Deep learning [¹⁸F]-FDG-PET/CT‑based algorithm for tumor burden estimation in metastatic melanoma patients under immunotherapy.

ABSTRACT:

Background and purpose

Artificial intelligence is increasingly used in radiation oncology, yet its application for tumor burden (TB) estimation remains limited. This study evaluated the performance of a [¹⁸F]-fluorodeoxyglucose positron emission tomography/computerized tomography ([¹⁸F]-FDG-PET/CT)-based deep learning model, PET-Assisted Reporting System ("PARS", Siemens Healthineers), for lesion detection, segmentation, and TB estimation in patients with metastatic melanoma undergoing immunotherapy.

Materials and methods

This retrospective study included 165 stage IV melanoma patients who underwent [¹⁸F]-FDG-PET/CT imaging prior to immunotherapy. Gross tumor volumes were segmented using PARS and compared with manual delineations perfo

SUBMITTER: Faro LL 

PROVIDER: S-EPMC12639252 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature