Integrated clinical-radiomic model for predicting treatment response of concurrent chemo-radiotherapy and radiotherapy alone in controversial subgroup of AJCC/UICC ninth edition stage I nasopharyngeal cancer
Year: 2025
Type: article
Study design: Observational Study
Abstract: Objective: Radiotherapy (RT) is the definitive treatment for stage II nasopharyngeal carcinoma (NPC), which is classified as stages IA and IB in the latest ninth edition of American Joint Committee on Cancer (AJCC)/Union for International Cancer Control (UICC). A crucial question is whether concurrent chemo-radiotherapy (CCRT) could derive additional benefits to this recent "down-staging" subgroup of NPC patients. This study aimed to interrogate clinical and radiomic features for predicting 5-year progression-free survival (PFS) of stage II NPC treated with RT alone or CCRT. Methods: Imaging and clinical data of 166 stage II NPC (eighth edition AJCC/UICC) patients were collected. Data were allocated into training, internal testing, and external testing sets. For each case, 851 radiomic features were extracted and 10 clinical features were collected. Radiomic and clinical features most associated with the 5-year PFS were selected separately. A combined model was developed using multivariate logistic regression by integrating selected features and treatment option to predict 5-year PFS. Model performances were evaluated by area under the receiver operating curve (AUC), prediction accuracy, and decision curve analysis. Survival analyses including Kaplan-Meier analysis and Cox regression model were performed for further analysis. Results: . 0.62-0.73 (clinical or radiomic alone). Kaplan-Meier analysis using the combined model showed significant discrimination in PFS of the predicted low-risk and high-risk groups in the training and internal testing cohorts (P<0.05). Conclusions: Integrating with clinical and radiomic features could provide prognostic information on 5-year PFS under either treatment regimen, guiding individualized decisions of chemotherapy based on the predicted treatment outcome.
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4410313505