Machine Learning CT-Based Automatic Nodal Segmentation and PET Semi-Quantification of Intraoperative ⁶⁸Ga-PSMA-11 PET/CT Images in High-Risk Prostate Cancer: A Pilot Study.

ABSTRACT: High-resolution intraoperative PET/CT specimen imaging, coupled with prostate-specific membrane antigen (PSMA) molecular targeting, holds great potential for the rapid ex vivo identification of disease localizations in high-risk prostate cancer patients undergoing surgery. However, the accurate analysis of radiotracer uptake would require time-consuming manual volumetric segmentation of 3D images. The aim of this study was to test the feasibility of using machine learning to perform automatic nodal segmentation of intraoperative ⁶⁸Ga-PSMA-11 PET/CT specimen images. Six (n = 6) lymph-nodal specimens were imaged in the operating room after an e.v. injection of 2.1 MBq/kg of ⁶⁸Ga-PSMA-11. A machine learning-based approach for automatic lymph-nodal segmentation was

SUBMITTER: Rovera G 

PROVIDER: S-EPMC10529304 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature