摘要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:Thirteen radiomic features,three clinical features,and treatment option were considered for model development.The combined model showed higher prognostic performance than using either.For the merged testing set(internal and external testing sets),AUC is 0.76(combined)vs.0.56-0.80(clinical or radiomic alone)and accuracy is 0.75(combined)vs.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.