Deep learning based automated delineation of the intraprostatic gross tumour volume in PSMA-PET for patients with primary prostate cancer.

ABSTRACT:

Purpose

With the increased use of focal radiation dose escalation for primary prostate cancer (PCa), accurate delineation of gross tumor volume (GTV) in prostate-specific membrane antigen PET (PSMA-PET) becomes crucial. Manual approaches are time-consuming and observer dependent. The purpose of this study was to create a deep learning model for the accurate delineation of the intraprostatic GTV in PSMA-PET.

Methods

A 3D U-Net was trained on 128 different ¹⁸F-PSMA-1007 PET images from three different institutions. Testing was done on 52 patients including one independent internal cohort (Freiburg: n = 19) and three independent external cohorts (Dresden: n = 14 ¹⁸F-PSMA-1007, Boston: Massachusetts General Hospital (MGH): n = 9 ¹⁸F-DCFPyL-PSMA a

SUBMITTER: Holzschuh JC 

PROVIDER: S-EPMC10862258 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature