Characterization of Segmented 18F-DCFPyL PET/CT Lesions in the Context of PSMA-RADS Structured Reporting

Conclusions: Median effective segmentation thresholds were low (43%, 35%) for PSMA-RADS scores 4 and 5 but higher (61.4% ~ 70%) across 2 and all 3 subcategories. The challenging segmentation task for PyL PET images requires supervision by an expert or use of an advanced method such as deep learning. The relatively small sizes of the PyL lesions demonstrate its high focal concentration and specificity; yet, it also indicates that a radiomics analysis of these lesions may not provide new, significant information. Nonetheless, the differences in parameters related to each of the PSMA-RADS categories suggests that a sufficiently large dataset of lesions could be used with artificial intelligence applications to automate lesion categorization. Distribution of Lesion Underlying Tissue Sites for Each PSMA-RADS Score1A | 1B23A3B3C3D45Total Lesions per Anatomical LocationBone Benign26|75184Bone Metastasis1|1513275389819Breast Cancer|11Lymphadenopathy178|5272929217222813151886Prostate|213965125Soft Tissue Benign66|572481011698Soft Tissue Metastasis1|1219151968125Spleen8|1211Total Lesions per Category280|63283033315130406158383749

Source: Journal of Nuclear Medicine - May 20, 2019 Category: Nuclear Medicine Authors: Ashrafinia, S., Salehi Sadaghiani, M., Dalaie, P., Tulbah, R., Yin, Y., Leung, K., VanDenBerg, R., Leal, J., Gorin, M., Pomper, M., Rahmim, A., Rowe, S. Tags: Prostate/GU Imaging Posters Source Type: research