Purpose To investigate the predictive value of 18F-FDG PET/CT radiomics model for event-free survival(EFS)in extranodal natural killer/T-cell lymphoma(ENKTL).Materials and Methods A total of 90 patients with ENKTL who underwent 18F-FDG PET/CT examination before treatment in Henan Provincial People′s Hospital from January 2013 to January 2021 were retrospectively collected and randomly divided into training(63 cases)and validation groups(27 cases).Features were extracted from baseline PET and CT images.The least absolute shrinkage and selection operator algorithm combined with Cox survival analysis were used to select features and construct clinical model,radiomics model and clinical+radiomics composite model,the median risk score of the model was used as the cut-off value to divide the patients into high-risk group and low-risk group.C-index and receiver operating characteristic curve analysis were used to evaluate the predictive performance of the three models.The nomogram was constructed based on the optimal model and calibration curves were used to describe the consistency between the survival probability and the actual probability of the optimal model for predicting ENKTL patients,Kaplan-Meier analysis and log-rank test were used to evaluate the prognostic value of the optimal model.Results The composite model showed higher prognostic performance in the training(C-index 0.791,95%CI 0.702-0.879,area under the curve,AUC=0.882)and validation groups(C-index 0.770,95%CI 0.650-0.889,AUC=0.720)than that of the clinical and radiomics models alone.The calibration curves showed good consistency between the composite model in predicting the third year probability of EFS and the actual outcome,and the survival curves showed that the EFS of the high-risk group were significantly lower than that of the low-risk group.Conclusion Composite model based on radiomics and clinical parameters can provide more comprehensive prognostic information and improve diagnostic accuracy.The nomogram provides a non-invasive diagnostic tool for risk stratification of patients with ENKTL and facilitates individualized treatment.
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