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Deep Learning Applications in Lymphoma Imaging.

Vera Sorin1, Israel Cohen2, Ruth Lekach3

  • 1Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA.

Acta Haematologica
|July 14, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence, especially deep learning models, is revolutionizing lymphoma imaging for automated detection and classification. Challenges remain in clinical adoption due to data variability and workflow integration.

Keywords:
Artificial intelligenceConvolutional neural networkDeep learningLymphomaOncology

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Oncology
  • Hematologic Malignancies

Background:

  • Lymphomas are heterogeneous lymphoid cancers requiring advanced diagnostics.
  • Imaging like PET/CT, CT, and MRI are crucial for lymphoma diagnosis, staging, and monitoring.
  • Challenges in current imaging include tumor heterogeneity and inter-observer variability.

Purpose of the Study:

  • To explore the role of Artificial Intelligence (AI), specifically deep learning (DL), in enhancing lymphoma imaging.
  • To assess AI's capabilities in automated detection, segmentation, and classification across various imaging modalities.
  • To identify challenges and opportunities for AI integration in clinical lymphoma management.

Main Methods:

  • Application of deep learning (DL) models to analyze lymphoma imaging data.
  • Utilizing AI for automated tasks in PET/CT, CT, and MRI.
  • Evaluating AI performance in detection, segmentation, and classification of lymphomas.

Main Results:

  • AI and DL models demonstrate potential for automated detection, segmentation, and classification in lymphoma imaging.
  • Key applications include metabolic response assessment, tumor volume quantification, lymph node analysis, and CNS involvement detection.
  • AI can improve diagnostic accuracy and reduce inter-observer variability.

Conclusions:

  • Deep learning models are transforming lymphoma imaging across PET/CT, CT, and MRI.
  • Widespread clinical adoption faces hurdles including protocol variability, data limitations, interpretability, and workflow integration.
  • Rigorous validation is essential for safe and effective AI integration into clinical practice.