PSMA PET Evaluation with a Deep Learning Platform Compared with a Standard Image Viewer and Histopathology

Standardized prostate-specific membrane antigen (PSMA) PET/CT evaluation and reporting was introduced to aid interpretation, reproducibility, and communication. Artificial intelligence may enhance these efforts. This study aimed to evaluate the performance of aPROMISE, a deep learning segmentation and reporting software for PSMA PET/CT, compared with a standard image viewer (IntelliSpace Portal [ISP]) in patients undergoing PSMA-radioguided surgery. This allowed the correlation of target lesions with histopathology as a standard of truth. Methods: [⁶⁸Ga]Ga-PSMA-I&T PET/CT of 96 patients with biochemical persistence or recurrence after prostatectomy (median prostate-specific antigen, 0.56 ng/mL; interquartile range, 0.31-1.24 ng/mL), who underwent PSMA-radioguided surgery, were retrospectively analyzed (twice with ISP and twice with aPROMISE) by 2 readers. Cohen κ with 95% CI was calculated to assess intra- and interrater agreement for miTNM stages. Differences between miTNM codelines were classified as no difference, minor difference (change of lymph node region without N/M change), and major difference (miTNM change). Results: Intrarater agreement rates were high for all categories, both readers, and systems (≥91.7%) with moderate to almost perfect κ values (reader 1, ISP, ≥0.51; range, 0.21-0.9; aPROMISE, ≥0.64; range, 0.41-0.99; reader 2, ISP, ≥0.83; range, 0.69-1; aPROMISE, ≥0.78; range, 0.63-1). Major differences occurred more frequently for reader 1 than for reader 2 (ISP, 26% vs. 13.5%; aPROMISE, 22.9% vs. 12.5%). Interrater agreement rates were high with both systems (≥92.2%), demonstrating substantial κ values (ISP, ≥0.73; range, 0.47-0.99; aPROMISE, ≥0.74; range, 0.54-1) with major miTNM staging differences in 21 (21.9%) cases. Readers identified 140 lesions by consensus, of which aPROMISE automatically segmented 129 (92.1%) lesions. Unsegmented lesions either were adjacent to high urine activity or demonstrated low PSMA expression. Agreement rates between imaging and histopathology were substantial (≥86.5%), corresponding to moderate to substantial κ values (≥0.6; range, 0.45-1) with major staging differences in 33 (34.4%) patients. This included 13 (13.5%) cases with metastases distant from targets identified on imaging. One of these lesions was automatically segmented by aPROMISE. Conclusion: Intra- and interreader agreement for PSMA PET/CT evaluation were similarly high with ISP and aPROMISE. The algorithm segmented 92.1% of all identified lesions. Software applications with artificial intelligence could be applied as support tools in PSMA PET/CT evaluation of early prostate cancer.