Multi-omics deep learning improves FDG PET-CT-based long-term prognostication of breast cancer.

ABSTRACT: [18F] fluorodeoxyglucose positron emission tomography - computed tomography (FDG PET-CT) is increasingly used for staging of breast cancer in the primary and recurrent setting, as well as in evaluating treatment response and in follow-up. Quantitative parameters derived from the primary tumor, even in non-metastatic patients (i.e., without distant metastases but possibly with nodal involvement), have shown prognostic value. Beyond visual interpretation, quantitative evaluations may improve diagnostic accuracy and reproducibility. However, current studies often rely on predefined parameters such as maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG), which may overlook the high-dimensional patterns inherent in FDG PET-CT. To address th

SUBMITTER: Liang X 

PROVIDER: S-EPMC12921030 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature