{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T15:05:10Z","timestamp":1789052710753,"version":"build-2803163510"},"reference-count":35,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"the Projects for Research and Development of Medical and Health Appropriate Technology of Guangxi Zhuang Autonomous Region","award":["No. S2018087"],"award-info":[{"award-number":["No. S2018087"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Technol Cancer Res Treat"],"published-print":{"date-parts":[[2023,1]]},"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To investigate the capability of an Magnetic resonance imaging (MRI) radiomics model based on pretreatment texture features in predicting the short-term efficacy of recombinant human endostatin (RHES) plus concurrent chemoradiotherapy (CCRT) for nasopharyngeal carcinoma (NPC).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We retrospectively enrolled 65 patients newly diagnosed as having NPC and treated with RHES\u2009+\u2009CCRT. A total of 144 texture features were extracted from the MRI before RHES\u2009+\u2009CCRT treatment of all the NPC patients. The maximum relevance minimum redundancy (mRMR) method was used to remove redundant, irrelevant texture features, and calculate the Rad score of the primary tumor. Multivariable logistic regression was used to select the most predictive features subset, and prediction models were constructed. The performance of the 3 models in predicting the early response of RHES\u2009+\u2009CCRT for NPC was explored.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The diagnostic efficiency of combined model and radiomics model in distinguishing between the effective and the ineffective groups of patients was found to be moderate. The area under the ROC curve (AUC) of the combined model and radiomics model was 0.74 (95% confidence interval [CI]: 0.62-0.86) and 0.71 (95% CI: 0.58-0.84), respectively, with both being higher than the AUC of the clinics model (0.63, 95% CI: 0.49-0.78). Compared with the radiomics model, the combined model showed marginally improved diagnostic performance in predicting RHES\u2009+\u2009CCRT treatment response. The accuracy of combined model and radiomics model for RHES\u2009+\u2009CCRT response assessment in NPC were higher than those of the clinics model (0.723, 0.723 vs 0.677).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The pretreatment MRI-based radiomics may be a noninvasive and effective method for the prediction of RHES\u2009+\u2009CCRT early response in patients with NPC.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/15330338231160619","type":"journal-article","created":{"date-parts":[[2023,4,24]],"date-time":"2023-04-24T15:02:25Z","timestamp":1682348545000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Performance of Pretreatment MRI-Based Radiomics in Recombinant Human Endostatin Plus Concurrent Chemoradiotherapy Response Prediction in Nasopharyngeal Carcinoma: A Retrospective Study"],"prefix":"10.1177","volume":"22","author":[{"given":"Lixuan","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zongxiang","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Guangxi Tumor Radiation Therapy Clinical Medical Research Center, Nanning, Guangxi Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Ren","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muliang","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yao","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingjun","family":"Shen","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Guangxi Tumor Radiation Therapy Clinical Medical Research Center, Nanning, Guangxi Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huashan","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Diagnosis, GE Healthcare, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3369-8532","authenticated-orcid":false,"given":"Liling","family":"Long","sequence":"additional","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Province, China"},{"name":"Key Laboratory of Early Prevention and Treatment for Regional High-Frequency Tumor, Guangxi Medical University, Ministry of Education, Nanning, Guangxi Province, China"},{"name":"Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, Nanning, Guangxi Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2023,4,24]]},"reference":[{"key":"bibr1-15330338231160619","doi-asserted-by":"publisher","DOI":"10.1016\/S2214-109X(19)30488-7"},{"key":"bibr2-15330338231160619","doi-asserted-by":"publisher","DOI":"10.6004\/jnccn.2020.0031"},{"key":"bibr3-15330338231160619","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(19)30956-0"},{"issue":"12","key":"bibr4-15330338231160619","doi-asserted-by":"crossref","first-page":"2230","DOI":"10.1002\/cncr.28049","volume":"119","author":"Chen 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