Comparison of 18F-FDG PET/CT and 18F-FAPI PET/CT in Systemic Staging of Newly Diagnosed Breast Cancer

Rationale and Objectives: The purpose of this study was to compare the performance of F-18-FAPI PET/CT and F-18-FDG PET/CT in systemic staging of newly diagnosed breast cancer. Methods: Breast cancer patients with initial clinical stage IIB-IIIC who have consequently underwent both F-18-FAPI and F-18-FDG PET/CT from June 2022 to June 2023 were retrospectively analyzed. New clinical stage was assigned to each patient if unsuspected level III axillary and extraaxillary regional lymph node metastases (URNM) and/or distant metastases were disclosed after PET/CT. Sensitivity for both tests was calculated on patient basis and lesion basis using histology or follow-up imaging as reference standard. Results: 38 patients were included. The overall upstaging rate was 47.4% for F-18-FAPI PET/CT (18/38) and 34.2% for F-18-FDG PET/CT (13/38). The rate of distant metastases disclosed by F-18-FAPI PET/CT was 5.5% in stage IIB patients, 30% in stage IIIA patients, 50% in stage IIIB patients, and 75% in stage IIIC patients. On patent-based analysis, the sensitivity of F-18-FAPI PET/CT was significantly different from that of F-18-FDG PET/CT in detecting URNM [100% (13/13) vs 53.8% (7/13), (P = 0.031)], but not for distant metastases [100% (10/10) vs 90% (9/10), (P = 1.000)]. On lesion-based analysis, the sensitivity of F-18-FAPI PET/CT was significantly higher than that of F-18-FDG PET/CT in detecting URNM [97.6% (41/42) vs 52.4% (22/42), (P < 0.001)] and distant metastases [98.1% (51/52) vs 67.3% (35/52), (P < 0.001)]. Conclusion: F-18-FAPI PET/CT leads to significant upstaging in newly diagnosed breast cancer, in a rate higher than F-18-FDG PET/CT. The significantly higher lesion-based sensitivity in unsuspected metastases implies a future role of F-18-FAPI PET/CT in evaluation of metastatic disease burden. (c) 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.