Dual tracer 68Ga-DOTATOC and 18F-FDG PET/computed tomography radiomics in pancreatic neuroendocrine neoplasms: an endearing tool for preoperative risk assessment

Aim To explore the potentiality of radiomics analysis, performed on(68)Ga-DOTATOC and fluorine-18-fluorodeoxyglucose (F-18-FDG) PET/computed tomography (CT) images, in predicting tumour aggressiveness and outcome in patients candidate to surgery for pancreatic neuroendocrine neoplasms (PanNENs). Patients and methods Retrospective study including 61 patients who underwent(68)Ga-DOTATOC and(18)F-FDG PET/CT before surgery for PanNEN. Semiquantitative variables [SUVmax and somatostatin receptor density (SRD) for(68)Ga-DOTATOC PET; SUVmax and MTV for(18)F-FDG PET] and texture features [intensity variability, size zone variability (SZV), zone percentage, entropy; homogeneity, dissimilarity and coefficient of variation (Co-V)] have been analysed to evaluate their possible role in predicting tumour characteristics. Principal component analysis (PCA) was firstly performed and then multiple regression analyses were performed by using the extracted principal components. Results Regarding(68)Ga-DOTATOC PET, SZV, entropy, intensity variability and SRD were predictive for tumour dimension. Regarding(18)F-FDG PET, intensity variability, SZV, homogeneity, SUVmax and MTV were predictive for tumour dimension. Four principal components were extracted from PCA: PC1 correlated with all(18)F-FDG variables, while PC2, PC3 and PC4 with(68)Ga-DOTATOC variables. PC1 was the only significantly predicting angioinvasion (P = 0.0222); PC4 was the only one significantly predicting lymph nodal involvement (P = 0.0151). All principal components except PC4 significantly predicted tumour dimension (P <0.0001 for PC1,P = 0.0016 for PC2 andP < 0.0001 for PC3). Co-V from(68)Ga-DOTATOC PET/CT was predictive of the outcome. Conclusion Specific texture features derived from preoperative(68)Ga-DOTATOC and(18)F-FDG PET/CT could noninvasively predict specific tumour characteristics and patients' outcome, delineating the potential role of dual tracer technique and texture analysis in the risk assessment of patients with PanNENs.