{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T20:34:42Z","timestamp":1782419682529,"version":"3.54.5"},"reference-count":30,"publisher":"Elsevier BV","issue":"3","license":[{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"funder":[{"DOI":"10.13039\/501100010040","name":"Taishan Scholar Project of Shandong Province","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100010040","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010029","name":"Taishan Scholar Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ts20171209"],"award-info":[{"award-number":["ts20171209"]}],"id":[{"id":"10.13039\/501100010029","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.fr","clinicalkey.jp","clinicalkey.es","clinicalkey.com.au","academicradiology.org","clinicalkey.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Academic Radiology"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1016\/j.acra.2024.10.011","type":"journal-article","created":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T11:52:07Z","timestamp":1730634727000},"page":"1631-1644","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":9,"title":["A Stacked Multimodality Model Based on Functional MRI Features and Deep Learning Radiomics for Predicting the Early Response to Radiotherapy in Nasopharyngeal Carcinoma"],"prefix":"10.1016","volume":"32","author":[{"given":"Xiaowen","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingtao","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ya","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lizhen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiujuan","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.acra.2024.10.011_bib1","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.oraloncology.2017.10.020","article-title":"Delayed clinical complete response to intensity-modulated radiotherapy in nasopharyngeal carcinoma","volume":"75","author":"Li","year":"2017","journal-title":"Oral Oncology"},{"key":"10.1016\/j.acra.2024.10.011_bib2","doi-asserted-by":"crossref","DOI":"10.1016\/j.oraloncology.2020.105130","article-title":"The effect of adjuvant chemotherapy and early tumor regression on the outcome of nasopharyngeal cancer patients treated with concurrent chemoradiotherapy","volume":"113","author":"Lee","year":"2021","journal-title":"Oral Oncology"},{"key":"10.1016\/j.acra.2024.10.011_bib3","doi-asserted-by":"crossref","first-page":"861","DOI":"10.7150\/jca.17957","article-title":"Magnetic resonance imaging-detected tumor residue after intensity-modulated radiation therapy and its association with post-radiation plasma epstein-barr virus deoxyribonucleic acid in nasopharyngeal carcinoma","volume":"8","author":"Lv","year":"2017","journal-title":"J Cancer"},{"key":"10.1016\/j.acra.2024.10.011_bib4","first-page":"377","article-title":"Evolution of treatment for nasopharyngeal cancer \u2013 success and setback in the intensity-modulated radiotherapy era","volume":"110","author":"Lee","year":"2014","journal-title":"Radiother\u00a0Oncol"},{"key":"10.1016\/j.acra.2024.10.011_bib5","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.222028","article-title":"Functional MRI in neuro-oncology: state of the art and future directions","volume":"308","author":"Pasquini","year":"2023","journal-title":"Radiology"},{"key":"10.1016\/j.acra.2024.10.011_bib6","first-page":"97","article-title":"Can 3D pseudo-continuous arterial spin labeling perfusion imaging be applied to predict early response to chemoradiotherapy in patients with advanced nasopharyngeal carcinoma?","volume":"160","author":"Sun","year":"2021","journal-title":"Radiother\u00a0Oncol"},{"key":"10.1016\/j.acra.2024.10.011_bib7","doi-asserted-by":"crossref","first-page":"1320","DOI":"10.1186\/s12885-021-09063-1","article-title":"Percent change in apparent diffusion coefficient and plasma EBV DNA after induction chemotherapy identifies distinct prognostic response phenotypes in advanced nasopharyngeal carcinoma","volume":"21","author":"Liu","year":"2021","journal-title":"BMC Cancer"},{"key":"10.1016\/j.acra.2024.10.011_bib8","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1148\/radiol.2015151169","article-title":"Radiomics: images are more than pictures, they are data","volume":"278","author":"Gillies","year":"2016","journal-title":"Radiology"},{"key":"10.1016\/j.acra.2024.10.011_bib9","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","article-title":"Identifying medical diagnoses and treatable diseases by image-based deep learning","volume":"172","author":"Kermany","year":"2018","journal-title":"Cell"},{"key":"10.1016\/j.acra.2024.10.011_bib10","doi-asserted-by":"crossref","first-page":"2199","DOI":"10.1001\/jama.2017.14585","article-title":"Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer","volume":"318","author":"Ehteshami Bejnordi","year":"2017","journal-title":"JAMA"},{"key":"10.1016\/j.acra.2024.10.011_bib11","doi-asserted-by":"crossref","first-page":"40","DOI":"10.3174\/ajnr.A6365","article-title":"Deep transfer learning and radiomics feature prediction of survival of patients with high-grade gliomas","volume":"41","author":"Han","year":"2020","journal-title":"Am J Neuroradiol"},{"key":"10.1016\/j.acra.2024.10.011_bib12","first-page":"183","article-title":"Image-based deep learning model for predicting pathological response in rectal cancer using post-chemoradiotherapy magnetic resonance imaging","volume":"161","author":"Jang","year":"2021","journal-title":"Radiother\u00a0Oncol"},{"key":"10.1016\/j.acra.2024.10.011_bib13","doi-asserted-by":"crossref","first-page":"475","DOI":"10.7150\/ijbs.55716","article-title":"Application of radiomics and machine learning in head and neck cancers","volume":"17","author":"Peng","year":"2021","journal-title":"Int J Biol Sci"},{"key":"10.1016\/j.acra.2024.10.011_bib14","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1016\/j.ccell.2018.03.002","article-title":"The platelet lifeline to cancer: challenges and opportunities","volume":"33","author":"Haemmerle","year":"2018","journal-title":"Cancer Cell"},{"key":"10.1016\/j.acra.2024.10.011_bib15","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1038\/nrc.2017.51","article-title":"Microenvironmental regulation of tumour angiogenesis","volume":"17","author":"De Palma","year":"2017","journal-title":"Nat Rev Cancer"},{"key":"10.1016\/j.acra.2024.10.011_bib16","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1186\/s13244-022-01248-x","article-title":"Arterial spin labeling of nasopharyngeal carcinoma shows early therapy response","volume":"13","author":"Liu","year":"2022","journal-title":"Insights Imaging"},{"key":"10.1016\/j.acra.2024.10.011_bib17","first-page":"1179","article-title":"Intravoxel incoherent motion MRI for predicting early response to induction chemotherapy and chemoradiotherapy in patients with nasopharyngeal carcinoma","volume":"43","author":"Xiao-ping","year":"2016","journal-title":"Magn\u00a0Reson\u00a0Imaging"},{"key":"10.1016\/j.acra.2024.10.011_bib18","first-page":"1528","article-title":"Early response to chemoradiotherapy for nasopharyngeal carcinoma treatment: value of dynamic contrast-enhanced 3.0 T MRI","volume":"41","author":"Zheng","year":"2015","journal-title":"Magn\u00a0Reson\u00a0Imaging"},{"key":"10.1016\/j.acra.2024.10.011_bib19","first-page":"209","article-title":"3D-ASL perfusion correlates with VEGF expression and overall survival in glioma patients: Comparison of quantitative perfusion and pathology on accurate spatial location-matched basis","volume":"50","author":"Pang","year":"2019","journal-title":"Magn\u00a0Reson\u00a0Imaging"},{"key":"10.1016\/j.acra.2024.10.011_bib20","unstructured":"Perfusion imaging of brain gliomas using arterial spin labeling: correlation with histopathological vascular density in MRI-guided biopsies - PubMed. \u3008https:\/\/pubmed.ncbi.nlm.nih.gov\/27924360\/\u3009. Accessed 16 Mar 2024."},{"key":"10.1016\/j.acra.2024.10.011_bib21","first-page":"1140","article-title":"Can arterial spin labeling perfusion imaging be used to differentiate nasopharyngeal carcinoma from nasopharyngeal lymphoma?","volume":"53","author":"Sun","year":"2021","journal-title":"Magn\u00a0Reson\u00a0Imaging"},{"key":"10.1016\/j.acra.2024.10.011_bib22","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1186\/s13244-022-01248-x","article-title":"Arterial spin labeling of nasopharyngeal carcinoma shows early therapy response","volume":"13","author":"Liu","year":"2022","journal-title":"Insights Imaging"},{"key":"10.1016\/j.acra.2024.10.011_bib23","first-page":"23","article-title":"Diffusion MRI of cancer: from low to high b-values","volume":"49","author":"Tang","year":"2019","journal-title":"Magn\u00a0Reson\u00a0Imaging"},{"key":"10.1016\/j.acra.2024.10.011_bib24","doi-asserted-by":"crossref","first-page":"3201","DOI":"10.3390\/cancers14133201","article-title":"Machine learning based on MRI DWI radiomics features for prognostic prediction in nasopharyngeal carcinoma","volume":"14","author":"Hu","year":"2022","journal-title":"Cancers"},{"key":"10.1016\/j.acra.2024.10.011_bib25","article-title":"MRI-based clinical radiomics nomogram may predict the early response after concurrent chemoradiotherapy in locally advanced nasopharyngeal carcinoma","volume":"13","author":"Wu","year":"2023","journal-title":"Front Oncol"},{"key":"10.1016\/j.acra.2024.10.011_bib26","doi-asserted-by":"crossref","DOI":"10.1148\/ryai.2019180075","article-title":"Radiomics model to predict early progression of nonmetastatic nasopharyngeal carcinoma after intensity modulation radiation therapy: a multicenter study","volume":"1","author":"Du","year":"2019","journal-title":"Radiol Artif Intell"},{"key":"10.1016\/j.acra.2024.10.011_bib27","doi-asserted-by":"crossref","DOI":"10.1259\/bjr.20201212","article-title":"MRI-based radiomics as response predictor to radiochemotherapy for metastatic cervical lymph node in nasopharyngeal carcinoma","volume":"94","author":"Xu","year":"2021","journal-title":"BJR"},{"key":"10.1016\/j.acra.2024.10.011_bib28","article-title":"Application of stacking hybrid machine learning algorithms in delineating multi-type flooding in Bangladesh","volume":"295","author":"Rahman","year":"2021","journal-title":"J\u00a0Environ\u00a0Manag"},{"key":"10.1016\/j.acra.2024.10.011_bib29","article-title":"Stacking ensemble learning model to predict 6-month mortality in ischemic stroke patients","volume":"12","author":"Hwangbo","year":"2022","journal-title":"Sci Rep"},{"key":"10.1016\/j.acra.2024.10.011_bib30","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-022-21389-9","article-title":"A stacking ensemble machine learning model to predict alpha-1 antitrypsin deficiency-associated liver disease clinical outcomes based on UK Biobank data","volume":"12","author":"Meng","year":"2022","journal-title":"Sci Rep"}],"container-title":["Academic Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1076633224007712?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1076633224007712?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,2,27]],"date-time":"2025-02-27T18:23:57Z","timestamp":1740680637000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1076633224007712"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3]]},"references-count":30,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["S1076633224007712"],"URL":"https:\/\/doi.org\/10.1016\/j.acra.2024.10.011","relation":{},"ISSN":["1076-6332"],"issn-type":[{"value":"1076-6332","type":"print"}],"subject":[],"published":{"date-parts":[[2025,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A Stacked Multimodality Model Based on Functional MRI Features and Deep Learning Radiomics for Predicting the Early Response to Radiotherapy in Nasopharyngeal Carcinoma","name":"articletitle","label":"Article Title"},{"value":"Academic Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.acra.2024.10.011","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Association of Academic Radiology. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}]}}