Global and regional accuracy of deep learning-based tumor segmentation from whole-body [¹⁸F]fluorodeoxyglucose PET/CT images.

ABSTRACT:

Background

The number of [¹⁸F]fluorodeoxyglucose ([¹⁸F]FDG)-PET/CT scans performed has significantly increased in the last decade in line with the increasing trend of oncological malignancies. Such images, which signal high glucose-uptake areas are key in defining the extent of the disease, staging and response to therapy. Processing and evaluation of ([¹⁸F]FDG)-PET/CT scans, however, require manual annotation by well-trained specialists and above all time. In time and resource-constrained settings meeting the increasing demand for PET/CT scans has become challenging. The main goal of our study was to test the relationship between the volumes predicted by the deep learning algorithm and the manually segmented ones. The secondary objective goal was to measure the extent at which the

SUBMITTER: Ciarmiello A 

PROVIDER: S-EPMC12764704 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature