HSIEN-CHUN TSENGCHAO-YU SHENPAN-FU KAOCHUN-YI CHUANGWEI-CHIH SHEN2026-10-162026-10-162025-01-01Pure ID: 15472046Pure UUID: f86487a4-84b8-4b02-9762-f72e4f6d0f8710.3390/cancers17010096https://www.scopus.com/pages/publications/85214525464https://scholars.csmu.edu.tw/handle/123456789/23290Radiotherapy is the primary and only curative treatment for nasopharyngeal carcinoma (NPC). The adoption of intensity-modulated radiotherapy (IMRT) alone or combined with optimized chemotherapeutic strategies has improved survival rates while reducing treatment-related toxicities. However, the persistent tumor status, including residual tumor presence and early recurrence, is the predominant cause of treatment failure, associated with poorer survival outcomes. This study extracted the radiomic features from pretreatment PET images, which were used to construct a prediction model to identify patients with NPC at high risk of having persistent tumors after treatment. The model trained by the AdaBoost algorithm showed significant diagnostic performance in predicting treatment failure. This model exhibited the feasibility of assisting clinicians in identifying high-risk patients before treatment, allowing for more personalized treatment plans and improving patient outcomes.enjournal article