NIH R01 · 2025
Multimodal Learning for Contextually-Aware Longitudinal PET/CT image analysis
PROJECT SUMMARY 18F-Fluorodeoxyglucose (FDG) PET/CT imaging has become an essential tool for guiding and adapting treatments for lymphoma. However, the PET evaluation criteria currently used for assessing lymphoma, which consists of subjective visual scoring on a 5-point scale, is suboptimal. The visual scores suffer from high inter- observer variability and have low prognostic power for new emerging biological therapies. Quantitative PET metrics have been shown to be more predictive of clinical outcomes than visual scores, but quantitative analysis of whole-body PET/CT images is prohibitively time-consuming and impractical in routine clinical care. Deep learning (DL) has shown promise in…
From the public funding record at NIH RePORTER. Describes the funded project, not the reviews below.