An Automated Deep Learning-Based Framework for Uptake Segmentation and Classification on PSMA PET/CT Imaging of Patients with Prostate Cancer.

ABSTRACT: Uptake segmentation and classification on PSMA PET/CT are important for automating whole-body tumor burden determinations. We developed and evaluated an automated deep learning (DL)-based framework that segments and classifies uptake on PSMA PET/CT. We identified 193 [¹⁸F] DCFPyL PET/CT scans of patients with biochemically recurrent prostate cancer from two institutions, including 137 [¹⁸F] DCFPyL PET/CT scans for training and internally testing, and 56 scans from another institution for external testing. Two radiologists segmented and labelled foci as suspicious or non-suspicious for malignancy. A DL-based segmentation was developed with two independent CNNs. An anatomical prior guidance was applied to make the DL framework focus on PSMA-avid lesions. Segmentation pe

SUBMITTER: Li Y 

PROVIDER: S-EPMC11522269 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature