BMC Medical Imaging (Dec 2022)

Performance of 18 F-FAPI PET/CT in assessing glioblastoma before radiotherapy: a pilot study

  • Yutang Yao,
  • Xiaofei Tan,
  • Wenya Yin,
  • Ying Kou,
  • Xiaoxiong Wang,
  • Xiao Jiang,
  • Shirong Chen,
  • Yongli Liu,
  • Jun Dang,
  • Jun Yin,
  • Zhuzhong Cheng

DOI
https://doi.org/10.1186/s12880-022-00952-w
Journal volume & issue
Vol. 22, no. 1
pp. 1 – 7

Abstract

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Abstract Background We aimed to determine the performance of 18 F-FAPI PET/CT used for preprocedural assessment of glioblastoma before radiotherapy. Methods Twelve glioblastoma patients having undergone incomplete surgical resection or biopsy were examined with 18 F-FAPI PET/CT and MRI scanning before radiotherapy. All patients had confirmed tumor residues according to findings of histopathological and/or long-term clinical and radiological follow-ups. Lesion characterization data, including SUVmax and tumor-to-background ratio (TBR) on PET/CT were attained. PET/CT and MRI findings were compared in terms of number of lesions. The correlation between immunohistochemistry, molecular expression, and PET/CT parameters was also evaluated. Results 18 F-FAPI PET/CT detected 16 FAPI-avid out of 23 lesions in 12 patients described on MRI. MRI was statistically different from 18 F-FAPI PET/CT for lesion detection according to the exact McNemar statistical test (P = 0.0156). The SUVmax and TBR of the glioblastomas was 7.08 ± 3.55 and 19.95 ± 13.22, respectively. The sensitivity and positive predictive value (PPV) of 18 F-FAPI PET were 69.6% and 100%, respectively. Neither the Ki-67 index nor the molecular expression was correlated with the FAPI-PET/CT parameters. Conclusion 18 F-FAPI PET/CT detects glioblastomas at a lower rate than MRI. However, the 100% PPV of the examination may make it useful for differentiating controversial lesions detected on MRI. The 18 F-FAPI-avid lesions are displayed more clearly probably due to a higher TBR. 18 F-FAPI PET/CT imaging might find application in glioblastoma biopsy and radiotherapy planning.

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