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Robust Multimodal Deep Learning for Lymphoma Subtype Classification Using 18F-FDG PET Maximum Intensity Projection

Seonhwa Kim1, Jun Hyeong Park2, Chul-Ho Kim3

  • 1Department of Radiation Oncology, School of Medicine, Ajou University, Suwon 16499, Republic of Korea.

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Summary
This summary is machine-generated.

A new deep learning model integrates PET imaging and clinical data to accurately classify lymphoma subtypes, improving diagnostic consistency across different scanners. This approach aids in personalized lymphoma management.

Keywords:
18F-FDGHodgkin lymphomaPET imageb-cell lymphomacancer imagingdeep learning

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Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Classifying lymphoma subtypes using 18F-FDG PET imaging is challenging due to imaging inconsistencies and variability.
  • Previous methods lacked standardization in protocols, scanner types, and inter-institutional data.
  • A multimodal deep learning framework is proposed to address these limitations.

Purpose of the Study:

  • To develop and validate a deep learning framework for hierarchical classification of lymphoma subtypes.
  • To integrate harmonized PET imaging features with structured clinical information.
  • To overcome inter-institutional variability and scanner inconsistencies in lymphoma classification.

Main Methods:

  • Collected multi-center data including 18F-FDG PET images and clinical variables.
  • Implemented a Scanner-Conditioned Normalization (SCN) module to harmonize feature distributions.
  • Utilized a multimodal deep learning model integrating anterior and lateral MIP images with clinical data.

Main Results:

  • Achieved AUC of 0.89 (internal) and 0.84 (external) for Hodgkin vs. non-Hodgkin lymphoma classification.
  • Achieved AUC of 0.84 (internal) and 0.76 (external) for DLBCL vs. FL classification.
  • Demonstrated robust performance across internal and external validation cohorts.

Conclusions:

  • Deep learning with 18F-FDG PET and clinical data shows potential for lymphoma subtype classification.
  • The proposed framework, LymphoMAP, offers a step towards assistive tools for early clinical decision-making.
  • Automated pipelines can support, not replace, conventional diagnostic workflows in personalized lymphoma management.