EJNMMI Research (Feb 2023)

The potential application of MR-derived ADCmin values from 68Ga-DOTATATE and 18F-FDG dual tracer PET/MR as replacements for FDG PET in assessment of grade and stage of pancreatic neuroendocrine tumors

  • Jing Gao,
  • Si Xu,
  • Huijun Ju,
  • Yu Pan,
  • Yifan Zhang

DOI
https://doi.org/10.1186/s13550-023-00960-z
Journal volume & issue
Vol. 13, no. 1
pp. 1 – 7

Abstract

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Abstract Background To evaluate the utility of 68Ga-DOTATATE and 18F-FDG PET/MR for prediction of grade and stage of pancreatic neuroendocrine tumors (PNETs), and to examine the correlation between parameters obtained from FDG PET and diffusion-weighted imaging (DWI) MR parameters. Methods A retrospective study using 68Ga-DOTATATE and 18F-FDG PET/MR imaging was performed between April 2020 and May 2022 on 46 individuals with histologically confirmed PNETs. Metabolic tumor volume (MTV), maximum standardized uptake value (FSUVmax), and tumor lesion glycolysis (TLG) for FDG; somatostatin receptor density (SRD), maximum standardized uptake value (GSUVmax), and total lesion somatostatin receptor density (TLSRD) for DOTATATE; and minimum and mean apparent diffusion coefficient (ADCmin and ADCmean) values for MRI, respectively. We performed Spearman’s correlation analysis to examine the links between these variables and primary tumor stage and grading. Results Higher PNET grading was associated with higher FSUVmax, MTV, and TLG values (P < 0.05). TLG, SRD, ADCmin, and ADCmean values were correlated with N staging, while SRD, MTV, ADCmin, TLG, and ADCmean were associated with M staging. Notably, ADCmin was a negative correlation between FSUVmax (r = − 0.52; P < 0.001), MTV (r = − 0.50; P < 0.001), and TLG (r = − 0.56; P < 0.001). Conclusions This study highlights significant correlative relationships between FDG PET-derived parameters and ADCmin. ADCmin may offer utility as a tool for PNET staging and grading in lieu of FDG PET. 68Ga-DOTATATE PET/MR alone may be a sufficient alternative to dual tracer PET/MR when conducting grading and staging of primary PNETs.

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