{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,5]],"date-time":"2026-11-05T21:30:17Z","timestamp":1793914217520,"version":"4.8.0"},"reference-count":38,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100004761","name":"Hainan Province Natural Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004761","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","ejradiology.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["European Journal of Radiology"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.ejrad.2026.112814","type":"journal-article","created":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T17:28:46Z","timestamp":1775150926000},"page":"112814","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["Construction of an interpretable multimodal image model for differentiating T1-stage nasopharyngeal carcinoma from benign hyperplasia"],"prefix":"10.1016","volume":"200","author":[{"given":"Kai-Jie","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tian-Cheng","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Si-Jia","family":"Zuo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fu-Jin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1606-4845","authenticated-orcid":false,"given":"Wei-Yuan","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ejrad.2026.112814_b0005","doi-asserted-by":"crossref","first-page":"3356","DOI":"10.1200\/JCO.2015.60.9347","article-title":"Management of nasopharyngeal carcinoma: current practice and future perspective","volume":"33","author":"Lee","year":"2015","journal-title":"J. Clin. Oncol."},{"key":"10.1016\/j.ejrad.2026.112814_b0010","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/S0140-6736(19)30956-0","article-title":"Nasopharyngeal carcinoma","volume":"394","author":"Chen","year":"2019","journal-title":"Lancet"},{"key":"10.1016\/j.ejrad.2026.112814_b0015","doi-asserted-by":"crossref","first-page":"385","DOI":"10.3892\/mco.2014.262","article-title":"Alternative endpoints to the 5-year overall survival and locoregional control for nasopharyngeal carcinoma: a retrospective analysis of 2,450 patients","volume":"2","author":"Chen","year":"2014","journal-title":"Mol Clin Oncol."},{"key":"10.1016\/j.ejrad.2026.112814_b0020","doi-asserted-by":"crossref","DOI":"10.1093\/annonc\/mdp150","article-title":"Nasopharyngeal cancer: ESMO Clinical Recommendations for diagnosis, treatment and follow-up","volume":"20","author":"Chan","year":"2009","journal-title":"Ann. Oncol."},{"key":"10.1016\/j.ejrad.2026.112814_b0025","doi-asserted-by":"crossref","first-page":"3403","DOI":"10.1002\/cncr.33552","article-title":"Comparison of new magnetic resonance imaging grading system with conventional endoscopy for the early detection of nasopharyngeal carcinoma","volume":"127","author":"Liu","year":"2021","journal-title":"Cancer"},{"key":"10.1016\/j.ejrad.2026.112814_b0030","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1093\/annonc\/mdz106","article-title":"Complementary roles of MRI and endoscopic examination in the early detection of nasopharyngeal carcinoma","volume":"30","author":"King","year":"2019","journal-title":"Ann. Oncol."},{"key":"10.1016\/j.ejrad.2026.112814_b0035","doi-asserted-by":"crossref","first-page":"515","DOI":"10.3174\/ajnr.A5493","article-title":"MR imaging criteria for the detection of nasopharyngeal carcinoma: discrimination of early-stage primary tumors from benign hyperplasia","volume":"39","author":"King","year":"2018","journal-title":"AJNR Am. J. Neuroradiol."},{"key":"10.1016\/j.ejrad.2026.112814_b0040","doi-asserted-by":"crossref","first-page":"515","DOI":"10.3174\/ajnr.A6444","article-title":"Early detection of cancer: evaluation of MR imaging grading systems in patients with suspected nasopharyngeal carcinoma","volume":"41","author":"King","year":"2020","journal-title":"AJNR Am. J. Neuroradiol."},{"key":"10.1016\/j.ejrad.2026.112814_b0045","doi-asserted-by":"crossref","first-page":"1751","DOI":"10.1007\/s11547-024-01904-w","article-title":"Radiomics in radiology: what the radiologist needs to know about technical aspects and clinical impact","volume":"129","author":"Ferrari","year":"2024","journal-title":"Radiol. Med."},{"key":"10.1016\/j.ejrad.2026.112814_b0050","doi-asserted-by":"crossref","first-page":"13087","DOI":"10.1038\/srep13087","article-title":"Machine learning methods for quantitative radiomic biomarkers","volume":"5","author":"Parmar","year":"2015","journal-title":"Sci. Rep."},{"key":"10.1016\/j.ejrad.2026.112814_b0055","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1136\/thorax-2023-220226","article-title":"Radiomics analysis to predict pulmonary nodule malignancy using machine learning approaches","volume":"79","author":"Warkentin","year":"2024","journal-title":"Thorax"},{"key":"10.1016\/j.ejrad.2026.112814_b0060","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1007\/s11547-021-01423-y","article-title":"Radiomics in breast MRI: current progress toward clinical application in the era of artificial intelligence","volume":"127","author":"Satake","year":"2022","journal-title":"Radiol. Med."},{"key":"10.1016\/j.ejrad.2026.112814_b0065","article-title":"Radiomics in breast cancer: current advances and future directions","volume":"5","author":"Qi","year":"2024","journal-title":"Cell Rep. Med."},{"key":"10.1016\/j.ejrad.2026.112814_b0070","doi-asserted-by":"crossref","DOI":"10.1002\/acm2.14023","article-title":"A combined radiomic model distinguishing GISTs from leiomyomas and schwannomas in the stomach based on endoscopic ultrasonography images","volume":"24","author":"Zhang","year":"2023","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"10.1016\/j.ejrad.2026.112814_b0075","doi-asserted-by":"crossref","unstructured":"Antolin A, N. Roson, R. Mast, J. Arce, R. Almodovar, R. Cortada, A. Maceda, M. Escobar, E. Trilla, J. Morote. The role of radiomics in the prediction of clinically significant prostate cancer in the PI-RADS v2 and v2.1 era: a systematic review. Cancers (Basel). 16 (2024) 2951, 10.3390\/cancers16172951.","DOI":"10.3390\/cancers16172951"},{"key":"10.1016\/j.ejrad.2026.112814_b0080","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.104813","article-title":"Interpretable prediction of 3-year all-cause mortality in patients with heart failure caused by coronary heart disease based on machine learning and SHAP","volume":"137","author":"Wang","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.ejrad.2026.112814_b0085","doi-asserted-by":"crossref","first-page":"4512","DOI":"10.1038\/s41467-022-31384-3","article-title":"Explaining a series of models by propagating Shapley values","volume":"13","author":"Chen","year":"2022","journal-title":"Nat. Commun."},{"key":"10.1016\/j.ejrad.2026.112814_b0090","doi-asserted-by":"crossref","first-page":"8984","DOI":"10.1038\/s41598-023-35795-0","article-title":"Machine learning explainability in nasopharyngeal cancer survival using LIME and SHAP","volume":"13","author":"Alabi","year":"2023","journal-title":"Sci. Rep."},{"key":"10.1016\/j.ejrad.2026.112814_b0095","unstructured":"Amin MB, S.B. Edge, F.L. Greene, D.R. Byrd, R.K. Brookland, M.K. Washington, J.E. Gershenwald, C.C. Compton, K.R. Hess, D.C. Sullivan, J.M. Jessup, J.D. Brierley, L.E. Gaspar, R.L. Schilsky, C.M. Balch, D.P. Winchester, E.A. Asare, M. Madera, D.M. Gress, L.R. Meyer. AJCC Cancer Staging Manual. Springer, Cham. 8th ed. (2017) XVII, 1032."},{"key":"10.1016\/j.ejrad.2026.112814_b0100","unstructured":"Paszke A, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, S. Chintala. PyTorch: an imperative style, high-performance deep learning library. Adv Neural Inf Process Syst. 32 (2019) 8024-8035, 10.48550\/arXiv.1912.01703."},{"key":"10.1016\/j.ejrad.2026.112814_b0105","doi-asserted-by":"crossref","unstructured":"Tanaka T. [Fundamentals] 5. Python+scikit-learn for Machine Learning in Medical Imaging. Nihon Hoshasen Gijutsu Gakkai Zasshi. 79 (2023) 1189-1193, 10.6009\/jjrt.2023-2266.","DOI":"10.6009\/jjrt.2023-2266"},{"key":"10.1016\/j.ejrad.2026.112814_b0110","doi-asserted-by":"crossref","first-page":"7895","DOI":"10.1007\/s00330-024-10834-0","article-title":"Class imbalance on medical image classification: towards better evaluation practices for discrimination and calibration performance","volume":"34","author":"Mosquera","year":"2024","journal-title":"Eur. Radiol."},{"key":"10.1016\/j.ejrad.2026.112814_b0115","doi-asserted-by":"crossref","first-page":"3417","DOI":"10.3390\/cancers16193417","article-title":"Learning from imbalanced data: integration of advanced resampling techniques and machine learning models for enhanced cancer diagnosis and prognosis","volume":"16","author":"Gurcan","year":"2024","journal-title":"Cancers (Basel)"},{"key":"10.1016\/j.ejrad.2026.112814_b0120","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1109\/TAI.2022.3160658","article-title":"On supervised class-imbalanced learning: an updated perspective and some key challenges","volume":"3","author":"Das","year":"2022","journal-title":"IEEE Trans. Artif. Intell."},{"key":"10.1016\/j.ejrad.2026.112814_b0125","article-title":"An empirical evaluation of sampling methods for the classification of imbalanced data","volume":"17","author":"Kim","year":"2022","journal-title":"PLoS One"},{"key":"10.1016\/j.ejrad.2026.112814_b0130","doi-asserted-by":"crossref","unstructured":"Tang VH, S.T.M. Duong, C.D.Tr. Nguyen, T.M. Huynh, V.T. Duc, C. Phan, H. Le, T. Bui, S.Q.H. Truong. Wavelet radiomics features from multiphase CT images for screening hepatocellular carcinoma: analysis and comparison. Sci Rep. 13 (2023) 19559, 10.1038\/s41598-023-46695-8.","DOI":"10.1038\/s41598-023-46695-8"},{"key":"10.1016\/j.ejrad.2026.112814_b0135","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1007\/s10238-024-01377-1","article-title":"The diagnostic value of multimodal imaging based on MR combined with ultrasound in benign and malignant breast diseases","volume":"24","author":"Bai","year":"2024","journal-title":"Clin. Exp. Med."},{"key":"10.1016\/j.ejrad.2026.112814_b0140","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.ejrad.2026.112814_b0145","doi-asserted-by":"crossref","first-page":"594","DOI":"10.3390\/diagnostics11040594","article-title":"Advanced imaging analysis in prostate MRI: building a radiomic signature to predict tumor aggressiveness","volume":"11","author":"Damascelli","year":"2021","journal-title":"Diagnostics (basel)."},{"key":"10.1016\/j.ejrad.2026.112814_b0150","doi-asserted-by":"crossref","first-page":"22330","DOI":"10.1038\/s41598-021-01470-5","article-title":"A wavelet features derived radiomics nomogram for prediction of malignant and benign early-stage lung nodules","volume":"11","author":"Jing","year":"2021","journal-title":"Sci. Rep."},{"key":"10.1016\/j.ejrad.2026.112814_b0155","doi-asserted-by":"crossref","first-page":"3433","DOI":"10.3390\/cancers14143433","article-title":"Radiomics for discrimination between early-stage nasopharyngeal carcinoma and benign hyperplasia with stable feature selection on MRI","volume":"14","author":"Wong","year":"2022","journal-title":"Cancers (Basel)"},{"key":"10.1016\/j.ejrad.2026.112814_b0160","doi-asserted-by":"crossref","unstructured":"Boca Petresc B, C. Caraiani, L. Popa, A. Lebovici, D.S. Feier, C. Bodale, M.M. Buruian. The utility of ADC first-order histogram features for the prediction of metachronous metastases in rectal cancer: a preliminary study. Biology (Basel). 11 (2022) 452, 10.3390\/biology11030452.","DOI":"10.3390\/biology11030452"},{"key":"10.1016\/j.ejrad.2026.112814_b0165","doi-asserted-by":"crossref","first-page":"1679","DOI":"10.1002\/jmri.27199","article-title":"MRI-based radiomics signature: a potential biomarker for identifying glypican 3-positive hepatocellular carcinoma","volume":"52","author":"Gu","year":"2020","journal-title":"J. Magn. Reson. Imaging"},{"key":"10.1016\/j.ejrad.2026.112814_b0170","doi-asserted-by":"crossref","first-page":"5627","DOI":"10.1007\/s00330-019-06133-8","article-title":"Distinguishing early-stage nasopharyngeal carcinoma from benign hyperplasia using intravoxel incoherent motion diffusion-weighted MRI","volume":"29","author":"Ai","year":"2019","journal-title":"Eur. Radiol."},{"key":"10.1016\/j.ejrad.2026.112814_b0175","doi-asserted-by":"crossref","DOI":"10.1016\/j.oraloncology.2020.104862","article-title":"Development of a self-constrained 3D DenseNet model in automatic detection and segmentation of nasopharyngeal carcinoma using magnetic resonance images","volume":"110","author":"Ke","year":"2020","journal-title":"Oral Oncol."},{"key":"10.1016\/j.ejrad.2026.112814_b0180","doi-asserted-by":"crossref","first-page":"3856","DOI":"10.1007\/s00330-020-07451-y","article-title":"Convolutional neural network for discriminating nasopharyngeal carcinoma and benign hyperplasia on MRI","volume":"31","author":"Wong","year":"2021","journal-title":"Eur. Radiol."},{"key":"10.1016\/j.ejrad.2026.112814_b0185","doi-asserted-by":"crossref","first-page":"1835","DOI":"10.3390\/diagnostics14161835","article-title":"Enhancing the clinical utility of radiomics: addressing the challenges of repeatability and reproducibility in CT and MRI","volume":"14","author":"Teng","year":"2024","journal-title":"Diagnostics (basel)."},{"key":"10.1016\/j.ejrad.2026.112814_b0190","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1148\/radiol.211597","article-title":"Radiomic analysis: study design, statistical analysis, and other bias mitigation strategies","volume":"304","author":"Moskowitz","year":"2022","journal-title":"Radiology"}],"container-title":["European Journal of Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0720048X26001622?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0720048X26001622?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T00:39:55Z","timestamp":1780101595000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0720048X26001622"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":38,"alternative-id":["S0720048X26001622"],"URL":"https:\/\/doi.org\/10.1016\/j.ejrad.2026.112814","relation":{},"ISSN":["0720-048X"],"issn-type":[{"value":"0720-048X","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Construction of an interpretable multimodal image model for differentiating T1-stage nasopharyngeal carcinoma from benign hyperplasia","name":"articletitle","label":"Article Title"},{"value":"European Journal of Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ejrad.2026.112814","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"112814"}}