{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T14:30:17Z","timestamp":1778509817247,"version":"3.51.4"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,12,17]],"date-time":"2022-12-17T00:00:00Z","timestamp":1671235200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,12,17]],"date-time":"2022-12-17T00:00:00Z","timestamp":1671235200000},"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":["BMC Med Imaging"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>It is difficult to predict normal-sized lymph node metastasis (LNM) in cervical cancer clinically. We aimed to investigate the feasibility of using deep learning (DL) nomogram based on readout segmentation of long variable echo-trains diffusion weighted imaging (RESOLVE-DWI) and related patient information to preoperatively predict normal-sized LNM in patients with cervical cancer.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>A dataset of MR images [RESOLVE-DWI and apparent diffusion coefficient (ADC)] and patient information (age, tumor size, International Federation of Gynecology and Obstetrics stage, ADC value and squamous cell carcinoma antigen level) of 169 patients with cervical cancer between November 2013 and January 2022 were retrospectively collected. The LNM status was determined by final histopathology. The collected studies were randomly divided into a development cohort (n\u2009=\u2009126) and a test cohort (n\u2009=\u200943). A single-channel convolutional neural network (CNN) and a multi-channel CNN based on ResNeSt architectures were proposed for predicting normal-sized LNM from single or multi modalities of MR images, respectively. A DL nomogram was constructed by incorporating the clinical information and the multi-channel CNN. These models\u2019 performance was analyzed by the receiver operating characteristic analysis in the test cohort.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Compared to the single-channel CNN model using RESOLVE-DWI and ADC respectively, the multi-channel CNN model that integrating both two MR modalities showed improved performance in development cohort [AUC 0.848; 95% confidence interval (CI) 0.774\u20130.906] and test cohort (AUC 0.767; 95% CI 0.613\u20130.882). The DL nomogram showed the best performance in development cohort (AUC 0.890; 95% CI 0.821\u20130.938) and test cohort (AUC 0.844; 95% CI 0.701\u20130.936).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>The DL nomogram incorporating RESOLVE-DWI and clinical information has the potential to preoperatively predict normal-sized LNM of cervical cancer.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12880-022-00948-6","type":"journal-article","created":{"date-parts":[[2022,12,17]],"date-time":"2022-12-17T16:02:57Z","timestamp":1671292977000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["RESOLVE-DWI-based deep learning nomogram for prediction of normal-sized lymph node metastasis in cervical cancer: a preliminary study"],"prefix":"10.1186","volume":"22","author":[{"given":"Weiliang","family":"Qian","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhisen","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weidao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongkun","family":"Yin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jibin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianming","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunhong","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,17]]},"reference":[{"key":"948_CR1","doi-asserted-by":"publisher","first-page":"7","DOI":"10.3322\/caac.21708","volume":"72","author":"RL Siegel","year":"2022","unstructured":"Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA Cancer J Clin. 2022;72:7\u201333.","journal-title":"CA Cancer J Clin"},{"key":"948_CR2","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1159\/000485840","volume":"41","author":"K Nanthamongkolkul","year":"2018","unstructured":"Nanthamongkolkul K, Hanprasertpong J. Predictive factors of pelvic lymph node metastasis in early-stage cervical cancer. Oncol Res Treat. 2018;41:194\u20138.","journal-title":"Oncol Res Treat"},{"key":"948_CR3","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.ejso.2012.10.011","volume":"39","author":"A Achouri","year":"2013","unstructured":"Achouri A, Huchon C, Bats AS, Bensaid C, Nos C, L\u00e9curu F. Complications of lymphadenectomy for gynecologic cancer. Eur J Surg Oncol. 2013;39:81\u20136.","journal-title":"Eur J Surg Oncol"},{"key":"948_CR4","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1002\/ijgo.12749","volume":"145","author":"N Bhatla","year":"2019","unstructured":"Bhatla N, Berek JS, Cuello Fredes M, Denny LA, Grenman S, Karunaratne K, et al. Revised FIGO staging for carcinoma of the cervix uteri. Int J Gynaecol Obstet. 2019;145:129\u201335.","journal-title":"Int J Gynaecol Obstet"},{"key":"948_CR5","doi-asserted-by":"publisher","first-page":"1471","DOI":"10.1111\/j.1349-7006.2010.01532.x","volume":"101","author":"HJ Choi","year":"2010","unstructured":"Choi HJ, Ju W, Myung SK, Kim Y. Diagnostic performance of computer tomography, magnetic resonance imaging, and positron emission tomography or positron emission tomography\/computer tomography for detection of metastatic lymph nodes in patients with cervical cancer: meta-analysis. Cancer Sci. 2010;101:1471\u20139.","journal-title":"Cancer Sci"},{"key":"948_CR6","doi-asserted-by":"publisher","first-page":"7802","DOI":"10.1007\/s00330-020-07632-9","volume":"31","author":"L Manganaro","year":"2021","unstructured":"Manganaro L, Lakhman Y, Bharwani N, Gui B, Gigli S, Vinci V, et al. Staging, recurrence and follow-up of uterine cervical cancer using MRI: updated guidelines of the European Society of Urogenital Radiology after revised FIGO staging 2018. Eur Radiol. 2021;31:7802\u201316.","journal-title":"Eur Radiol"},{"key":"948_CR7","doi-asserted-by":"publisher","first-page":"1880","DOI":"10.1111\/j.1525-1438.2006.00715.x","volume":"16","author":"S Tangjitgamol","year":"2006","unstructured":"Tangjitgamol S, Manusirivithaya S, Jesadapatarakul S, Leelahakorn S, Thawaramara T. Lymph node size in uterine cancer: a revisit. Int J Gynecol Cancer. 2006;16:1880\u20134.","journal-title":"Int J Gynecol Cancer"},{"key":"948_CR8","doi-asserted-by":"publisher","first-page":"4633","DOI":"10.1002\/cncr.31630","volume":"124","author":"S Napel","year":"2018","unstructured":"Napel S, Mu W, Jardim-Perassi BV, Aerts H, Gillies RJ. Quantitative imaging of cancer in the postgenomic era: radio(geno)mics, deep learning, and habitats. Cancer. 2018;124:4633\u201349.","journal-title":"Cancer"},{"key":"948_CR9","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1038\/s41568-018-0016-5","volume":"18","author":"A Hosny","year":"2018","unstructured":"Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts H. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18:500\u201310.","journal-title":"Nat Rev Cancer"},{"key":"948_CR10","doi-asserted-by":"publisher","first-page":"464","DOI":"10.3389\/fonc.2020.00464","volume":"10","author":"T Dong","year":"2020","unstructured":"Dong T, Yang C, Cui B, Zhang T, Sun X, Song K, et al. Development and validation of a deep learning radiomics model predicting lymph node status in operable cervical cancer. Front Oncol. 2020;10:464.","journal-title":"Front Oncol"},{"key":"948_CR11","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1148\/radiol.2019190372","volume":"294","author":"LQ Zhou","year":"2020","unstructured":"Zhou LQ, Wu XL, Huang SY, Wu GG, Ye HR, Wei Q, et al. Lymph node metastasis prediction from primary breast cancer US images using deep learning. Radiology. 2020;294:19\u201328.","journal-title":"Radiology"},{"key":"948_CR12","doi-asserted-by":"publisher","first-page":"2477","DOI":"10.21037\/qims-20-525","volume":"11","author":"J Li","year":"2021","unstructured":"Li J, Zhou Y, Wang P, Zhao H, Wang X, Tang N, et al. Deep transfer learning based on magnetic resonance imaging can improve the diagnosis of lymph node metastasis in patients with rectal cancer. Quant Imaging Med Surg. 2021;11:2477\u201385.","journal-title":"Quant Imaging Med Surg"},{"key":"948_CR13","doi-asserted-by":"publisher","first-page":"101113","DOI":"10.1016\/j.tranon.2021.101113","volume":"14","author":"Y Liu","year":"2021","unstructured":"Liu Y, Fan H, Dong D, Liu P, He B, Meng L, et al. Computed tomography-based radiomic model at node level for the prediction of normal-sized lymph node metastasis in cervical cancer. Transl Oncol. 2021;14:101113.","journal-title":"Transl Oncol"},{"key":"948_CR14","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1148\/radiol.14132921","volume":"273","author":"HC Thoeny","year":"2014","unstructured":"Thoeny HC, Froehlich JM, Triantafyllou M, Huesler J, Bains LJ, Vermathen P, et al. Metastases in normal-sized pelvic lymph nodes: detection with diffusion-weighted MR imaging. Radiology. 2014;273:125\u201335.","journal-title":"Radiology"},{"key":"948_CR15","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.ejrad.2019.01.003","volume":"114","author":"T Wang","year":"2019","unstructured":"Wang T, Gao T, Yang J, Yan X, Wang Y, Zhou X, et al. Preoperative prediction of pelvic lymph nodes metastasis in early-stage cervical cancer using radiomics nomogram developed based on T2-weighted MRI and diffusion-weighted imaging. Eur J Radiol. 2019;114:128\u201335.","journal-title":"Eur J Radiol"},{"key":"948_CR16","doi-asserted-by":"publisher","first-page":"885","DOI":"10.1002\/jmri.27101","volume":"52","author":"M Xiao","year":"2020","unstructured":"Xiao M, Ma F, Li Y, Li Y, Li M, Zhang G, et al. Multiparametric MRI-based radiomics nomogram for predicting lymph node metastasis in early-stage cervical cancer. J Magn Reson Imaging. 2020;52:885\u201396.","journal-title":"J Magn Reson Imaging"},{"key":"948_CR17","doi-asserted-by":"publisher","first-page":"20180293","DOI":"10.1259\/bjr.20180293","volume":"92","author":"W Qian","year":"2019","unstructured":"Qian W, Chen Q, Zhang Z, Wang H, Zhang J, Xu J. Comparison between readout-segmented and single-shot echo-planar imaging in the evaluation of cervical cancer staging. Br J Radiol. 2019;92:20180293.","journal-title":"Br J Radiol"},{"key":"948_CR18","doi-asserted-by":"publisher","first-page":"502","DOI":"10.1097\/RCT.0000000000000724","volume":"42","author":"P Lu","year":"2018","unstructured":"Lu P, Tian G, Liu X, Wang F, Zhang Z, Sha Y. Differentiating neuromyelitis optica-related and multiple sclerosis-related acute optic neuritis using conventional magnetic resonance imaging combined with readout-segmented echo-planar diffusion-weighted imaging. J Comput Assist Tomogr. 2018;42:502\u20139.","journal-title":"J Comput Assist Tomogr"},{"key":"948_CR19","doi-asserted-by":"crossref","unstructured":"Zhang H, Wu C, Zhang Z, Zhu Y, Lin H, Zhang Z, et al. ResNeSt: split-attention networks. In:\u00a02022 IEEE\/CVF conference on computer vision and pattern recognition workshops (CVPRW). 2022. p.\u00a02735\u20132745.","DOI":"10.1109\/CVPRW56347.2022.00309"},{"key":"948_CR20","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","volume":"26","author":"K Clark","year":"2013","unstructured":"Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, et al. The cancer imaging archive (TCIA): maintaining and operating a public information repository. J Digit Imaging. 2013;26:1045\u201357.","journal-title":"J Digit Imaging"},{"key":"948_CR21","doi-asserted-by":"publisher","first-page":"5902","DOI":"10.1007\/s00330-020-07659-y","volume":"31","author":"H Liu","year":"2021","unstructured":"Liu H, Chen Y, Zhang Y, Wang L, Luo R, Wu H, et al. A deep learning model integrating mammography and clinical factors facilitates the malignancy prediction of BI-RADS 4 microcalcifications in breast cancer screening. Eur Radiol. 2021;31:5902\u201312.","journal-title":"Eur Radiol"},{"key":"948_CR22","doi-asserted-by":"crossref","unstructured":"Zhou B, Khosla A, Lapedriza \u00c0, Oliva A, Torralba A. Learning deep features for discriminative localization. In:\u00a02016 IEEE conference on computer vision and recognition P. (CVPR). 2016. pp.\u00a02921\u20132929.","DOI":"10.1109\/CVPR.2016.319"},{"key":"948_CR23","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1159\/000456006","volume":"82","author":"B Liu","year":"2017","unstructured":"Liu B, Gao S, Li S. A comprehensive comparison of CT, MRI, positron emission tomography or positron emission tomography\/CT, and diffusion weighted imaging-MRI for detecting the lymph nodes metastases in patients with cervical cancer: a meta-analysis based on 67 studies. Gynecol Obstet Invest. 2017;82:209\u201322.","journal-title":"Gynecol Obstet Invest"},{"key":"948_CR24","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.radonc.2019.04.035","volume":"138","author":"Q Wu","year":"2019","unstructured":"Wu Q, Wang S, Chen X, Wang Y, Dong L, Liu Z, et al. Radiomics analysis of magnetic resonance imaging improves diagnostic performance of lymph node metastasis in patients with cervical cancer. Radiother Oncol. 2019;138:141\u20138.","journal-title":"Radiother Oncol"},{"key":"948_CR25","doi-asserted-by":"publisher","first-page":"e2011625","DOI":"10.1001\/jamanetworkopen.2020.11625","volume":"3","author":"Q Wu","year":"2020","unstructured":"Wu Q, Wang S, Zhang S, Wang M, Ding Y, Fang J, et al. Development of a deep learning model to identify lymph node metastasis on magnetic resonance imaging in patients with cervical cancer. JAMA Netw Open. 2020;3:e2011625.","journal-title":"JAMA Netw Open"},{"key":"948_CR26","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1186\/s13550-017-0260-9","volume":"7","author":"H Wang","year":"2017","unstructured":"Wang H, Zhou Z, Li Y, Chen Z, Lu P, Wang W, et al. Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from (18)F-FDG PET\/CT images. EJNMMI Res. 2017;7:11.","journal-title":"EJNMMI Res"},{"key":"948_CR27","doi-asserted-by":"publisher","first-page":"3815","DOI":"10.1007\/s00261-021-03021-y","volume":"46","author":"L Zhao","year":"2021","unstructured":"Zhao L, Liang M, Wang S, Yang Y, Zhang H, Zhao X. Preoperative evaluation of extramural venous invasion in rectal cancer using radiomics analysis of relaxation maps from synthetic MRI. Abdom Radiol (NY). 2021;46:3815\u201325.","journal-title":"Abdom Radiol (NY)"},{"key":"948_CR28","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1177\/0284185118780903","volume":"60","author":"J Song","year":"2019","unstructured":"Song J, Hu Q, Huang J, Ma Z, Chen T. Combining tumor size and diffusion-weighted imaging to diagnose normal-sized metastatic pelvic lymph nodes in cervical cancers. Acta Radiol. 2019;60:388\u201395.","journal-title":"Acta Radiol"},{"key":"948_CR29","doi-asserted-by":"publisher","first-page":"20200203","DOI":"10.1259\/bjr.20200203","volume":"95","author":"Q Song","year":"2022","unstructured":"Song Q, Yu Y, Zhang X, Zhu Y, Luo Y, Yu T, et al. Value of MRI and diffusion weighted imaging in diagnosing normal-sized pelvic lymph nodes metastases in patients with cervical cancer. Br J Radiol. 2022;95:20200203.","journal-title":"Br J Radiol"},{"key":"948_CR30","doi-asserted-by":"publisher","first-page":"848","DOI":"10.1177\/0284185119879686","volume":"61","author":"A Zhang","year":"2020","unstructured":"Zhang A, Song J, Ma Z, Chen T. Application of apparent diffusion coefficient values derived from diffusion-weighted imaging for assessing different sized metastatic lymph nodes in cervical cancers. Acta Radiol. 2020;61:848\u201355.","journal-title":"Acta Radiol"},{"key":"948_CR31","doi-asserted-by":"publisher","first-page":"6938","DOI":"10.1007\/s00330-021-07735-x","volume":"31","author":"J Song","year":"2021","unstructured":"Song J, Hu Q, Ma Z, Zhao M, Chen T, Shi H. Feasibility of T(2)WI-MRI-based radiomics nomogram for predicting normal-sized pelvic lymph node metastasis in cervical cancer patients. Eur Radiol. 2021;31:6938\u201348.","journal-title":"Eur Radiol"},{"key":"948_CR32","doi-asserted-by":"publisher","first-page":"1129","DOI":"10.1007\/s00261-020-02762-6","volume":"46","author":"Z Bai","year":"2021","unstructured":"Bai Z, Shi J, Yang Z, Zeng W, Hu H, Zhong J, et al. Quantitative kinetic parameters of primary tumor can be used to predict pelvic lymph node metastasis in early-stage cervical cancer. Abdom Radiol (NY). 2021;46:1129\u201336.","journal-title":"Abdom Radiol (NY)"},{"key":"948_CR33","doi-asserted-by":"publisher","first-page":"542","DOI":"10.1002\/bjs.11928","volume":"108","author":"C Jin","year":"2021","unstructured":"Jin C, Jiang Y, Yu H, Wang W, Li B, Chen C, et al. Deep learning analysis of the primary tumour and the prediction of lymph node metastases in gastric cancer. Br J Surg. 2021;108:542\u20139.","journal-title":"Br J Surg"},{"key":"948_CR34","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1007\/s13244-017-0567-0","volume":"8","author":"E Dappa","year":"2017","unstructured":"Dappa E, Elger T, Hasenburg A, D\u00fcber C, Battista MJ, H\u00f6tker AM. The value of advanced MRI techniques in the assessment of cervical cancer: a review. Insights Imaging. 2017;8:471\u201381.","journal-title":"Insights Imaging"},{"key":"948_CR35","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1102\/1470-7330.2009.0017","volume":"9","author":"S Ganeshalingam","year":"2009","unstructured":"Ganeshalingam S, Koh DM. Nodal staging. Cancer Imaging. 2009;9:104\u201311.","journal-title":"Cancer Imaging"},{"key":"948_CR36","doi-asserted-by":"publisher","first-page":"20190105","DOI":"10.1259\/bjr.20190105","volume":"92","author":"U Schick","year":"2019","unstructured":"Schick U, Lucia F, Dissaux G, Visvikis D, Badic B, Masson I, et al. MRI-derived radiomics: methodology and clinical applications in the field of pelvic oncology. Br J Radiol. 2019;92:20190105.","journal-title":"Br J Radiol"},{"key":"948_CR37","doi-asserted-by":"publisher","first-page":"1251","DOI":"10.1002\/jmri.27900","volume":"55","author":"Q Xu","year":"2022","unstructured":"Xu Q, Zhu Q, Liu H, Chang L, Duan S, Dou W, et al. Differentiating benign from malignant renal tumors using T2- and diffusion-weighted images: a comparison of deep learning and radiomics models versus assessment from radiologists. J Magn Reson Imaging. 2022;55:1251\u20139.","journal-title":"J Magn Reson Imaging"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-022-00948-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-022-00948-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-022-00948-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,17]],"date-time":"2022-12-17T16:03:12Z","timestamp":1671292992000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-022-00948-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,17]]},"references-count":37,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["948"],"URL":"https:\/\/doi.org\/10.1186\/s12880-022-00948-6","relation":{},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,17]]},"assertion":[{"value":"11 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 December 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 December 2022","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 retrospective study was approved by the Ethics Committee of the Affiliated Suzhou Hospital of Nanjing Medical University (Approval No.: KL901275). The need for Informed Consent was waived by the Ethics Committee of the Affiliated Suzhou Hospital of Nanjing Medical University due to the retrospective nature of the study. We confirmed that all methods were performed in accordance with the relevant guidelines and regulations (For example: Declarations of Helsinki).","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 that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"221"}}