Predicting Radiotherapy Outcomes with Deep Learning Models Through Baseline and Adaptive Simulation Computed Tomography in Patients with Pharyngeal Cancer

Wu, Kuo-ChenKuo-ChenWuChen, Shang-WenShang-WenChenChang, Yuan-YenYuan-YenChangWang, Yao-ChingYao-ChingWangLin, Ying-ChunYing-ChunLinChang, Chao-JenChao-JenChangHsu, Zong-KaiZong-KaiHsuChang, Ruey-FengRuey-FengChangKao, Chia-HungChia-HungKao2025-12-182025-12-182025-10-30https://www.scopus.com/record/display.uri?eid=2-s2.0-105021514186&origin=resultslisthttps://scholars.lib.ntu.edu.tw/handle/123456789/734775Background/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’s 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’s performance.trueadaptive radiotherapycomputed tomographydeep contrastive learningpharyngeal cancertreatment outcome[SDGs]SDG3Predicting Radiotherapy Outcomes with Deep Learning Models Through Baseline and Adaptive Simulation Computed Tomography in Patients with Pharyngeal Cancerjournal article10.3390/cancers172134922-s2.0-105021514186