{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T06:37:10Z","timestamp":1772779030347,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T00:00:00Z","timestamp":1761782400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science and Technology Council","award":["113-2221-E-002-167"],"award-info":[{"award-number":["113-2221-E-002-167"]}]},{"name":"National Science and Technology Council","award":["112-2634-F-002-003"],"award-info":[{"award-number":["112-2634-F-002-003"]}]},{"DOI":"10.13039\/501100004391","name":"China Medical University Hospital","doi-asserted-by":"crossref","award":["DMR-114-001"],"award-info":[{"award-number":["DMR-114-001"]}],"id":[{"id":"10.13039\/501100004391","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004391","name":"China Medical University Hospital","doi-asserted-by":"crossref","award":["DMR-114-006"],"award-info":[{"award-number":["DMR-114-006"]}],"id":[{"id":"10.13039\/501100004391","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Background\/Objectives: The implementation of adaptive radiation therapy (ART) is increasingly becoming widely available in the clinical practice of radiotherapy (RT). For patients with pharyngeal cancer receiving RT, this study aimed to develop a deep learning (DL) model by merging baseline and ART simulation computed tomography (CT) images to predict treatment outcomes. Methods: Clinical and imaging data from 162 patients of newly diagnosed oropharyngeal or hypopharyngeal cancer were analyzed. All completed definitive treatment and their baseline and ART non-contrast simulation CTs were utilized for training. After augmentation of the CT images, a deep contrastive learning model was employed to predict the occurrence of local recurrence (LR), neck lymph node relapse (NR), and distant metastases (DM). Receiver operating characteristic curve analysis was conducted to evaluate the model\u2019s performance. Results: Over a median follow-up period of 34 months, 53 (32.7%), 36 (22.2%), and 23 (14.0%) patients developed LR, NR, and DM, respectively. Following the integration of prediction results from baseline and ART simulation CTs, the area under the curve for predicting the occurrence of LR, NR, and DM reached 0.773, 0.747, and 0.793. At the same time, the accuracy for the three endpoints was 72.4%, 74.7%, and 75.7%, respectively. Conclusions: For patients with pharyngeal cancer ready to receive RT-based treatment, our proposed models can predict the development of LR, NR, or DM through baseline and ART simulation CTs. External validation needs to be conducted to confirm the model\u2019s performance.<\/jats:p>","DOI":"10.3390\/cancers17213492","type":"journal-article","created":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T03:33:07Z","timestamp":1761881587000},"page":"3492","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Predicting Radiotherapy Outcomes with Deep Learning Models Through Baseline and Adaptive Simulation Computed Tomography in Patients with Pharyngeal Cancer"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9292-2819","authenticated-orcid":false,"given":"Kuo-Chen","family":"Wu","sequence":"first","affiliation":[{"name":"Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan"},{"name":"Artificial Intelligence and Robotics Innovation Center, China Medical University Hospital, Taichung 404327, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shang-Wen","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Medicine, China Medical University, Taichung 40402, Taiwan"},{"name":"School of Medicine, Taipei Medical University, Taipei 11031, Taiwan"},{"name":"Department of Radiation Oncology, China Medical University Hospital, Taichung 404327, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan-Yen","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Taichung University of Science and Technology, Taichung 404336, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yao-Ching","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, China Medical University Hospital, Taichung 404327, Taiwan"},{"name":"Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung 40402, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying-Chun","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Medicine, China Medical University, Taichung 40402, Taiwan"},{"name":"Department of Radiation Oncology, China Medical University Hospital, Taichung 404327, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao-Jen","family":"Chang","sequence":"additional","affiliation":[{"name":"Artificial Intelligence and Robotics Innovation Center, China Medical University Hospital, Taichung 404327, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zong-Kai","family":"Hsu","sequence":"additional","affiliation":[{"name":"Artificial Intelligence and Robotics Innovation Center, China Medical University Hospital, Taichung 404327, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruey-Feng","family":"Chang","sequence":"additional","affiliation":[{"name":"Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan"},{"name":"Artificial Intelligence and Robotics Innovation Center, China Medical University Hospital, Taichung 404327, Taiwan"},{"name":"Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chia-Hung","family":"Kao","sequence":"additional","affiliation":[{"name":"Artificial Intelligence and Robotics Innovation Center, China Medical University Hospital, Taichung 404327, Taiwan"},{"name":"Graduate Institute of Biomedical Sciences, School of Medicine, College of Medicine, China Medical University, Taichung 40402, Taiwan"},{"name":"Department of Bioinformatics and Medical Engineering, Asia University, Taichung 413305, Taiwan"},{"name":"Department of Nuclear Medicine and PET Center, China Medical University Hospital, Taichung 404327, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1054","DOI":"10.1016\/j.ijrobp.2020.10.021","article-title":"Adaptive Radiation Therapy (ART) Strategies and Technical Considerations: A State of the ART Review From NRG Oncology","volume":"109","author":"Lee","year":"2021","journal-title":"Int. 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