Robust Multimodal Deep Learning for Lymphoma Subtype Classification Using ¹⁸F-FDG PET Maximum Intensity Projection Images and Clinical Data: A Multi-Center Study.

ABSTRACT: Background: Previous attempts to classify lymphoma subtypes based on metabolic features extracted from ¹⁸F-FDG PET imaging have been hindered by inconsistencies in imaging protocols, scanner types, and inter-institutional variability. To overcome these limitations, we propose a multimodal deep learning framework that integrates harmonized PET imaging features with structured clinical information. The proposed framework is designed to perform hierarchical classification of clinically meaningful lymphoma subtypes through two sequential binary classification tasks. Methods: We collected multi-center data comprising ¹⁸F-FDG PET images and structured clinical variables of patients with lymphoma. To mitigate domain shifts caused by different scanner manufactur

SUBMITTER: Kim S 

PROVIDER: S-EPMC12838601 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature