{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T20:03:05Z","timestamp":1780603385261,"version":"3.54.1"},"reference-count":36,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Technol Cancer Res Treat"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Induction chemotherapy (IC) represents a standard treatment approach for locally advanced nasopharyngeal carcinoma (LA-NPC), yet marked interpatient heterogeneity in treatment response persists. This study sought to develop and evaluate a temporal Transformer\u2013based fusion model integrating baseline pretreatment and early intratreatment MRI to facilitate early risk stratification and inform individualized therapeutic management.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>In this retrospective multicenter study, 488 patients with pathologically confirmed LA-NPC were enrolled from two institutions. All patients underwent induction chemotherapy and received contrast-enhanced T1-weighted imaging (CE-T1WI) before treatment initiation (Pre-IC) and at an early post-treatment time point (Post-IC). A dual-branch independent network architecture with Twins-SVT as the backbone was implemented to separately extract deep learning features from Pre-IC and Post-IC CE-T1WI images. Subsequently, an attention-based temporal Transformer fusion module was designed to model nonlinear longitudinal interactions and dynamic evolutionary patterns between pre- and post-treatment tumor representations, yielding a longitudinal temporal fusion predictive model. Gradient-weighted class activation mapping (Grad-CAM) was applied to enhance interpretability through visualization of salient imaging regions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The deep learning model based on Pre-IC imaging yielded an AUC of 0.844 (95% CI: 0.767\u20130.922) in the internal validation cohort and 0.819 (95% CI: 0.725\u20130.913) in the external validation cohort. The Post-IC\u2013based model demonstrated AUCs of 0.863 (95% CI: 0.790\u20130.936) and 0.838 (95% CI: 0.753\u20130.923) in the internal and external validation cohorts, respectively. The longitudinal temporal Transformer fusion model achieved higher predictive performance, with AUCs increasing to 0.889 (95% CI: 0.824\u20130.955) in the internal validation cohort and 0.865 (95% CI: 0.791\u20130.939) in the external validation cohort.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Compared with single\u2013time point models, a longitudinal contrast-enhanced MRI\u2013based temporal Transformer fusion model enabled more accurate early individualized prediction of induction chemotherapy response in patients with locally advanced nasopharyngeal carcinoma.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/15330338261457511","type":"journal-article","created":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T17:16:04Z","timestamp":1780334164000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Longitudinal MRI Temporal Transformer Fusion Model for Predicting Induction Chemotherapy Efficacy in Locally Advanced Nasopharyngeal Carcinoma"],"prefix":"10.1177","volume":"25","author":[{"given":"Lei","family":"Han","sequence":"first","affiliation":[{"name":"The Affiliated Huai\u2019an Hospital of Xuzhou Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyu","family":"Chen","sequence":"additional","affiliation":[{"name":"The Affiliated Huai\u2019an Hospital of Xuzhou Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengyu","family":"Zhao","sequence":"additional","affiliation":[{"name":"The Affiliated Huai\u2019an Hospital of Xuzhou Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Liu","sequence":"additional","affiliation":[{"name":"The Affiliated Huai\u2019an Hospital of Xuzhou Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongyang","family":"Yu","sequence":"additional","affiliation":[{"name":"The Affiliated Huai\u2019an Hospital of Xuzhou Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0372-0256","authenticated-orcid":false,"given":"Hui","family":"Zhang","sequence":"additional","affiliation":[{"name":"The Affiliated Huaian NO.1 People\u2019s Hospital of Nanjing Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0259-5538","authenticated-orcid":false,"given":"Fang","family":"Ge","sequence":"additional","affiliation":[{"name":"The Affiliated Huai\u2019an Hospital of Xuzhou Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,6,1]]},"reference":[{"key":"e_1_3_8_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(19)30956-0"},{"key":"e_1_3_8_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(24)01438-7"},{"key":"e_1_3_8_4_2","doi-asserted-by":"publisher","DOI":"10.3390\/cancers15061689"},{"key":"e_1_3_8_5_2","doi-asserted-by":"publisher","DOI":"10.21037\/qims-24-1642"},{"key":"e_1_3_8_6_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-026-71019-5"},{"key":"e_1_3_8_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tranon.2022.101412"},{"key":"e_1_3_8_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ceb.2024.102422"},{"key":"e_1_3_8_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ccell.2024.07.001"},{"key":"e_1_3_8_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2021.109744"},{"key":"e_1_3_8_11_2","doi-asserted-by":"publisher","DOI":"10.34133\/research.0749"},{"key":"e_1_3_8_12_2","doi-asserted-by":"publisher","DOI":"10.1186\/s40644-022-00448-4"},{"key":"e_1_3_8_13_2","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2023.1192953"},{"key":"e_1_3_8_14_2","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.774455"},{"key":"e_1_3_8_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.106785"},{"key":"e_1_3_8_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2022.09.019"},{"key":"e_1_3_8_17_2","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ad00fe"},{"key":"e_1_3_8_18_2","doi-asserted-by":"publisher","DOI":"10.1002\/mp.17775"},{"key":"e_1_3_8_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eclinm.2025.103298"},{"key":"e_1_3_8_20_2","first-page":"e078378","article-title":"TRIPOD+ AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins GS","year":"2024","unstructured":"Collins GS, Moons KGM, Dhiman P, et al. 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