{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T17:06:19Z","timestamp":1784739979708,"version":"3.55.0"},"reference-count":39,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100022294","name":"National Taiwan University Hospital Yunlin Branch","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100022294","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005762","name":"National Taiwan University Hospital","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005762","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers in Biology and Medicine"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.compbiomed.2026.111799","type":"journal-article","created":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T04:40:02Z","timestamp":1780634402000},"page":"111799","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Multimodal deep learning integration of clinical, dosiomics, and radiomics features to predict toxicity in breast cancer patients undergoing hypofractionated radiotherapy"],"prefix":"10.1016","volume":"213","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8154-850X","authenticated-orcid":false,"given":"Yen-Ting","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"You-Cheng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yung-Chieh","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kuan-An","family":"Chu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shih-Ting","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hsiang-Kuang","family":"Tony Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5175-6735","authenticated-orcid":false,"given":"Ting-An","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.compbiomed.2026.111799_bib1","doi-asserted-by":"crossref","first-page":"1495","DOI":"10.1158\/1055-9965.EPI-15-0535","article-title":"International variation in female breast cancer incidence and mortality rates","volume":"24","author":"DeSantis","year":"2015","journal-title":"Cancer Epidemiol. Biomarkers Prev."},{"key":"10.1016\/j.compbiomed.2026.111799_bib2","doi-asserted-by":"crossref","first-page":"1707","DOI":"10.1016\/S0140-6736(11)61629-2","article-title":"Effect of radiotherapy after breast-conserving surgery on 10-year recurrence and 15-year breast cancer death: meta-analysis of individual patient data for 10 801 women in 17 randomised trials","volume":"378","author":"Group","year":"2011","journal-title":"Lancet"},{"key":"10.1016\/j.compbiomed.2026.111799_bib3","doi-asserted-by":"crossref","first-page":"1086","DOI":"10.1016\/S1470-2045(13)70386-3","article-title":"The UK standardisation of breast radiotherapy (START) trials of radiotherapy hypofractionation for treatment of early breast cancer: 10-year follow-up results of two randomised controlled trials","volume":"14","author":"Haviland","year":"2013","journal-title":"Lancet Oncol."},{"key":"10.1016\/j.compbiomed.2026.111799_bib4","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1056\/NEJMoa0906260","article-title":"Long-term results of hypofractionated radiation therapy for breast cancer","volume":"362","author":"Whelan","year":"2010","journal-title":"N. Engl. J. Med."},{"key":"10.1016\/j.compbiomed.2026.111799_bib5","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1016\/S1470-2045(06)70699-4","article-title":"Effect of radiotherapy fraction size on tumour control in patients with early-stage breast cancer after local tumour excision: long-term results of a randomised trial","volume":"7","author":"Owen","year":"2006","journal-title":"Lancet Oncol."},{"key":"10.1016\/j.compbiomed.2026.111799_bib6","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.breast.2021.04.002","article-title":"Cost-effectiveness of postmastectomy hypofractionated radiation therapy vs conventional fractionated radiation therapy for high-risk breast cancer","volume":"58","author":"Yang","year":"2021","journal-title":"Breast"},{"key":"10.1016\/j.compbiomed.2026.111799_bib7","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.ad.2019.05.009","article-title":"Using the common terminology criteria for adverse events (CTCAE\u2013version 5.0) to evaluate the severity of adverse events of anticancer therapies","volume":"112","author":"Freites-Martinez","year":"2021","journal-title":"Actas Dermosifiliogr."},{"key":"10.1016\/j.compbiomed.2026.111799_bib8","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1159\/000087909","article-title":"The effect of treatment interruptions in the postoperative irradiation of breast cancer","volume":"69","author":"Bese","year":"2005","journal-title":"Oncology"},{"key":"10.1016\/j.compbiomed.2026.111799_bib9","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.1093\/jnci\/djad127","article-title":"Effect of treatment interruptions on overall survival in patients with triple-negative breast cancer","volume":"115","author":"Chow","year":"2023","journal-title":"J. Natl. Cancer Inst.: J. Natl. Cancer Inst."},{"key":"10.1016\/j.compbiomed.2026.111799_bib10","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1016\/j.ejso.2014.12.002","article-title":"Effect of cosmetic outcome on quality of life after breast cancer surgery","volume":"41","author":"Kim","year":"2015","journal-title":"Eur. J. Surg. Oncol."},{"key":"10.1016\/j.compbiomed.2026.111799_bib11","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1186\/s13244-020-00887-2","article-title":"Radiomics in medical imaging\u2014\u201chow-to\u201d guide and critical reflection","volume":"11","author":"Van Timmeren","year":"2020","journal-title":"Insights Imaging"},{"key":"10.1016\/j.compbiomed.2026.111799_bib12","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.canrad.2023.05.001","article-title":"Prediction of toxicity outcomes following radiotherapy using deep learning-based models: a systematic review","volume":"27","author":"Tan","year":"2023","journal-title":"Cancer Radiother."},{"key":"10.1016\/j.compbiomed.2026.111799_bib13","doi-asserted-by":"crossref","first-page":"790","DOI":"10.3389\/fonc.2020.00790","article-title":"Machine learning-based models for prediction of toxicity outcomes in radiotherapy","volume":"10","author":"Isaksson","year":"2020","journal-title":"Front. Oncol."},{"key":"10.1016\/j.compbiomed.2026.111799_bib14","doi-asserted-by":"crossref","first-page":"2859","DOI":"10.1118\/1.3582947","article-title":"Use of machine learning methods for prediction of acute toxicity in organs at risk following prostate radiotherapy","volume":"38","author":"Pella","year":"2011","journal-title":"Med. Phys."},{"key":"10.1016\/j.compbiomed.2026.111799_bib15","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1186\/s13014-022-02154-8","article-title":"Radiation pneumonitis prediction after stereotactic body radiation therapy based on 3D dose distribution: dosiomics and\/or deep learning-based radiomics features","volume":"17","author":"Huang","year":"2022","journal-title":"Radiat. Oncol."},{"key":"10.1016\/j.compbiomed.2026.111799_bib16","doi-asserted-by":"crossref","first-page":"35","DOI":"10.3389\/fonc.2018.00035","article-title":"Design and selection of machine learning methods using radiomics and dosiomics for normal tissue complication probability modeling of xerostomia","volume":"8","author":"Gabrys","year":"2018","journal-title":"Front. Oncol."},{"key":"10.1016\/j.compbiomed.2026.111799_bib17","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejso.2024.108450","article-title":"A nomogram based on pretreatment radiomics and dosiomics features for predicting overall survival associated with esophageal squamous cell cancer","author":"Kawahara","year":"2024","journal-title":"Eur. J. Surg. Oncol."},{"key":"10.1016\/j.compbiomed.2026.111799_bib18","doi-asserted-by":"crossref","DOI":"10.1016\/j.oraloncology.2024.107000","article-title":"Xerostomia prediction in patients with nasopharyngeal carcinoma during radiotherapy using segmental dose distribution in dosiomics and radiomics models","volume":"158","author":"Zhang","year":"2024","journal-title":"Oral Oncol."},{"key":"10.1016\/j.compbiomed.2026.111799_bib19","doi-asserted-by":"crossref","first-page":"S1","DOI":"10.1016\/j.ijrobp.2022.07.2320","article-title":"Nrg RTOG 1005: a phase III trial of hypo fractionated whole breast irradiation with concurrent boost vs. conventional whole breast irradiation plus sequential boost following lumpectomy for high risk early-stage breast cancer","volume":"114","author":"Vicini","year":"2022","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"10.1016\/j.compbiomed.2026.111799_bib20","article-title":"The toxicity of hybrid techniques combining hypofractionated whole breast radiotherapy with concomitant tumor bed boost in patients with breast cancer","author":"Liu","year":"2025","journal-title":"J. Formos. Med. Assoc."},{"key":"10.1016\/j.compbiomed.2026.111799_bib21","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.1016\/j.mri.2012.05.001","article-title":"3D slicer as an image computing platform for the quantitative imaging network","volume":"30","author":"Fedorov","year":"2012","journal-title":"Magn. Reson. Imaging"},{"key":"10.1016\/j.compbiomed.2026.111799_bib22","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1148\/radiol.2020191145","article-title":"The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping","volume":"295","author":"Zwanenburg","year":"2020","journal-title":"Radiology"},{"key":"10.1016\/j.compbiomed.2026.111799_bib23","doi-asserted-by":"crossref","first-page":"80151","DOI":"10.1109\/ACCESS.2022.3165792","article-title":"Efficient medical diagnosis of human heart diseases using machine learning techniques with and without GridSearchCV","volume":"10","author":"Ahmad","year":"2022","journal-title":"IEEE Access"},{"key":"10.1016\/j.compbiomed.2026.111799_bib24","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1186\/s13014-022-02043-0","article-title":"Transparency in quality of radiotherapy for breast cancer in the Netherlands: a national registration of radiotherapy-parameters","volume":"17","author":"Maliko","year":"2022","journal-title":"Radiat. Oncol."},{"key":"10.1016\/j.compbiomed.2026.111799_bib25","doi-asserted-by":"crossref","first-page":"e1945","DOI":"10.1016\/S2214-109X(24)00355-3","article-title":"Global radiotherapy demands and corresponding radiotherapy-professional workforce requirements in 2022 and predicted to 2050: a population-based study","volume":"12","author":"Zhu","year":"2024","journal-title":"Lancet Global Health"},{"key":"10.1016\/j.compbiomed.2026.111799_bib26","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1001\/jamaoncol.2023.4837","article-title":"Global stage distribution of breast cancer at diagnosis: a systematic review and meta-analysis","volume":"10","author":"Fuentes","year":"2024","journal-title":"JAMA Oncol."},{"key":"10.1016\/j.compbiomed.2026.111799_bib27","doi-asserted-by":"crossref","DOI":"10.1016\/j.eclinm.2023.101886","article-title":"MASCC clinical practice guidelines for the prevention and management of acute radiation dermatitis: part 1) systematic review","volume":"58","author":"Behroozian","year":"2023","journal-title":"EClinicalMedicine"},{"key":"10.1016\/j.compbiomed.2026.111799_bib28","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.radmp.2020.02.004","article-title":"Prevention and treatment for radiation-induced skin injury during radiotherapy","volume":"1","author":"Wang","year":"2020","journal-title":"Radiation Medicine and Protection"},{"key":"10.1016\/j.compbiomed.2026.111799_bib29","article-title":"Interventions for radiation-induced fibrosis in patients with breast cancer: systematic review and meta-analyses","volume":"7","author":"Nogueira","year":"2022","journal-title":"Adv. Radiat. Oncol."},{"key":"10.1016\/j.compbiomed.2026.111799_bib30","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1186\/s13014-025-02695-8","article-title":"A stacking ensemble framework integrating radiomics and deep learning for prognostic prediction in head and neck cancer","volume":"20","author":"Wang","year":"2025","journal-title":"Radiat. Oncol."},{"key":"10.1016\/j.compbiomed.2026.111799_bib31","doi-asserted-by":"crossref","first-page":"1669","DOI":"10.1080\/09553002.2023.2214206","article-title":"Radiomics and dosiomics-based prediction of radiotherapy-induced xerostomia in head and neck cancer patients","volume":"99","author":"Abdollahi","year":"2023","journal-title":"Int. J. Radiat. Biol."},{"key":"10.1016\/j.compbiomed.2026.111799_bib33","first-page":"1","article-title":"Radiomics and dosiomics approaches to estimate lung function after stereotactic body radiation therapy in patients with lung tumors","author":"Ieko","year":"2025","journal-title":"Radiol. Phys. Technol."},{"key":"10.1016\/j.compbiomed.2026.111799_bib34","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1186\/s12885-024-12753-1","article-title":"Utilizing radiomics and dosiomics with AI for precision prediction of radiation dermatitis in breast cancer patients","volume":"24","author":"Lee","year":"2024","journal-title":"BMC Cancer"},{"key":"10.1016\/j.compbiomed.2026.111799_bib35","doi-asserted-by":"crossref","first-page":"3767","DOI":"10.3390\/cancers17233767","article-title":"Dose-guided hybrid AI model with deep and handcrafted radiomics for explainable radiation dermatitis prediction in breast cancer VMAT","volume":"17","author":"Lee","year":"2025","journal-title":"Cancers"},{"key":"10.1016\/j.compbiomed.2026.111799_bib36","doi-asserted-by":"crossref","DOI":"10.1016\/j.radonc.2025.110709","article-title":"Normal tissue complication probability model for acute oral mucositis in patients with head and neck cancer undergoing carbon ion radiation therapy based on dosimetry, radiomics, and dosiomics","volume":"204","author":"Meng","year":"2025","journal-title":"Radiother. Oncol."},{"key":"10.1016\/j.compbiomed.2026.111799_bib37","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.231319","article-title":"The image biomarker standardization initiative: standardized convolutional filters for reproducible radiomics and enhanced clinical insights","volume":"310","author":"Whybra","year":"2024","journal-title":"Radiology"},{"key":"10.1016\/j.compbiomed.2026.111799_bib38","doi-asserted-by":"crossref","DOI":"10.1177\/15330338261424144","article-title":"Region-specific multi-omics modeling for predicting acute radiation-induced proctitis in cervical cancer radiotherapy: a retrospective analysis","volume":"25","author":"Xiao","year":"2026","journal-title":"Technol. Cancer Res. Treat."},{"key":"10.1016\/j.compbiomed.2026.111799_bib39","first-page":"4783","article-title":"Radiation-induced skin and heart toxicity in patients with breast cancer treated with adjuvant proton radiotherapy: a comparison with photon radiotherapy","volume":"13","author":"Hsieh","year":"2023","journal-title":"Am. J. Cancer Res."},{"key":"10.1016\/j.compbiomed.2026.111799_bib40","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-025-95185-6","article-title":"Prediction of acute skin toxicity in tomotherapy of breast cancer using skin DVH data","volume":"15","author":"Saadatmand","year":"2025","journal-title":"Sci. Rep."}],"container-title":["Computers in Biology and Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S001048252600363X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S001048252600363X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T16:48:40Z","timestamp":1784738920000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S001048252600363X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":39,"alternative-id":["S001048252600363X"],"URL":"https:\/\/doi.org\/10.1016\/j.compbiomed.2026.111799","relation":{},"ISSN":["0010-4825"],"issn-type":[{"value":"0010-4825","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Multimodal deep learning integration of clinical, dosiomics, and radiomics features to predict toxicity in breast cancer patients undergoing hypofractionated radiotherapy","name":"articletitle","label":"Article Title"},{"value":"Computers in Biology and Medicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compbiomed.2026.111799","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"111799"}}