{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T20:44:38Z","timestamp":1790109878602,"version":"4.0.1"},"reference-count":67,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T00:00:00Z","timestamp":1710979200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T00:00:00Z","timestamp":1710979200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2023YFF1204600"],"award-info":[{"award-number":["2023YFF1204600"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82227802"],"award-info":[{"award-number":["82227802"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82302306"],"award-info":[{"award-number":["82302306"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Clinical Frontier Technology Program of the First Affiliated Hospital of Jinan University, China","award":["JNU1AF-CFTP-2022-a01201"],"award-info":[{"award-number":["JNU1AF-CFTP-2022-a01201"]}]},{"name":"the Science and Technology Projects in Guangzhou","award":["202201020022"],"award-info":[{"award-number":["202201020022"]}]},{"name":"the Science and Technology Projects in Guangzhou","award":["2023A03J1036"],"award-info":[{"award-number":["2023A03J1036"]}]},{"name":"the Science and Technology Projects in Guangzhou","award":["2023A03J1038"],"award-info":[{"award-number":["2023A03J1038"]}]},{"name":"the Science and Technology Youth Talent Nurturing Program of Jinan University","award":["21623209"],"award-info":[{"award-number":["21623209"]}]},{"DOI":"10.13039\/501100002858","name":"the Postdoctoral Science Foundation of China","doi-asserted-by":"crossref","award":["2022M721349"],"award-info":[{"award-number":["2022M721349"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Radiol med"],"DOI":"10.1007\/s11547-024-01796-w","type":"journal-article","created":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T08:02:07Z","timestamp":1711008127000},"page":"598-614","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Image-based artificial intelligence for the prediction of pathological complete response to neoadjuvant chemoradiotherapy in patients with rectal cancer: a systematic review and meta-analysis"],"prefix":"10.1007","volume":"129","author":[{"given":"Hui","family":"Shen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhe","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiuying","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingjing","family":"You","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuixing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6286-6227","authenticated-orcid":false,"given":"Bin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,3,21]]},"reference":[{"issue":"3","key":"1796_CR1","doi-asserted-by":"publisher","first-page":"233","DOI":"10.3322\/caac.21772","volume":"73","author":"RL Siegel","year":"2023","unstructured":"Siegel RL, Wagle NS, Cercek A, Smith RA, Jemal A (2023) Colorectal cancer statistics. A Cancer J Clin 73(3):233\u2013254","journal-title":"A Cancer J Clin"},{"issue":"1","key":"1796_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10147-017-1101-6","volume":"23","author":"T Watanabe","year":"2018","unstructured":"Watanabe T, Muro K, Ajioka Y, Hashiguchi Y, Ito Y, Saito Y et al (2018) Japanese society for cancer of the colon and rectum (JSCCR) guidelines 2016 for the treatment of colorectal cancer. Int J Clin Oncol 23(1):1\u201334","journal-title":"Int J Clin Oncol"},{"issue":"5","key":"1796_CR3","doi-asserted-by":"publisher","first-page":"1412","DOI":"10.1245\/s10434-018-07147-0","volume":"26","author":"T Wan","year":"2019","unstructured":"Wan T, Zhang XF, Liang C, Liao CW, Li JY, Zhou YM (2019) The prognostic value of a pathologic complete response after neoadjuvant therapy for digestive cancer: systematic review and meta-analysis of 21 studies. Ann Surg Oncol 26(5):1412\u20131420","journal-title":"Ann Surg Oncol"},{"issue":"9","key":"1796_CR4","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, R\u00f6del C, Kuo LJ et al (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":"1","key":"1796_CR5","doi-asserted-by":"publisher","first-page":"e13","DOI":"10.1016\/S1470-2045(14)70398-5","volume":"16","author":"CA Marijnen","year":"2015","unstructured":"Marijnen CA (2015) Organ preservation in rectal cancer: have all questions been answered? Lancet Oncol 16(1):e13-22","journal-title":"Lancet Oncol"},{"issue":"7","key":"1796_CR6","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1001\/jamasurg.2020.0226","volume":"155","author":"JJ Smith","year":"2020","unstructured":"Smith JJ, Paty PB, Garcia-Aguilar J (2020) Watch and wait in rectal cancer or more wait and see? JAMA Surg 155(7):657\u2013658","journal-title":"JAMA Surg"},{"issue":"12","key":"1796_CR7","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1038\/ncpgasthep0977","volume":"4","author":"JK LeBlanc","year":"2007","unstructured":"LeBlanc JK (2007) Imaging and management of rectal cancer. Nat Clin Pract Gastroenterol Hepatol 4(12):665\u2013676","journal-title":"Nat Clin Pract Gastroenterol Hepatol"},{"issue":"1","key":"1796_CR8","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1038\/s41698-021-00205-z","volume":"5","author":"J Yan","year":"2021","unstructured":"Yan J, Zhang B, Zhang S, Cheng J, Liu X, Wang W et al (2021) Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients. NPJ Precision Oncol 5(1):72","journal-title":"NPJ Precision Oncol"},{"issue":"3","key":"1796_CR9","doi-asserted-by":"publisher","first-page":"1104","DOI":"10.1007\/s00330-018-5719-1","volume":"29","author":"MJ Gollub","year":"2019","unstructured":"Gollub MJ, Blazic I, Felder S, Knezevic A, Gonen M, Garcia-Aguilar J et al (2019) Value of adding dynamic contrast-enhanced MRI visual assessment to conventional MRI and clinical assessment in the diagnosis of complete tumour response to chemoradiotherapy for rectal cancer. Eur Radiol 29(3):1104\u20131113","journal-title":"Eur Radiol"},{"issue":"7","key":"1796_CR10","doi-asserted-by":"publisher","first-page":"812","DOI":"10.3348\/kjr.2020.0483","volume":"21","author":"SH Park","year":"2020","unstructured":"Park SH, Cho SH, Choi SH, Jang JK, Kim MJ, Kim SH et al (2020) MRI assessment of complete response to preoperative chemoradiation therapy for rectal cancer: 2020 guide for practice from the korean society of abdominal radiology. Korean J Radiol 21(7):812\u2013828","journal-title":"Korean J Radiol"},{"issue":"3","key":"1796_CR11","doi-asserted-by":"publisher","first-page":"1506","DOI":"10.1007\/s00330-021-08251-8","volume":"32","author":"NW Schurink","year":"2022","unstructured":"Schurink NW, van Kranen SR, Roberti S, van Griethuysen JJM, Bogveradze N, Castagnoli F et al (2022) Sources of variation in multicenter rectal MRI data and their effect on radiomics feature reproducibility. Eur Radiol 32(3):1506\u20131516","journal-title":"Eur Radiol"},{"issue":"2","key":"1796_CR12","first-page":"563","volume":"278","author":"RJ Gillies","year":"2016","unstructured":"Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures. They Are Data Radiol 278(2):563\u2013577","journal-title":"They Are Data Radiol"},{"issue":"2","key":"1796_CR13","doi-asserted-by":"publisher","first-page":"E256","DOI":"10.1148\/radiol.2021219005","volume":"299","author":"MR Tomaszewski","year":"2021","unstructured":"Tomaszewski MR, Gillies RJ (2021) The biological meaning of radiomic features. Radiology 299(2):E256","journal-title":"Radiology"},{"key":"1796_CR14","doi-asserted-by":"publisher","first-page":"1026216","DOI":"10.3389\/fonc.2022.1026216","volume":"12","author":"LL Jia","year":"2022","unstructured":"Jia LL, Zheng QY, Tian JH, He DL, Zhao JX, Zhao LP et al (2022) Artificial intelligence with magnetic resonance imaging for prediction of pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer: a systematic review and meta-analysis. Front Oncol 12:1026216","journal-title":"Front Oncol"},{"issue":"3","key":"1796_CR15","doi-asserted-by":"publisher","first-page":"800","DOI":"10.1148\/radiol.2017171920","volume":"286","author":"SH Park","year":"2018","unstructured":"Park SH, Han K (2018) Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction. Radiology 286(3):800\u2013809","journal-title":"Radiology"},{"issue":"22","key":"1796_CR16","doi-asserted-by":"publisher","first-page":"e152","DOI":"10.3346\/jkms.2018.33.e152","volume":"33","author":"SH Park","year":"2018","unstructured":"Park SH, Kressel HY (2018) Connecting technological innovation in artificial intelligence to real-world medical practice through rigorous clinical validation: what peer-reviewed medical journals could do. J Korean Med Sci 33(22):e152","journal-title":"J Korean Med Sci"},{"issue":"12","key":"1796_CR17","doi-asserted-by":"publisher","first-page":"3791","DOI":"10.1007\/s00259-021-05339-7","volume":"48","author":"M Kirienko","year":"2021","unstructured":"Kirienko M, Sollini M, Ninatti G, Loiacono D, Giacomello E, Gozzi N et al (2021) Distributed learning: a reliable privacy-preserving strategy to change multicenter collaborations using AI. Eur J Nucl Med Mol Imaging 48(12):3791\u20133804","journal-title":"Eur J Nucl Med Mol Imaging"},{"issue":"1","key":"1796_CR18","doi-asserted-by":"publisher","first-page":"5088","DOI":"10.1038\/s41467-020-18685-1","volume":"11","author":"C Jin","year":"2020","unstructured":"Jin C, Chen W, Cao Y, Xu Z, Tan Z, Zhang X et al (2020) Development and evaluation of an artificial intelligence system for COVID-19 diagnosis. Nat Commun 11(1):5088","journal-title":"Nat Commun"},{"issue":"7743","key":"1796_CR19","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1038\/s41586-019-0912-1","volume":"566","author":"M Reichstein","year":"2019","unstructured":"Reichstein M, Camps-Valls G, Stevens B, Jung M, Denzler J, Carvalhais N et al (2019) Deep learning and process understanding for data-driven Earth system science. Nature 566(7743):195\u2013204","journal-title":"Nature"},{"key":"1796_CR20","first-page":"n71","volume":"372","author":"MJ Page","year":"2021","unstructured":"Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD et al (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ (Clin Res ed) 372:n71","journal-title":"BMJ (Clin Res ed)"},{"issue":"10","key":"1796_CR21","doi-asserted-by":"publisher","first-page":"1663","DOI":"10.1038\/s41591-021-01517-0","volume":"27","author":"V Sounderajah","year":"2021","unstructured":"Sounderajah V, Ashrafian H, Rose S, Shah NH, Ghassemi M, Golub R et al (2021) A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI. Nat Med 27(10):1663\u20131665","journal-title":"Nat Med"},{"issue":"8","key":"1796_CR22","doi-asserted-by":"publisher","first-page":"529","DOI":"10.7326\/0003-4819-155-8-201110180-00009","volume":"155","author":"PF Whiting","year":"2011","unstructured":"Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB et al (2011) QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 155(8):529\u2013536","journal-title":"Ann Intern Med"},{"issue":"11","key":"1796_CR23","doi-asserted-by":"publisher","first-page":"1592","DOI":"10.7326\/M21-2234","volume":"174","author":"B Yang","year":"2021","unstructured":"Yang B, Mallett S, Takwoingi Y, Davenport CF, Hyde CJ, Whiting PF et al (2021) QUADAS-C: a tool for assessing risk of bias in comparative diagnostic accuracy studies. Ann Intern Med 174(11):1592\u20131599","journal-title":"Ann Intern Med"},{"issue":"7382","key":"1796_CR24","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1136\/bmj.326.7382.219","volume":"326","author":"DG Altman","year":"2003","unstructured":"Altman DG, Bland JM (2003) Interaction revisited: the difference between two estimates. BMJ (Clin Res ed) 326(7382):219","journal-title":"BMJ (Clin Res ed)"},{"issue":"11","key":"1796_CR25","doi-asserted-by":"publisher","first-page":"3645","DOI":"10.1007\/s00261-022-03625-y","volume":"47","author":"S Abbaspour","year":"2022","unstructured":"Abbaspour S, Abdollahi H, Arabalibeik H, Barahman M, Arefpour AM, Fadavi P, Ay M, Mahdavi SR (2022) Endorectal ultrasound radiomics in locally advanced rectal cancer patients: despeckling and radiotherapy response prediction using machine learning. Abdom Radiol 47(11):3645\u20133659","journal-title":"Abdom Radiol"},{"issue":"5","key":"1796_CR26","doi-asserted-by":"publisher","first-page":"1531","DOI":"10.1002\/jmri.27140","volume":"52","author":"JT Antunes","year":"2020","unstructured":"Antunes JT, Ofshteyn A, Bera K, Wang EY, Brady JT, Willis JE et al (2020) Radiomic features of primary rectal cancers on baseline T-2-weighted MRI are associated with pathologic complete response to neoadjuvant chemoradiation: a multisite study. J Magn Reson Imaging 52(5):1531\u20131541","journal-title":"J Magn Reson Imaging"},{"issue":"1","key":"1796_CR27","doi-asserted-by":"publisher","first-page":"12611","DOI":"10.1038\/s41598-018-30657-6","volume":"8","author":"JE Bibault","year":"2018","unstructured":"Bibault JE, Giraud P, Housset M, Durdux C, Taieb J, Berger A et al (2018) Deep learning and radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer. Sci Rep 8(1):12611","journal-title":"Sci Rep"},{"issue":"1","key":"1796_CR28","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1186\/s13014-022-02048-9","volume":"17","author":"L Boldrini","year":"2022","unstructured":"Boldrini L, Lenkowicz J, Orlandini LC, Yin G, Cusumano D, Chiloiro G et al (2022) Applicability of a pathological complete response magnetic resonance-based radiomics model for locally advanced rectal cancer in intercontinental cohort. Radiat Oncol (London, England) 17(1):78","journal-title":"Radiat Oncol (London, England)"},{"issue":"4","key":"1796_CR29","doi-asserted-by":"publisher","first-page":"1079","DOI":"10.3390\/cancers14041079","volume":"14","author":"A Bordron","year":"2022","unstructured":"Bordron A, Rio E, Badic B, Miranda O, Pradier O, Hatt M, Visvikis D, Lucia F, Schick U, Bourbonne V (2022) External validation of a radiomics model for the prediction of complete response to neoadjuvant chemoradiotherapy in rectal cancer. Cancers 14(4):1079","journal-title":"Cancers"},{"key":"1796_CR30","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1016\/j.radonc.2019.07.033","volume":"142","author":"P Bulens","year":"2020","unstructured":"Bulens P, Couwenberg A, Intven M, Debucquoy A, Vandecaveye V, Van Cutsem E et al (2020) Predicting the tumor response to chemoradiotherapy for rectal cancer: model development and external validation using MRI radiomics. Radiother Oncol 142:246\u2013252","journal-title":"Radiother Oncol"},{"issue":"11","key":"1796_CR31","doi-asserted-by":"publisher","first-page":"5072","DOI":"10.1007\/s00261-021-03219-0","volume":"46","author":"Y Cheng","year":"2021","unstructured":"Cheng Y, Luo Y, Hu Y, Zhang Z, Wang X, Yu Q et al (2021) Multiparametric MRI-based radiomics approaches on predicting response to neoadjuvant chemoradiotherapy (nCRT) in patients with rectal cancer. Abdom Radiol (New York) 46(11):5072\u20135085","journal-title":"Abdom Radiol (New York)"},{"issue":"12","key":"1796_CR32","doi-asserted-by":"publisher","first-page":"3082","DOI":"10.3390\/cancers15123082","volume":"15","author":"G Chiloiro","year":"2023","unstructured":"Chiloiro G, Cusumano D, Romano A, Boldrini L, Nicol\u00ec G, Votta C, Tran HE, Barbaro B, Carano D, Valentini V, Gambacorta MA (2023) Delta radiomic analysis of mesorectum to predict treatment response and prognosis in locally advanced rectal cancer. Cancers 15(12):3082","journal-title":"Cancers"},{"issue":"3","key":"1796_CR33","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 et al (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":"1","key":"1796_CR34","doi-asserted-by":"publisher","first-page":"e8","DOI":"10.1016\/S2589-7500(21)00215-6","volume":"4","author":"L Feng","year":"2022","unstructured":"Feng L, Liu Z, Li C, Li Z, Lou X, Shao L et al (2022) Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study. Lancet Digital health 4(1):e8\u2013e17","journal-title":"Lancet Digital health"},{"key":"1796_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ejrad.2019.06.013","volume":"118","author":"R Ferrari","year":"2019","unstructured":"Ferrari R, Mancini-Terracciano C, Voena C, Rengo M, Zerunian M, Ciardiello A et al (2019) MR-based artificial intelligence model to assess response to therapy in locally advanced rectal cancer. Eur J Radiol 118:1\u20139","journal-title":"Eur J Radiol"},{"issue":"3","key":"1796_CR36","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 et al (2018) MR imaging of rectal cancer: radiomics analysis to assess treatment response after neoadjuvant therapy. Radiology 287(3):833\u2013843","journal-title":"Radiology"},{"issue":"8","key":"1796_CR37","doi-asserted-by":"publisher","first-page":"2770","DOI":"10.1007\/s00261-022-03572-8","volume":"47","author":"N Horvat","year":"2022","unstructured":"Horvat N, Veeraraghavan H, Nahas CSR, Bates DDB, Ferreira FR, Zheng J et al (2022) Combined artificial intelligence and radiologist model for predicting rectal cancer treatment response from magnetic resonance imaging: an external validation study. Abdom Radiol (New York) 47(8):2770\u20132782","journal-title":"Abdom Radiol (New York)"},{"key":"1796_CR38","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/j.radonc.2021.06.019","volume":"161","author":"BS Jang","year":"2021","unstructured":"Jang BS, Lim YJ, Song C, Jeon SH, Lee KW, Kang SB et al (2021) Image-based deep learning model for predicting pathological response in rectal cancer using post-chemoradiotherapy magnetic resonance imaging. Radiother Oncol: J Eur Soc Ther Radiol Oncol 161:183\u2013190","journal-title":"Radiother Oncol: J Eur Soc Ther Radiol Oncol"},{"issue":"2","key":"1796_CR39","doi-asserted-by":"publisher","first-page":"971","DOI":"10.1007\/s00330-021-08144-w","volume":"32","author":"VS Jayaprakasam","year":"2022","unstructured":"Jayaprakasam VS, Paroder V, Gibbs P, Bajwa R, Gangai N, Sosa RE et al (2022) MRI radiomics features of mesorectal fat can predict response to neoadjuvant chemoradiation therapy and tumor recurrence in patients with locally advanced rectal cancer. Eur Radiol 32(2):971\u2013980","journal-title":"Eur Radiol"},{"issue":"1","key":"1796_CR40","doi-asserted-by":"publisher","first-page":"1851","DOI":"10.1038\/s41467-021-22188-y","volume":"12","author":"C Jin","year":"2021","unstructured":"Jin C, Yu H, Ke J, Ding P, Yi Y, Jiang X et al (2021) Predicting treatment response from longitudinal images using multi-task deep learning. Nat Commun 12(1):1851","journal-title":"Nat Commun"},{"issue":"20","key":"1796_CR41","doi-asserted-by":"publisher","first-page":"9494","DOI":"10.3390\/app11209494","volume":"11","author":"S Lee","year":"2021","unstructured":"Lee S, Lim J, Shin J, Kim S, Hwang H (2021) Pathologic complete response prediction after neoadjuvant chemoradiation therapy for rectal cancer using radiomics and deep embedding network of MRI. Appl Sci 11(20):9494","journal-title":"Appl Sci"},{"issue":"1","key":"1796_CR42","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1186\/s12880-021-00560-0","volume":"21","author":"Z Li","year":"2021","unstructured":"Li Z, Ma X, Shen F, Lu H, Xia Y, Lu J (2021) Evaluating treatment response to neoadjuvant chemoradiotherapy in rectal cancer using various MRI-based radiomics models. BMC Med Imaging 21(1):30","journal-title":"BMC Med Imaging"},{"issue":"23","key":"1796_CR43","doi-asserted-by":"publisher","first-page":"7253","DOI":"10.1158\/1078-0432.CCR-17-1038","volume":"23","author":"Z Liu","year":"2017","unstructured":"Liu Z, Zhang XY, Shi YJ, Wang L, Zhu HT, Tang Z et al (2017) Radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Clin Cancer Res: An Official J Am Associat Cancer Res 23(23):7253\u20137262","journal-title":"Clin Cancer Res: An Official J Am Associat Cancer Res"},{"key":"1796_CR44","doi-asserted-by":"publisher","first-page":"850774","DOI":"10.3389\/fonc.2022.850774","volume":"12","author":"Y Mao","year":"2022","unstructured":"Mao Y, Pei Q, Fu Y, Liu H, Chen C, Li H et al (2022) Pre-Treatment computed tomography radiomics for predicting the response to neoadjuvant chemoradiation in locally advanced rectal cancer: a retrospective study. Front Oncol 12:850774","journal-title":"Front Oncol"},{"issue":"12","key":"1796_CR45","doi-asserted-by":"publisher","first-page":"3004","DOI":"10.3390\/cancers14123004","volume":"14","author":"V Nardone","year":"2022","unstructured":"Nardone V, Reginelli A, Grassi R, Vacca G, Giacobbe G, Angrisani A, Clemente A, Danti G, Correale P, Carbone SF, Pirtoli L (2022) Ability of delta radiomics to predict a complete pathological response in patients with loco-regional rectal cancer addressed to neoadjuvant chemo-radiation and surgery. Cancers 14(12):3004","journal-title":"Cancers"},{"key":"1796_CR46","doi-asserted-by":"publisher","first-page":"711747","DOI":"10.3389\/fonc.2021.711747","volume":"11","author":"X Pang","year":"2021","unstructured":"Pang X, Wang F, Zhang Q, Li Y, Huang R, Yin X et al (2021) A Pipeline for predicting the treatment response of neoadjuvant chemoradiotherapy for locally advanced rectal cancer using single mri modality: combining deep segmentation network and radiomics analysis based on \u201csuspicious region.\u201d Front Oncol 11:711747","journal-title":"Front Oncol"},{"issue":"11","key":"1796_CR47","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 et al (2020) Clinical utility of radiomics at baseline rectal MRI to predict complete response of rectal cancer after chemoradiation therapy. Abdom Radiol (New York) 45(11):3608\u20133617","journal-title":"Abdom Radiol (New York)"},{"issue":"5","key":"1796_CR48","doi-asserted-by":"publisher","first-page":"207","DOI":"10.21037\/atm.2020.01.107","volume":"8","author":"WC Shen","year":"2020","unstructured":"Shen WC, Chen SW, Wu KC, Lee PY, Feng CL, Hsieh TC et al (2020) Predicting pathological complete response in rectal cancer after chemoradiotherapy with a random forest using (18)F-fluorodeoxyglucose positron emission tomography and computed tomography radiomics. Ann Translat Med 8(5):207","journal-title":"Ann Translat Med"},{"issue":"2","key":"1796_CR49","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1148\/radiol.211986","volume":"303","author":"J Shin","year":"2022","unstructured":"Shin J, Seo N, Baek SE, Son NH, Lim JS, Kim NK et al (2022) MRI radiomics model predicts pathologic complete response of rectal cancer following chemoradiotherapy. Radiology 303(2):351\u2013358","journal-title":"Radiology"},{"issue":"4","key":"1796_CR50","doi-asserted-by":"publisher","first-page":"2426","DOI":"10.1007\/s00330-021-08303-z","volume":"32","author":"T Tochigi","year":"2022","unstructured":"Tochigi T, Kamran SC, Parakh A, Noda Y, Ganeshan B, Blaszkowsky LS et al (2022) Response prediction of neoadjuvant chemoradiation therapy in locally advanced rectal cancer using CT-based fractal dimension analysis. Eur Radiol 32(4):2426\u20132436","journal-title":"Eur Radiol"},{"issue":"Suppl 1","key":"1796_CR51","doi-asserted-by":"publisher","first-page":"S95","DOI":"10.1016\/j.acra.2020.10.026","volume":"28","author":"L Wan","year":"2021","unstructured":"Wan L, Peng W, Zou S, Ye F, Geng Y, Ouyang H et al (2021) MRI-based delta-radiomics are predictive of pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Acad Radiol 28(Suppl 1):S95-s104","journal-title":"Acad Radiol"},{"key":"1796_CR52","doi-asserted-by":"publisher","first-page":"1059033","DOI":"10.3389\/frai.2022.1059033","volume":"5","author":"D Wang","year":"2022","unstructured":"Wang D, Lee SH, Geng H, Zhong H, Plastaras J, Wojcieszynski A et al (2022) Interpretable machine learning for predicting pathologic complete response in patients treated with chemoradiation therapy for rectal adenocarcinoma. Front Artif Intelligence 5:1059033","journal-title":"Front Artif Intelligence"},{"issue":"3","key":"1796_CR53","doi-asserted-by":"publisher","first-page":"1906","DOI":"10.1007\/s00330-022-09204-5","volume":"33","author":"Q Wei","year":"2023","unstructured":"Wei Q, Chen Z, Tang Y, Chen W, Zhong L, Mao L et al (2023) External validation and comparison of MR-based radiomics models for predicting pathological complete response in locally advanced rectal cancer: a two-centre, multi-vendor study. Eur Radiol 33(3):1906\u20131917","journal-title":"Eur Radiol"},{"key":"1796_CR54","doi-asserted-by":"publisher","first-page":"S176","DOI":"10.1016\/j.acra.2022.12.037","volume":"30","author":"L Wen","year":"2023","unstructured":"Wen L, Liu J, Hu P, Bi F, Liu S, Jian L, Zhu S, Nie S, Cao F, Lu Q, Yu X (2023) MRI-based radiomic models outperform radiologists in predicting pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Acad Radiol 30:S176\u2013S184","journal-title":"Acad Radiol"},{"issue":"1","key":"1796_CR55","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 et al (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":"1796_CR56","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1148\/radiol.2020190936","volume":"296","author":"XY Zhang","year":"2020","unstructured":"Zhang XY, Wang L, Zhu HT, Li ZW, Ye M, Li XT et al (2020) Predicting rectal cancer response to neoadjuvant chemoradiotherapy using deep learning of diffusion kurtosis MRI. Radiology 296(1):56\u201364","journal-title":"Radiology"},{"issue":"2","key":"1796_CR57","doi-asserted-by":"publisher","first-page":"562","DOI":"10.1002\/jmri.28033","volume":"56","author":"HT Zhu","year":"2022","unstructured":"Zhu HT, Zhang XY, Shi YJ, Li XT, Sun YS (2022) The conversion of MRI data with multiple b-values into signature-like pictures to predict treatment response for rectal cancer. J Magn Resonance Imaging: JMRI 56(2):562\u2013569","journal-title":"J Magn Resonance Imaging: JMRI"},{"issue":"1","key":"1796_CR58","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1186\/s12967-021-02919-x","volume":"19","author":"Z Zhuang","year":"2021","unstructured":"Zhuang Z, Liu Z, Li J, Wang X, Xie P, Xiong F et al (2021) Radiomic signature of the FOWARC trial predicts pathological response to neoadjuvant treatment in rectal cancer. J Transl Med 19(1):256","journal-title":"J Transl Med"},{"issue":"8","key":"1796_CR59","doi-asserted-by":"publisher","first-page":"e2228776","DOI":"10.1001\/jamanetworkopen.2022.28776","volume":"5","author":"L Lu","year":"2022","unstructured":"Lu L, Phua QS, Bacchi S, Goh R, Gupta AK, Kovoor JG et al (2022) Small study effects in diagnostic imaging accuracy: a meta-analysis. JAMA Netw Open 5(8):e2228776","journal-title":"JAMA Netw Open"},{"issue":"3","key":"1796_CR60","doi-asserted-by":"publisher","first-page":"1737","DOI":"10.1007\/s00330-022-09191-7","volume":"33","author":"S Lian","year":"2023","unstructured":"Lian S, Liu H, Meng T, Ma L, Zeng W, Xie C (2023) Quantitative synthetic MRI for predicting locally advanced rectal cancer response to neoadjuvant chemoradiotherapy. Eur Radiol 33(3):1737\u20131745","journal-title":"Eur Radiol"},{"issue":"4","key":"1796_CR61","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.ejca.2011.11.036","volume":"48","author":"P Lambin","year":"2012","unstructured":"Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48(4):441\u2013446","journal-title":"Eur J Cancer"},{"issue":"4","key":"1796_CR62","doi-asserted-by":"publisher","first-page":"1143","DOI":"10.1016\/j.ijrobp.2018.05.053","volume":"102","author":"A Traverso","year":"2018","unstructured":"Traverso A, Wee L, Dekker A, Gillies R (2018) Repeatability and reproducibility of radiomic features: a systematic review. Int J Radiat Oncol Biol Phys 102(4):1143\u20131158","journal-title":"Int J Radiat Oncol Biol Phys"},{"issue":"1","key":"1796_CR63","doi-asserted-by":"publisher","first-page":"e220715","DOI":"10.1148\/radiol.220715","volume":"307","author":"M Gidwani","year":"2023","unstructured":"Gidwani M, Chang K, Patel JB, Hoebel KV, Ahmed SR, Singh P et al (2023) Inconsistent partitioning and unproductive feature associations yield idealized radiomic models. Radiology 307(1):e220715","journal-title":"Radiology"},{"key":"1796_CR64","doi-asserted-by":"publisher","first-page":"106047","DOI":"10.1016\/j.cmpb.2021.106047","volume":"204","author":"Y Jiao","year":"2021","unstructured":"Jiao Y, Li J, Qian C, Fei S (2021) Deep learning-based tumor microenvironment analysis in colon adenocarcinoma histopathological whole-slide images. Comput Methods Programs Biomed 204:106047","journal-title":"Comput Methods Programs Biomed"},{"issue":"6","key":"1796_CR65","doi-asserted-by":"publisher","first-page":"7270","DOI":"10.1109\/TPAMI.2022.3218591","volume":"45","author":"W Chen","year":"2023","unstructured":"Chen W, Liu Y, Wang W, Bakker EM, Georgiou T, Fieguth P et al (2023) Deep learning for instance retrieval: a survey. IEEE Trans Pattern Anal Mach Intell 45(6):7270\u20137292","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"4","key":"1796_CR66","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1016\/j.jclinepi.2010.07.017","volume":"64","author":"GH Guyatt","year":"2011","unstructured":"Guyatt GH, Oxman AD, Vist G, Kunz R, Brozek J, Alonso-Coello P et al (2011) GRADE guidelines: 4. Rating the quality of evidence\u2013study limitations (risk of bias). J Clin Epidemiol 64(4):407\u2013415","journal-title":"J Clin Epidemiol"},{"issue":"2","key":"1796_CR67","doi-asserted-by":"publisher","first-page":"172","DOI":"10.2967\/jnumed.121.262464","volume":"63","author":"F Orlhac","year":"2022","unstructured":"Orlhac F, Eertink JJ, Cottereau AS, Zijlstra JM, Thieblemont C, Meignan M et al (2022) A guide to ComBat Harmonization of Imaging biomarkers in multicenter studies. J Nucl Med 63(2):172\u2013179","journal-title":"J Nucl Med"}],"container-title":["La radiologia medica"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11547-024-01796-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11547-024-01796-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11547-024-01796-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T08:05:13Z","timestamp":1713254713000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11547-024-01796-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,21]]},"references-count":67,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["1796"],"URL":"https:\/\/doi.org\/10.1007\/s11547-024-01796-w","relation":{},"ISSN":["1826-6983"],"issn-type":[{"value":"1826-6983","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,21]]},"assertion":[{"value":"21 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 January 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 March 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors declare that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}