{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T18:42:23Z","timestamp":1787164943351,"version":"build-2736575974"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,5,24]],"date-time":"2024-05-24T00:00:00Z","timestamp":1716508800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,5,24]],"date-time":"2024-05-24T00:00:00Z","timestamp":1716508800000},"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":["Int J Colorectal Dis"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study aimed to assess tumor regression grade (TRG) in patients with rectal cancer after neoadjuvant chemoradiotherapy (NCRT) through a machine learning\u2013based radiomics analysis using baseline T2-weighted magnetic resonance (MR) images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>In total, 148 patients with locally advanced rectal cancer(T2-4 or N+) who underwent MR imaging at baseline and after chemoradiotherapy between January 2010 and May 2021 were included. A region of interest for each tumor mass was drawn by a radiologist on oblique axial T2-weighted images, and main features were selected using principal component analysis after dimension reduction among 116 radiomics and three clinical features. Among eight learning models that were used for prediction model development, the model showing best performance was selected. Treatment responses were classified as either good or poor based on the MR-assessed TRG (mrTRG) and pathologic TRG (pTRG). The model performance was assessed using the area under the receiver operating curve (AUROC) to classify the response group.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Approximately 49% of the patients were in the good response (GR) group based on mrTRG (73\/148) and 26.9% based on pTRG (28\/104). The AUCs of clinical data, radiomics models, and combined radiomics with clinical data model for predicting mrTRG were 0.80 (95% confidence interval [CI] 0.73, 0.87), 0.74 (95% CI 0.66, 0.81), and 0.75(95% CI 0.68, 0.82), and those for predicting pTRG was 0.62 (95% CI 0.52, 0.71), 0.74 (95% CI 0.65, 0.82), and 0.79 (95% CI 0.71, 0.87).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Radiomics combined with clinical data model using baseline T2-weighted MR images demonstrated feasible diagnostic performance in predicting both MR-assessed and pathologic treatment response in patients with rectal cancer after NCRT.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00384-024-04651-6","type":"journal-article","created":{"date-parts":[[2024,5,24]],"date-time":"2024-05-24T20:02:21Z","timestamp":1716580941000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Machine learning\u2013based response assessment in patients with rectal cancer after neoadjuvant chemoradiotherapy: radiomics analysis for assessing tumor regression grade using T2-weighted magnetic resonance images"],"prefix":"10.1007","volume":"39","author":[{"given":"Yong Dae","family":"Lee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyug-Gi","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miri","family":"Seo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sung Kyoung","family":"Moon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seong Jin","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Myung-Won","family":"You","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,5,24]]},"reference":[{"issue":"suppl_4","key":"4651_CR1","doi-asserted-by":"publisher","first-page":"iv22","DOI":"10.1093\/annonc\/mdx224","volume":"28","author":"R Glynne-Jones","year":"2017","unstructured":"Glynne-Jones R, Wyrwicz L, Tiret E, Brown G, Rodel C, Cervantes A, Arnold D, Committee EG (2017) Rectal cancer: ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann Oncol 28(suppl_4):iv22\u2013iv40","journal-title":"Ann Oncol"},{"issue":"9","key":"4651_CR2","doi-asserted-by":"publisher","first-page":"835","DOI":"10.1016\/S1470-2045(10)70172-8","volume":"11","author":"M Maas","year":"2010","unstructured":"Maas M, Nelemans PJ, Valentini V, Das P, Rodel C, Kuo LJ, Calvo FA, Garcia-Aguilar J, Glynne-Jones R, Haustermans K, Mohiuddin M, Pucciarelli S, Small W Jr, Suarez J, Theodoropoulos G, Biondo S, Beets-Tan RG, Beets GL (2010) Long-term outcome in patients with a pathological complete response after chemoradiation for rectal cancer: a pooled analysis of individual patient data. Lancet Oncol 11(9):835\u2013844","journal-title":"Lancet Oncol"},{"issue":"15","key":"4651_CR3","doi-asserted-by":"publisher","first-page":"1554","DOI":"10.1200\/JCO.2013.54.3769","volume":"32","author":"E Fokas","year":"2014","unstructured":"Fokas E, Liersch T, Fietkau R, Hohenberger W, Beissbarth T, Hess C, Becker H, Ghadimi M, Mrak K, Merkel S, Raab HR, Sauer R, Wittekind C, Rodel C (2014) Tumor regression grading after preoperative chemoradiotherapy for locally advanced rectal carcinoma revisited: updated results of the CAO\/ARO\/AIO-94 trial. J Clin Oncol 32(15):1554\u20131562","journal-title":"J Clin Oncol"},{"issue":"7","key":"4651_CR4","doi-asserted-by":"publisher","first-page":"1003","DOI":"10.3348\/kjr.2018.0611","volume":"20","author":"N Seo","year":"2019","unstructured":"Seo N, Kim H, Cho MS, Lim JS (2019) Response assessment with MRI after chemoradiotherapy in rectal cancer: current evidences. Korean J Radiol 20(7):1003\u20131018","journal-title":"Korean J Radiol"},{"issue":"9","key":"4651_CR5","doi-asserted-by":"publisher","first-page":"2842","DOI":"10.1245\/s10434-012-2309-3","volume":"19","author":"UB Patel","year":"2012","unstructured":"Patel UB, Brown G, Rutten H, West N, Sebag-Montefiore D, Glynne-Jones R, Rullier E, Peeters M, Van Cutsem E, Ricci S, Van de Velde C, Kjell P, Quirke P (2012) Comparison of magnetic resonance imaging and histopathological response to chemoradiotherapy in locally advanced rectal cancer. Ann Surg Oncol 19(9):2842\u20132852","journal-title":"Ann Surg Oncol"},{"issue":"28","key":"4651_CR6","doi-asserted-by":"publisher","first-page":"3753","DOI":"10.1200\/JCO.2011.34.9068","volume":"29","author":"UB Patel","year":"2011","unstructured":"Patel UB, Taylor F, Blomqvist L, George C, Evans H, Tekkis P, Quirke P, Sebag-Montefiore D, Moran B, Heald R, Guthrie A, Bees N, Swift I, Pennert K, Brown G (2011) Magnetic resonance imaging-detected tumor response for locally advanced rectal cancer predicts survival outcomes: MERCURY experience. J Clin Oncol 29(28):3753\u20133760","journal-title":"J Clin Oncol"},{"issue":"10139","key":"4651_CR7","doi-asserted-by":"publisher","first-page":"2537","DOI":"10.1016\/S0140-6736(18)31078-X","volume":"391","author":"MJM van der Valk","year":"2018","unstructured":"van der Valk MJM, Hilling DE, Bastiaannet E, Kranenbarg EM, Beets GL, Figueiredo NL, Habr-Gama A, Perez RO, Renehan AG, van de Velde CJH, Consortium I (2018) Long-term outcomes of clinical complete responders after neoadjuvant treatment for rectal cancer in the International Watch & Wait Database (IWWD): an international multicentre registry study. Lancet 391(10139):2537\u20132545","journal-title":"Lancet"},{"issue":"6","key":"4651_CR8","doi-asserted-by":"publisher","first-page":"1717","DOI":"10.1148\/rg.2021210037","volume":"41","author":"JD Shur","year":"2021","unstructured":"Shur JD, Doran SJ, Kumar S, Ap Dafydd D, Downey K, O\u2019Connor JPB, Papanikolaou N, Messiou C, Koh DM, Orton MR (2021) Radiomics in oncology: a practical guide. Radiographics 41(6):1717\u20131732","journal-title":"Radiographics"},{"issue":"8","key":"4651_CR9","doi-asserted-by":"publisher","first-page":"4201","DOI":"10.1007\/s00330-020-06835-4","volume":"30","author":"H Park","year":"2020","unstructured":"Park H, Kim KA, Jung J-H, Rhie J, Choi SY (2020) MRI features and texture analysis for the early prediction of therapeutic response to neoadjuvant chemoradiotherapy and tumor recurrence of locally advanced rectal cancer. Eur Radiol 30(8):4201\u20134211","journal-title":"Eur Radiol"},{"issue":"2","key":"4651_CR10","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1007\/s12029-019-00291-0","volume":"51","author":"SP Shayesteh","year":"2020","unstructured":"Shayesteh SP, Alikhassi A, Farhan F, Gahletaki R, Soltanabadi M, Haddad P, Bitarafan-Rajabi A (2020) Prediction of response to neoadjuvant chemoradiotherapy by MRI-based machine learning texture analysis in rectal cancer patients. J Gastrointest Cancer 51(2):601\u2013609","journal-title":"J Gastrointest Cancer"},{"issue":"21","key":"4651_CR11","first-page":"5256","volume":"22","author":"K Nie","year":"2016","unstructured":"Nie K, Shi L, Chen Q, Hu X, Jabbour SK, Yue N, Niu T, Sun X (2016) Rectal cancer: assessment of neoadjuvant chemoradiation outcome based on radiomics of multiparametric MRI. Clin Cancer Investig J 22(21):5256\u20135264","journal-title":"Clin Cancer Investig J"},{"issue":"3","key":"4651_CR12","doi-asserted-by":"publisher","first-page":"1211","DOI":"10.1007\/s00330-018-5683-9","volume":"29","author":"Y Cui","year":"2019","unstructured":"Cui Y, Yang X, Shi Z, Yang Z, Du X, Zhao Z, Cheng X (2019) Radiomics analysis of multiparametric MRI for prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Eur Radiol 29(3):1211\u20131220","journal-title":"Eur Radiol"},{"issue":"11","key":"4651_CR13","doi-asserted-by":"publisher","first-page":"3608","DOI":"10.1007\/s00261-020-02502-w","volume":"45","author":"I Petkovska","year":"2020","unstructured":"Petkovska I, Tixier F, Ortiz EJ, Golia Pernicka JS, Paroder V, Bates DD, Horvat N, Fuqua J, Schilsky J, Gollub MJ, Garcia-Aguilar J, Veeraraghavan H (2020) Clinical utility of radiomics at baseline rectal MRI to predict complete response of rectal cancer after chemoradiation therapy. Abdom Radiol (NY) 45(11):3608\u20133617","journal-title":"Abdom Radiol (NY)"},{"issue":"3","key":"4651_CR14","doi-asserted-by":"publisher","first-page":"826","DOI":"10.1148\/radiol.2015151516","volume":"277","author":"PM Bossuyt","year":"2015","unstructured":"Bossuyt PM, Reitsma JB, Bruns DE, Gatsonis CA, Glasziou PP, Irwig L, Lijmer JG, Moher D, Rennie D, de Vet HC, Kressel HY, Rifai N, Golub RM, Altman DG, Hooft L, Korevaar DA, Cohen JF, Group S (2015) STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. Radiology 277(3):826\u2013832","journal-title":"Radiology"},{"issue":"21","key":"4651_CR15","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, Beets-Tan RGH, Fillion-Robin JC, Pieper S, Aerts H (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77(21):e104\u2013e107","journal-title":"Cancer Res"},{"issue":"10","key":"4651_CR16","doi-asserted-by":"publisher","first-page":"925","DOI":"10.1097\/DCR.0000000000000667","volume":"59","author":"J Bhoday","year":"2016","unstructured":"Bhoday J, Smith F, Siddiqui MR, Balyasnikova S, Swift RI, Perez R, Habr-Gama A, Brown G (2016) Magnetic resonance tumor regression grade and residual mucosal abnormality as predictors for pathological complete response in rectal cancer postneoadjuvant chemoradiotherapy. Dis Colon Rectum 59(10):925\u2013933","journal-title":"Dis Colon Rectum"},{"issue":"9","key":"4651_CR17","doi-asserted-by":"publisher","first-page":"1065","DOI":"10.3348\/kjr.2019.0797","volume":"21","author":"H Yoen","year":"2020","unstructured":"Yoen H, Park HE, Kim SH, Yoon JH, Hur BY, Bae JS, Kim JH, Oh HJ, Han JK (2020) Prognostic value of tumor regression grade on MR in rectal cancer: a large-scale, single-center experience. Korean J Radiol 21(9):1065\u20131076","journal-title":"Korean J Radiol"},{"issue":"6","key":"4651_CR18","doi-asserted-by":"publisher","first-page":"1454","DOI":"10.1245\/s10434-018-6462-1","volume":"25","author":"MR Weiser","year":"2018","unstructured":"Weiser MR (2018) AJCC 8th Edition: colorectal cancer. Ann Surg Oncol 25(6):1454\u20131455","journal-title":"Ann Surg Oncol"},{"issue":"1","key":"4651_CR19","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/s003840050072","volume":"12","author":"O Dworak","year":"1997","unstructured":"Dworak O, Keilholz L, Hoffmann A (1997) Pathological features of rectal cancer after preoperative radiochemotherapy. Int J Colorectal Dis 12(1):19\u201323","journal-title":"Int J Colorectal Dis"},{"key":"4651_CR20","doi-asserted-by":"publisher","DOI":"10.1155\/2014\/310542","volume":"2014","author":"MD Santos","year":"2014","unstructured":"Santos MD, Silva C, Rocha A, Matos E, Nogueira C, Lopes C (2014) Prognostic value of mandard and dworak tumor regression grading in rectal cancer: study of a single tertiary center. ISRN Surg 2014:310542","journal-title":"ISRN Surg"},{"issue":"9","key":"4651_CR21","doi-asserted-by":"publisher","first-page":"319","DOI":"10.3390\/cancers10090319","volume":"10","author":"C Song","year":"2018","unstructured":"Song C, Chung JH, Kang SB, Kim DW, Oh HK, Lee HS, Kim JW, Lee KW, Kim JH, Kim JS (2018) Impact of tumor regression grade as a major prognostic factor in locally advanced rectal cancer after neoadjuvant chemoradiotherapy: a proposal for a modified staging system. Cancers (Basel) 10(9):319","journal-title":"Cancers (Basel)"},{"issue":"12","key":"4651_CR22","doi-asserted-by":"publisher","first-page":"3538","DOI":"10.1158\/1078-0432.CCR-18-3190","volume":"25","author":"Z Liu","year":"2019","unstructured":"Liu Z, Li Z, Qu J, Zhang R, Zhou X, Li L, Sun K, Tang Z, Jiang H, Li H, Xiong Q, Ding Y, Zhao X, Wang K, Liu Z, Tian J (2019) Radiomics of multiparametric MRI for pretreatment prediction of pathologic complete response to neoadjuvant chemotherapy in breast cancer: a multicenter study. Clin Cancer Res 25(12):3538\u20133547","journal-title":"Clin Cancer Res"},{"issue":"3","key":"4651_CR23","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1148\/radiol.2018172300","volume":"287","author":"N Horvat","year":"2018","unstructured":"Horvat N, Veeraraghavan H, Khan M, Blazic I, Zheng J, Capanu M, Sala E, Garcia-Aguilar J, Gollub MJ, Petkovska I (2018) MR imaging of rectal cancer: radiomics analysis to assess treatment response after neoadjuvant therapy. Radiology 287(3):833\u2013843","journal-title":"Radiology"},{"issue":"6","key":"4651_CR24","doi-asserted-by":"publisher","first-page":"634","DOI":"10.3390\/bioengineering10060634","volume":"10","author":"L Shi","year":"2023","unstructured":"Shi L, Zhang Y, Hu J, Zhou W, Hu X, Cui T, Yue NJ, Sun X, Nie K (2023) Radiomics for the prediction of pathological complete response to neoadjuvant chemoradiation in locally advanced rectal cancer: a prospective observational trial. Bioengineering (Basel) 10(6):634","journal-title":"Bioengineering (Basel)"},{"issue":"1","key":"4651_CR25","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/s11604-022-01325-7","volume":"41","author":"AH Yardimci","year":"2023","unstructured":"Yardimci AH, Kocak B, Sel I, Bulut H, Bektas CT, Cin M, Dursun N, Bektas H, Mermut O, Yardimci VH, Kilickesmez O (2023) Radiomics of locally advanced rectal cancer: machine learning-based prediction of response to neoadjuvant chemoradiotherapy using pre-treatment sagittal T2-weighted MRI. Jpn J Radiol 41(1):71\u201382","journal-title":"Jpn J Radiol"},{"issue":"1","key":"4651_CR26","doi-asserted-by":"publisher","first-page":"5379","DOI":"10.1038\/s41598-021-84816-3","volume":"11","author":"A Delli Pizzi","year":"2021","unstructured":"Delli Pizzi A, Chiarelli AM, Chiacchiaretta P, d\u2019Annibale M, Croce P, Rosa C, Mastrodicasa D, Trebeschi S, Lambregts DMJ, Caposiena D, Serafini FL, Basilico R, Cocco G, Di Sebastiano P, Cinalli S, Ferretti A, Wise RG, Genovesi D, Beets-Tan RGH, Caulo M (2021) MRI-based clinical-radiomics model predicts tumor response before treatment in locally advanced rectal cancer. Sci Rep 11(1):5379","journal-title":"Sci Rep"},{"issue":"11","key":"4651_CR27","doi-asserted-by":"publisher","first-page":"6263","DOI":"10.1007\/s00330-020-06968-6","volume":"30","author":"H Shaish","year":"2020","unstructured":"Shaish H, Aukerman A, Vanguri R, Spinelli A, Armenta P, Jambawalikar S, Makkar J, Bentley-Hibbert S, Del Portillo A, Kiran R, Monti L, Bonifacio C, Kirienko M, Gardner KL, Schwartz L, Keller D (2020) Radiomics of MRI for pretreatment prediction of pathologic complete response, tumor regression grade, and neoadjuvant rectal score in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiation: an international multicenter study. Eur Radiol 30(11):6263\u20136273","journal-title":"Eur Radiol"},{"issue":"6","key":"4651_CR28","doi-asserted-by":"publisher","first-page":"1106","DOI":"10.1007\/s11605-015-2814-3","volume":"19","author":"A Kleiman","year":"2015","unstructured":"Kleiman A, Al-Khamis A, Farsi A, Kezouh A, Vuong T, Gordon PH, Vasilevsky CA, Morin N, Faria J, Ghitulescu G, Boutros M (2015) Normalization of CEA levels post-neoadjuvant therapy is a strong predictor of pathologic complete response in rectal cancer. J Gastrointest Surg 19(6):1106\u20131112","journal-title":"J Gastrointest Surg"},{"issue":"2","key":"4651_CR29","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1002\/jso.20320","volume":"93","author":"YA Park","year":"2006","unstructured":"Park YA, Sohn SK, Seong J, Baik SH, Lee KY, Kim NK, Cho CW (2006) Serum CEA as a predictor for the response to preoperative chemoradiation in rectal cancer. J Surg Oncol 93(2):145\u2013150","journal-title":"J Surg Oncol"},{"issue":"3","key":"4651_CR30","doi-asserted-by":"publisher","first-page":"632","DOI":"10.1007\/s00261-019-02321-8","volume":"45","author":"JJM van Griethuysen","year":"2020","unstructured":"van Griethuysen JJM, Lambregts DMJ, Trebeschi S, Lahaye MJ, Bakers FCH, Vliegen RFA, Beets GL, Aerts H, Beets-Tan RGH (2020) Radiomics performs comparable to morphologic assessment by expert radiologists for prediction of response to neoadjuvant chemoradiotherapy on baseline staging MRI in rectal cancer. Abdom Radiol (NY) 45(3):632\u2013643","journal-title":"Abdom Radiol (NY)"},{"issue":"4","key":"4651_CR31","doi-asserted-by":"publisher","first-page":"2312","DOI":"10.1007\/s00330-019-06565-2","volume":"30","author":"JK Jang","year":"2020","unstructured":"Jang JK, Choi SH, Park SH, Kim KW, Kim HJ, Lee JS, Kim AY (2020) MR tumor regression grade for pathological complete response in rectal cancer post neoadjuvant chemoradiotherapy: a systematic review and meta-analysis for accuracy. Eur Radiol 30(4):2312\u20132323","journal-title":"Eur Radiol"},{"issue":"12","key":"4651_CR32","doi-asserted-by":"publisher","first-page":"1671","DOI":"10.1002\/bjs.10898","volume":"105","author":"JK Jang","year":"2018","unstructured":"Jang JK, Lee JL, Park SH, Park HJ, Park IJ, Kim JH, Choi SH, Kim J, Yu CS, Kim JC (2018) Magnetic resonance tumour regression grade and pathological correlates in patients with rectal cancer. Br J Surg 105(12):1671\u20131679","journal-title":"Br J Surg"},{"key":"4651_CR33","first-page":"78","volume-title":"In: IEEE 6th International Conference on Advanced Computing\u00a027\u201328","author":"S Yadav","year":"2016","unstructured":"Yadav S (2016) Analysis of k-fold cross-validation over hold-out validation on colossal datasets for quality classification. In: IEEE 6th International Conference on Advanced Computing\u00a027\u201328. pp 78\u201383"}],"container-title":["International Journal of Colorectal Disease"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00384-024-04651-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00384-024-04651-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00384-024-04651-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,8]],"date-time":"2025-01-08T04:20:31Z","timestamp":1736310031000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00384-024-04651-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,24]]},"references-count":33,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["4651"],"URL":"https:\/\/doi.org\/10.1007\/s00384-024-04651-6","relation":{},"ISSN":["1432-1262"],"issn-type":[{"value":"1432-1262","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,24]]},"assertion":[{"value":"17 May 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 May 2024","order":2,"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"}}],"article-number":"78"}}