Phase 2 study of 99mTc-Trofolastat SPECT/CT to identify and localize prostate cancer in intermediate- and high-risk patients undergoing radical prostatectomy and extended pelvic LN dissection

Abstract

99mTc-trofolastat (99mTc-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 99mTc-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. Methods: PCa patients (n = 105) with an increased risk of LN involvement (LNI) underwent pelvic 99mTc-trofolastat SPECT/CT before radical prostatectomy with extended pelvic LN dissection. The sensitivity of 99mTc-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 99mTc-trofolastat uptake for LNI. Results: 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 1 3 from ≥ 3 1 4, whereas TBRs discriminated high-grade disease from normal lobes better. Visual scores and TBRs correlated significantly with GS. 99mTc-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%. Conclusion: 99mTc-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.