MP15-1968Ga-PSMA PET/CT For RECURRENT PROSTATE CANCER AFTER RADICAL PROSTATECTOMY: WHAT IS NEXT?doi:10.1097/JU.0000000000001996.19Giorgio GandagliaDaniele RobestiNicola FossatiArmando StabileEnrico CamisassaVito CucchiaraMarco MoschiniAndrea Necchi
Lesions with comparatively low contrast to the surrounding tissue can be hard to detect, particularly in noisy imaging conditions originating, for example, from very short scan times, very low amounts of tracer activity injected or from scanning obese patients, which increases the scatter fraction. ...
Interobserver-agreement for mpMRI and 68Ga-PSMA-PET/MRI was: ≥T3: κ = 0.58/0.47; N1: κ = 0.55/0.92. Diagnostic accuracy for mpMRI vs 68Ga-PSMA-PET/MRI readers for ≥ T3 was AUC: 0.72, 0.62 vs 0.71, 0.72 (p > 0.38) and for N1 was AUC: 0.39, 0.55 vs 0.72, 0.78 (p <...
A multiple logistic regression model revealed tumoural PSMA(%neg) (p < 0.01, OR = 9.629) and growth pattern (p = 0.0497, OR = 306.537) as significant predictors for a negative PSMA-PET scan. Conclusions We describe PSMA(%neg), infiltrative growth pattern, smaller tumour size and WHO/ISUP...
Robesti D.Gandaglia G.Fossati N.Stabile A.Mazzone E.Cucchiara V.Rosiello G.Leni R.Camisassa E.Moschini M.
Jan Hendrik RueschoffDaniela FerraroUrs MuehlematterNiels Rupp
We describe PSMA %neg , infiltrative growth pattern, smaller tumour size and WHO/ISUP grade group 2 as parameters associated with a lower 68 Ga-PSMA-11 uptake in prostate cancer. These findings can serve as fundament for future biopsy-based biomarker development to enable an individualized, ...
Introduction: Artificial intelligence is transforming healthcare by driving innovation, automation, and optimization across various fields of medicine. The aim of this study was to determine whether artificial intelligence (AI) techniques can be used in