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  • Goffin, Karolien E; Joniau, Steven; Tenke, Peter; Slawin, Kevin; Klein, Eric A; Stambler, Nancy; Strack, Thomas; Babich, John; Armor, Thomas; Wong, Vivien

    The Journal of nuclear medicine (1978), 09/2017, Letnik: 58, Številka: 9
    Journal Article

    Tc-trofolastat ( Tc-MIP-1404), a small-molecule inhibitor of prostate-specific membrane antigen, shows high potential to detect prostate cancer (PCa) noninvasively using SPECT. We therefore wanted to assess the performance of Tc-trofolastat SPECT/CT in a phase 2 multicenter, multireader prospective study in patients with intermediate- and high-grade PCa, before radical prostatectomy and extended pelvic lymph node (LN) dissection, with histopathology as the gold standard. PCa patients ( = 105) with an increased risk of LN involvement (LNI) underwent pelvic Tc-trofolastat SPECT/CT before radical prostatectomy with extended pelvic LN dissection. The sensitivity of Tc-trofolastat for detection of PCa on a patient and lobe basis, using visual and semiquantitative (tumor-to-background ratio TBR) scores, and of LNI was evaluated as well as the correlation of uptake within the gland to Gleason scores (GS) and assessment of the predictive potential of Tc-trofolastat uptake for LNI. PCa was detected in 98 patients (94%) with acceptable variability between readers. There was a significantly higher visual score and TBR in positive lobes compared with tumor-negative lobes. Receiver-operating characteristic analysis showed that visual scores more accurately discriminated lobes with GS ≤ 3 + 3 from ≥ 3 + 4, whereas TBRs discriminated high-grade disease from normal lobes better. Visual scores and TBRs correlated significantly with GS. Tc-trofolastat SPECT/CT detected LNI with a sensitivity of 50% and specificity of 87%, and TBR values significantly predicted LNI with a sensitivity of 90%. Tc-trofolastat SPECT/CT detects PCa with high sensitivity in patients with intermediate- and high-risk PCa compared with histology. It has the potential to be used as a surrogate marker for GS and predict LNI.