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Accurate diagnosis and grading are critical for pancreatic neuroendocrine neoplasm (pNEN) management. This study compares the diagnostic and grading value of 68Ga-DOTATATE PET/MR and 18F-FDG PET/MR for pNENs separately as well as in combination. A total of 36 patients with histologically confirmed pNENs, who underwent both 68Ga-DOTATATE PET/MR and 18F-FDG PET/MR within 2 weeks from 2020 to 2021, were retrospectively collected and analyzed. The maximum standardized uptake values of 68Ga-DOTATATE (G-SUVmax) and 18F-FDG (F-SUVmax) on PET and the minimum values of apparent diffusion coefficient (ADCmin) on MR were measured on the lesions with known histological grading (25 by surgery, 11 by biopsy). Receiver-operating characteristic analysis was applied to determine the cutoffs of these parameters or their combinations for differentiation between G1 and G2, as well as between low-grade and high-grade pNENs. The Spearman rank correlation coefficient was used to assess the correlation between the imaging parameters and the maximum tumor diameters. The detection rate of 68Ga-DOTATATE PET imaging alone was 95%, 87.5%, and 37.5% for G1, G2, and G3, respectively. Adding 18F-FDG PET or MR sequences of PET/MR increased the detection rate to 100% in all grades. Among the three parameters, G-SUVmax had the highest diagnostic rate in predicting tumor grade. It presented a sensitivity of 87.5% and a specificity of 80.0% with a cutoff value of 42.75 for differentiating G2 from G1 pNETs and a sensitivity and specificity of 100% and 71.4% with a cutoff value of 32.75 in predicting high-grade pNENs. The ratio of G-SUVmax to F-SUVmax (G-SUVmax/F-SUVmax) showed slight improvement in the diagnostic rate, while the product of G-SUVmax and ADCmin (G-SUVmax*ADCmin) did not improve the diagnostic rate. 68Ga-DOTATATE PET/MR alone is sufficient for the diagnosis of pNENs and the prediction of various grades. [ABSTRACT FROM AUTHOR]

Accurate diagnosis and grading are critical for pancreatic neuroendocrine neoplasm (pNEN) management. This study compares the diagnostic and grading value of 68 Ga-DOTATATE PET/MR and 18 F-FDG PET/MR for pNENs separately as well as in combination. A total of 36 patients with histologically confirmed pNENs, who underwent both 68 Ga-DOTATATE PET/MR and 18 F-FDG PET/MR within 2 weeks from 2020 to 2021, were retrospectively collected and analyzed. The maximum standardized uptake values of 68 Ga-DOTATATE (G-SUVmax) and 18 F-FDG (F-SUVmax) on PET and the minimum values of apparent diffusion coefficient (ADCmin) on MR were measured on the lesions with known histological grading (25 by surgery, 11 by biopsy). Receiver-operating characteristic analysis was applied to determine the cutoffs of these parameters or their combinations for differentiation between G1 and G2, as well as between low-grade and high-grade pNENs. The Spearman rank correlation coefficient was used to assess the correlation between the imaging parameters and the maximum tumor diameters. The detection rate of 68 Ga-DOTATATE PET imaging alone was 95%, 87.5%, and 37.5% for G1, G2, and G3, respectively. Adding 18 F-FDG PET or MR sequences of PET/MR increased the detection rate to 100% in all grades. Among the three parameters, G-SUVmax had the highest diagnostic rate in predicting tumor grade. It presented a sensitivity of 87.5% and a specificity of 80.0% with a cutoff value of 42.75 for differentiating G2 from G1 pNETs and a sensitivity and specificity of 100% and 71.4% with a cutoff value of 32.75 in predicting high-grade pNENs. The ratio of G-SUVmax to F-SUVmax (G-SUVmax/F-SUVmax) showed slight improvement in the diagnostic rate, while the product of G-SUVmax and ADCmin (G-SUVmax*ADCmin) did not improve the diagnostic rate. 68 Ga-DOTATATE PET/MR alone is sufficient for the diagnosis of pNENs and the prediction of various grades., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Zhou, Zhao, Pan, Ju, Huang, Jiang, Jin and Zhang.)