siva NK, Adjeroh DA, Hollander JM, Sengupta PP (2022) Ultrasonic texture features for assessing cardiac remodeling and dysfunction. J Am Coll Cardiol 80(23):2187\u20132201","journal-title":"J Am Coll Cardiol"},{"issue":"10","key":"3290_CR8","doi-asserted-by":"publisher","first-page":"1617","DOI":"10.1007\/s11548-018-1796-5","volume":"13","author":"T Liu","year":"2018","unstructured":"Liu T, Ge X, Yu J, Guo Y, Wang Y, Wang W et al (2018) Comparison of the application of B-mode and strain elastography ultrasound in the estimation of lymph node metastasis of papillary thyroid carcinoma based on a radiomics approach. Int J Comput Assist Radiol Surg 13(10):1617\u20131627","journal-title":"Int J Comput Assist Radiol Surg"},{"issue":"4","key":"3290_CR9","doi-asserted-by":"publisher","first-page":"700","DOI":"10.3174\/ajnr.A6505","volume":"41","author":"MR Kwon","year":"2020","unstructured":"Kwon MR, Shin JH, Park H, Cho H, Hahn SY, Park KW (2020) Radiomics study of thyroid ultrasound for predicting BRAF mutation in papillary thyroid carcinoma: preliminary results. AJNR Am J Neuroradiol 41(4):700\u2013705","journal-title":"AJNR Am J Neuroradiol"},{"issue":"4","key":"3290_CR10","doi-asserted-by":"publisher","first-page":"e0153319","DOI":"10.1371\/journal.pone.0153319","volume":"11","author":"J Sun","year":"2016","unstructured":"Sun J, Zhang J, Lu J, Gao J, Ren X, Teng L et al (2016) BRAF V600E and TERT promoter mutations in papillary thyroid carcinoma in chinese patients. PLoS ONE 11(4):e0153319","journal-title":"PLoS ONE"},{"issue":"11","key":"3290_CR11","doi-asserted-by":"publisher","first-page":"e0242806","DOI":"10.1371\/journal.pone.0242806","volume":"15","author":"J Yoon","year":"2020","unstructured":"Yoon J, Lee E, Koo JS, Yoon JH, Nam KH, Lee J et al (2020) Artificial intelligence to predict the BRAFV600E mutation in patients with thyroid cancer. PLoS ONE 15(11):e0242806","journal-title":"PLoS ONE"},{"issue":"6","key":"3290_CR12","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1089\/thy.2011.0274","volume":"22","author":"AS Kabaker","year":"2012","unstructured":"Kabaker AS, Tublin ME, Nikiforov YE, Armstrong MJ, Hodak SP, Stang MT et al (2012) Suspicious ultrasound characteristics predict BRAF V600E-positive papillary thyroid carcinoma. Thyroid 22(6):585\u2013589","journal-title":"Thyroid"},{"issue":"2","key":"3290_CR13","doi-asserted-by":"publisher","first-page":"1439","DOI":"10.3892\/ol.2017.6276","volume":"14","author":"Q Li","year":"2017","unstructured":"Li Q, Yuan J, Wang Y, Zhai Y (2017) Association between the BRAF V600E mutation and ultrasound features of the thyroid in thyroid papillary carcinoma. Oncol Lett 14(2):1439\u20131444","journal-title":"Oncol Lett"},{"key":"3290_CR14","doi-asserted-by":"publisher","first-page":"902","DOI":"10.3389\/fendo.2019.00902","volume":"10","author":"JM Xu","year":"2019","unstructured":"Xu JM, Chen YJ, Dang YY, Chen M (2019) Association between preoperative US, Elastography features and prognostic factors of papillary thyroid cancer with BRAF (V600E) mutation. Front Endocrinol (Lausanne) 10:902","journal-title":"Front Endocrinol (Lausanne)"},{"issue":"2","key":"3290_CR15","doi-asserted-by":"publisher","first-page":"e0228968","DOI":"10.1371\/journal.pone.0228968","volume":"15","author":"JH Yoon","year":"2020","unstructured":"Yoon JH, Han K, Lee E, Lee J, Kim EK, Moon HJ et al (2020) Radiomics in predicting mutation status for thyroid cancer: a preliminary study using radiomics features for predicting BRAFV600E mutations in papillary thyroid carcinoma. PLoS ONE 15(2):e0228968","journal-title":"PLoS ONE"},{"key":"3290_CR16","doi-asserted-by":"publisher","first-page":"654685","DOI":"10.3389\/fonc.2021.654685","volume":"11","author":"G Zhang","year":"2021","unstructured":"Zhang G, Wu Z, Xu L, Zhang X, Zhang D, Mao L et al (2021) Deep learning on enhanced CT images can predict the muscular invasiveness of bladder cancer. Front Oncol 11:654685","journal-title":"Front Oncol"},{"key":"3290_CR17","doi-asserted-by":"publisher","first-page":"964074","DOI":"10.3389\/fendo.2023.964074","volume":"14","author":"L Chang","year":"2023","unstructured":"Chang L, Zhang Y, Zhu J, Hu L, Wang X, Zhang H et al (2023) An integrated nomogram combining deep learning, clinical characteristics and ultrasound features for predicting central lymph node metastasis in papillary thyroid cancer: a multicenter study. Front Endocrinol (Lausanne) 14:964074","journal-title":"Front Endocrinol (Lausanne)"},{"issue":"21","key":"3290_CR18","doi-asserted-by":"publisher","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","volume":"77","author":"JJM van Griethuysen","year":"2017","unstructured":"van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V et al (2017) Computational Radiomics system to decode the radiographic phenotype. Cancer Res 77(21):e104\u2013e107","journal-title":"Cancer Res"},{"key":"3290_CR19","doi-asserted-by":"crossref","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D, editors (2017) Grad-CAM: visual explanations from deep networks via gradient-based localization. In: 2017 IEEE international conference on computer vision (ICCV)","DOI":"10.1109\/ICCV.2017.74"},{"issue":"7","key":"3290_CR20","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1210\/er.2007-0007","volume":"28","author":"M Xing","year":"2007","unstructured":"Xing M (2007) BRAF mutation in papillary thyroid cancer: pathogenic role, molecular bases, and clinical implications. Endocr Rev 28(7):742\u2013762","journal-title":"Endocr Rev"},{"issue":"2","key":"3290_CR21","first-page":"171","volume":"13","author":"JY Park","year":"2016","unstructured":"Park JY, Yi JW, Park CH, Lim Y, Lee KH, Lee KE et al (2016) Role of BRAF and RAS mutations in extrathyroidal extension in papillary thyroid cancer. Cancer Genomics Proteom 13(2):171\u2013181","journal-title":"Cancer Genomics Proteom"},{"issue":"6","key":"3290_CR22","doi-asserted-by":"publisher","first-page":"977","DOI":"10.1016\/j.surg.2012.08.019","volume":"152","author":"KC Lee","year":"2012","unstructured":"Lee KC, Li C, Schneider EB, Wang Y, Somervell H, Krafft M et al (2012) Is BRAF mutation associated with lymph node metastasis in patients with papillary thyroid cancer? Surgery 152(6):977\u2013983","journal-title":"Surgery"},{"issue":"12","key":"3290_CR23","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1634\/theoncologist.2010-0156","volume":"15","author":"AL Melck","year":"2010","unstructured":"Melck AL, Yip L, Carty SE (2010) The utility of BRAF testing in the management of papillary thyroid cancer. Oncologist 15(12):1285\u20131293","journal-title":"Oncologist"},{"issue":"6","key":"3290_CR24","doi-asserted-by":"publisher","first-page":"1139","DOI":"10.1016\/j.surg.2010.09.005","volume":"148","author":"CJ O'Neill","year":"2010","unstructured":"O\u2019Neill CJ, Bullock M, Chou A, Sidhu SB, Delbridge LW, Robinson BG et al (2010) BRAF(V600E) mutation is associated with an increased risk of nodal recurrence requiring reoperative surgery in patients with papillary thyroid cancer. Surgery 148(6):1139\u20131145 (discussion 45-6)","journal-title":"Surgery"},{"issue":"6","key":"3290_CR25","doi-asserted-by":"publisher","first-page":"1215","DOI":"10.1016\/j.surg.2009.09.011","volume":"146","author":"L Yip","year":"2009","unstructured":"Yip L, Nikiforova MN, Carty SE, Yim JH, Stang MT, Tublin MJ et al (2009) Optimizing surgical treatment of papillary thyroid carcinoma associated with BRAF mutation. Surgery 146(6):1215\u20131223","journal-title":"Surgery"},{"issue":"3","key":"3290_CR26","first-page":"2995","volume":"18","author":"Y Liu","year":"2019","unstructured":"Liu Y, He L, Yin G, Cheng L, Zeng B, Cheng J et al (2019) Association analysis and the clinical significance of BRAF gene mutations and ultrasound features in papillary thyroid carcinoma. Oncol Lett 18(3):2995\u20133002","journal-title":"Oncol Lett"},{"key":"3290_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2147\/JMDH.S393993","volume":"16","author":"T Zheng","year":"2023","unstructured":"Zheng T, Hu W, Wang H, Xie X, Tang L, Liu W et al (2023) MRI-based texture analysis for preoperative prediction of BRAF V600E mutation in papillary thyroid carcinoma. J Multidiscip Healthc 16:1\u201310","journal-title":"J Multidiscip Healthc"},{"issue":"2","key":"3290_CR28","doi-asserted-by":"publisher","first-page":"e230255","DOI":"10.1148\/radiol.230255","volume":"308","author":"M Li","year":"2023","unstructured":"Li M, Fan Y, You H, Li C, Luo M, Zhou J et al (2023) Dual-energy CT deep learning radiomics to predict macrotrabecular-massive hepatocellular carcinoma. Radiology 308(2):e230255","journal-title":"Radiology"},{"issue":"5","key":"3290_CR29","doi-asserted-by":"publisher","first-page":"e221843","DOI":"10.1148\/radiol.221843","volume":"307","author":"MPL Beuque","year":"2023","unstructured":"Beuque MPL, Lobbes MBI, van Wijk Y, Widaatalla Y, Primakov S, Majer M et al (2023) Combining deep learning and handcrafted radiomics for classification of suspicious lesions on contrast-enhanced mammograms. Radiology 307(5):e221843","journal-title":"Radiology"},{"key":"3290_CR30","doi-asserted-by":"publisher","first-page":"101852","DOI":"10.1016\/j.compmedimag.2020.101852","volume":"88","author":"A Caroppo","year":"2021","unstructured":"Caroppo A, Leone A, Siciliano P (2021) Deep transfer learning approaches for bleeding detection in endoscopy images. Comput Med Imag Graph 88:101852","journal-title":"Comput Med Imag Graph"},{"key":"3290_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neunet.2020.12.013","volume":"136","author":"K Zhang","year":"2021","unstructured":"Zhang K, Robinson N, Lee SW, Guan C (2021) Adaptive transfer learning for EEG motor imagery classification with deep convolutional neural network. Neural Netw 136:1\u201310","journal-title":"Neural Netw"},{"key":"3290_CR32","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.canlet.2021.12.015","volume":"527","author":"Q Li","year":"2022","unstructured":"Li Q, Jiang T, Zhang C, Zhang Y, Huang Z, Zhou H et al (2022) A nomogram based on clinical information, conventional ultrasound and radiomics improves prediction of malignant parotid gland lesions. Cancer Lett 527:107\u2013114","journal-title":"Cancer Lett"},{"issue":"1","key":"3290_CR33","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1186\/s40644-024-00676-w","volume":"24","author":"T Deng","year":"2024","unstructured":"Deng T, Liang J, Yan C, Ni M, Xiang H, Li C et al (2024) Development and validation of ultrasound-based radiomics model to predict germline BRCA mutations in patients with breast cancer. Cancer Imag: Off Publ Int Cancer Imag Soc 24(1):31","journal-title":"Cancer Imag: Off Publ Int Cancer Imag Soc"},{"issue":"2","key":"3290_CR34","doi-asserted-by":"publisher","first-page":"1902","DOI":"10.1093\/bib\/bbaa043","volume":"22","author":"S Jin","year":"2021","unstructured":"Jin S, Zeng X, Xia F, Huang W, Liu X (2021) Application of deep learning methods in biological networks. Brief Bioinform 22(2):1902\u20131917","journal-title":"Brief Bioinform"},{"key":"3290_CR35","first-page":"4761","volume":"2017","author":"Z Zhou","year":"2017","unstructured":"Zhou Z, Shin J, Zhang L, Gurudu S, Gotway M, Liang J (2017) Fine-tuning convolutional neural networks for biomedical image analysis: actively and incrementally. Proc IEEE Comput Soc Conf Comput Vis Patt Recognit 2017:4761\u20134772","journal-title":"Proc IEEE Comput Soc Conf Comput Vis Patt Recognit"},{"issue":"4","key":"3290_CR36","doi-asserted-by":"publisher","first-page":"1187","DOI":"10.1007\/s00259-021-05573-z","volume":"49","author":"C An","year":"2022","unstructured":"An C, Li D, Li S, Li W, Tong T, Liu L et al (2022) Deep learning radiomics of dual-energy computed tomography for predicting lymph node metastases of pancreatic ductal adenocarcinoma. Eur J Nucl Med Mol Imag 49(4):1187\u20131199","journal-title":"Eur J Nucl Med Mol Imag"},{"issue":"10","key":"3290_CR37","doi-asserted-by":"publisher","first-page":"3615","DOI":"10.1080\/07391102.2020.1767212","volume":"39","author":"K El Asnaoui","year":"2021","unstructured":"El Asnaoui K, Chawki Y (2021) Using X-ray images and deep learning for automated detection of coronavirus disease. J Biomol Struct Dyn 39(10):3615\u20133626","journal-title":"J Biomol Struct Dyn"},{"issue":"1","key":"3290_CR38","doi-asserted-by":"publisher","first-page":"16143","DOI":"10.1038\/s41598-021-95748-3","volume":"11","author":"YR Park","year":"2021","unstructured":"Park YR, Kim YJ, Ju W, Nam K, Kim S, Kim KG (2021) Comparison of machine and deep learning for the classification of cervical cancer based on cervicography images. Sci Rep 11(1):16143","journal-title":"Sci Rep"},{"key":"3290_CR39","doi-asserted-by":"publisher","first-page":"102078","DOI":"10.1016\/j.artmed.2021.102078","volume":"116","author":"D Karimi","year":"2021","unstructured":"Karimi D, Warfield SK, Gholipour A (2021) Transfer learning in medical image segmentation: new insights from analysis of the dynamics of model parameters and learned representations. Artif Intell Med 116:102078","journal-title":"Artif Intell Med"},{"issue":"1","key":"3290_CR40","doi-asserted-by":"publisher","first-page":"e190034","DOI":"10.1148\/ryai.2019190034","volume":"2","author":"Y Zhu","year":"2020","unstructured":"Zhu Y, Fahmy AS, Duan C, Nakamori S, Nezafat R (2020) Automated myocardial T2 and extracellular volume quantification in cardiac mri using transfer learning-based myocardium segmentation. Radiol Artif Intell 2(1):e190034","journal-title":"Radiol Artif Intell"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-024-03290-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-024-03290-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-024-03290-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T12:51:37Z","timestamp":1746535897000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-024-03290-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,15]]},"references-count":40,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["3290"],"URL":"https:\/\/doi.org\/10.1007\/s11548-024-03290-0","relation":{},"ISSN":["1861-6429"],"issn-type":[{"value":"1861-6429","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,15]]},"assertion":[{"value":"22 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 February 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 that there are no conflicts of interest that could be perceived as prejudicing the impartiality of the research reported.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This study was approved by Ethics Committee of Hangzhou First People\u2019s Hospital, and written informed consent was obtained from all patients.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}