Commemorating Britton Chance

Prediction of local recurrence and distant metastasis using radiomics analysis of pretreatment nasopharyngeal [18F]FDG PET/CT images

OBJECTIVES

To develop a radiomics signature to predict locoregional recurrence (LR) and distant metastasis (DM), as extracted from pretreatment 2-deoxy-2-[¹⁸F]fluoro-D-glucose ([18F]FDG) positron emission tomography/X-ray computed tomography (PET/CT) images in locally advanced nasopharyngeal carcinoma (NPC).

MATERIALS AND METHODS

Eighty-five patients with Stage III-IVB NPC underwent pretreatment [¹⁸F]FDG PET/CT scans and received radiotherapy or chemoradiotherapy. 53 of them achieved disease control, and 32 of them failed after treatment (15: LR, 17: DM). A total of 114 radiomic features were extracted from PET/CT images. For univariate analysis, Wilcoxon test and Chi-square test were used to compare median values of features between different treatment outcomes and predict the risk of treatment failure, respectively. For multivariate analysis, all features were grouped into clusters based on Pearson correlation using hierarchical clustering, and the representative feature of each cluster was chosen by the Relief algorithm. Then sequential floating forward selection (SFFS) coupled with a support vector machine (SVM) classifier were used to derive the optimized feature set in terms of the area under receiver operating characteristic (ROC) curve (AUC). The performance of the model was evaluated by leave-one-out-cross-validation, fivefold cross-validation, tenfold cross-validation.

RESULTS

Twenty features had significant differences between disease control and treatment failure. NPC patients with values of Compactness1, Compactness2, Coarseness_NGTDM or SGE_GLGLM above the median as well as patients with values of Irregularity, RLN_GLRLM or GLV_GLSZM below the median, showed a significant (p < 0.05) higher risk of treatment failure (about 50% vs. 25%). The derived radiomics signature consisted of 5 features with the highest AUC value of 0.8290 (sensitivity: 0.8438, specificity: 0.7736) using leave-one-out-cross-validation.

CONCLUSION

Locoregional recurrence (LR) and DM of locally advanced NPC can be predicted using radiomics analysis of pretreatment [¹⁸F]FDG PET/CT. The SFFS feature selection coupled with SVM classifier can derive the optimized feature set with correspondingly highest AUC value for pretreatment prediction of LR and/or DM of NPC.