AIM: To explore the potentiality of radiomics analysis, performed on Ga-DOTATOC and fluorine-18-fluorodeoxyglucose (F-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 Ga-DOTATOC and F-FDG PET/CT before surgery for PanNEN. Semiquantitative variables [SUVmax and somatostatin receptor density (SRD) for Ga-DOTATOC PET; SUVmax and MTV for 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 Ga-DOTATOC PET, SZV, entropy, intensity variability and SRD were predictive for tumour dimension. Regarding 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 F-FDG variables, while PC2, PC3 and PC4 with 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 and P < 0.0001 for PC3). Co-V from Ga-DOTATOC PET/CT was predictive of the outcome. CONCLUSION: Specific texture features derived from preoperative Ga-DOTATOC and 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.
AIM: To explore the potentiality of radiomics analysis, performed on Ga-DOTATOC and fluorine-18-fluorodeoxyglucose (F-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 Ga-DOTATOC and F-FDG PET/CT before surgery for PanNEN. Semiquantitative variables [SUVmax and somatostatin receptor density (SRD) for Ga-DOTATOC PET; SUVmax and MTV for 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 Ga-DOTATOC PET, SZV, entropy, intensity variability and SRD were predictive for tumour dimension. Regarding 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 F-FDG variables, while PC2, PC3 and PC4 with 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 and P < 0.0001 for PC3). Co-V from Ga-DOTATOC PET/CT was predictive of the outcome. CONCLUSION: Specific texture features derived from preoperative Ga-DOTATOC and 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.
Authors: P Mapelli; C Bezzi; D Palumbo; C Canevari; S Ghezzo; A M Samanes Gajate; B Catalfamo; A Messina; L Presotto; A Guarnaccia; V Bettinardi; F Muffatti; V Andreasi; M Schiavo Lena; L Gianolli; S Partelli; M Falconi; P Scifo; F De Cobelli; M Picchio Journal: Eur J Nucl Med Mol Imaging Date: 2022-02-14 Impact factor: 10.057
Authors: Calogero Casà; Antonio Piras; Andrea D'Aviero; Francesco Preziosi; Silvia Mariani; Davide Cusumano; Angela Romano; Ivo Boskoski; Jacopo Lenkowicz; Nicola Dinapoli; Francesco Cellini; Maria Antonietta Gambacorta; Vincenzo Valentini; Gian Carlo Mattiucci; Luca Boldrini Journal: Ther Adv Gastrointest Endosc Date: 2022-03-16
Authors: Gabriel C Fine; Matthew F Covington; Bhasker R Koppula; Ahmed Ebada Salem; Richard H Wiggins; John M Hoffman; Kathryn A Morton Journal: Cancers (Basel) Date: 2022-06-08 Impact factor: 6.575
Authors: Maria Elena Laino; Angela Ammirabile; Ludovica Lofino; Lorenzo Mannelli; Francesco Fiz; Marco Francone; Arturo Chiti; Luca Saba; Matteo Agostino Orlandi; Victor Savevski Journal: Healthcare (Basel) Date: 2022-08-11