Deep neural network for automatic characterization of lesions on 68Ga-PSMA-11 PET/CT

Yu Zhao, Andrei Gafita, Bernd Vollnberg, Giles Tetteh, Fabian Haupt, Ali Afshar-Oromieh, Bjoern Menze, Matthias Eiber, Axel Rominger & Kuangyu Shi
PURPOSE: This study proposes an automated prostate cancer (PC) lesion characterization method based on the deep neural network to determine tumor burden on 68Ga-PSMA-11 PET/CT to potentially facilitate the optimization of PSMA-directed radionuclide therapy. METHODS: We collected 68Ga-PSMA-11 PET/CT images from 193 patients with metastatic PC at three medical centers. For proof-of-concept, we focused on the detection of pelvis bone and lymph node lesions. A deep neural network (triple-combining 2.5D U-Net) was developed for the...
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