[18F]FDG-PET/CT radiomics for the identification of genetic clusters in pheochromocytomas and paragangliomas

Objectives Based on germline and somatic mutation profiles, pheochromocytomas and paragangliomas (PPGLs) can be classified into different clusters. We investigated the use of [F-18]FDG-PET/CT radiomics, SUVmax and biochemical profile for the identification of the genetic clusters of PPGLs. Methods In this single-centre cohort, 40 PPGLs (13 cluster 1, 18 cluster 2, 9 sporadic) were delineated using a 41% adaptive threshold of SUVpeak ([F-18]FDG-PET) and manually (low-dose CT; ldCT). Using PyRadiomics, 211 radiomic features were extracted. Stratified 5-fold cross-validation for the identification of the genetic cluster was performed using multinomial logistic regression with dimensionality reduction incorporated per fold. Classification performances of biochemistry, SUVmax and PET(/CT) radiomic models were compared and presented as mean (multiclass) test AUCs over the five folds. Results were validated using a sham experiment, randomly shuffling the outcome labels. Results The model with biochemistry only could identify the genetic cluster (multiclass AUC 0.60). The three-factor PET model had the best classification performance (multiclass AUC 0.88). A simplified model with only SUVmax performed almost similarly. Addition of ldCT features and biochemistry decreased the classification performances. All sham AUCs were approximately 0.50. Conclusion PET radiomics achieves a better identification of PPGLs compared to biochemistry, SUVmax, ldCT radiomics and combined approaches, especially for the differentiation of sporadic PPGLs. Nevertheless, a model with SUVmax alone might be preferred clinically, weighing model performances against laborious radiomic analysis. The limited added value of radiomics to the overall classification performance for PPGL should be validated in a larger external cohort.

Automated summary

This study investigates the use of [F-18]FDG-PET/CT radiomics, SUVmax, and biochemical profiles for identifying the genetic clusters of PPGLs. The results show that PET radiomics performs better in identifying PPGLs compared to biochemistry, SUVmax, and CT radiomics, especially in the differentiation of sporadic PPGLs.