Automatic Segmentation of Metastatic Breast Cancer Lesions on ¹⁸F-FDG PET/CT Longitudinal Acquisitions for Treatment Response Assessment.

ABSTRACT: Metastatic breast cancer patients receive lifelong medication and are regularly monitored for disease progression. The aim of this work was to (1) propose networks to segment breast cancer metastatic lesions on longitudinal whole-body PET/CT and (2) extract imaging biomarkers from the segmentations and evaluate their potential to determine treatment response. Baseline and follow-up PET/CT images of 60 patients from the EPICUREseinmeta study were used to train two deep-learning models to segment breast cancer metastatic lesions: One for baseline images and one for follow-up images. From the automatic segmentations, four imaging biomarkers were computed and evaluated: SULpeak, Total Lesion Glycolysis (TLG), PET Bone Index (PBI) and PET Liver Index (PLI). The first network obtained a mean Dic

SUBMITTER: Moreau N 

PROVIDER: S-EPMC8750371 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature