Preclinical Identification Of Tumor-Draining Lymph Nodes Using a Multimodal Non-invasive In vivo Imaging Approach

Philipp Knopf1, Dimitri Stowbur1,2, Sabrina H L Hoffmann1

  • 1Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University, Tübingen, Germany.

Abstract

Insights

Accurate identification of tumor-draining lymph nodes (TDLNs) is crucial for cancer immunotherapy. 18F-FDG PET/MRI offers a non-invasive method for precise TDLN identification and metabolic assessment, complementing Patent Blue V for macroscopic localization.

Area of Science:

  • Oncology
  • Immunology
  • Medical Imaging

Background:

  • Tumor-draining lymph node (TDLN) resection is standard for metastasis detection.
  • TDLN resection may reduce the efficacy of cancer immunotherapies.
  • Precise preclinical TDLN identification is vital for understanding immunological mechanisms.

Purpose of the Study:

  • To validate non-invasive in vivo imaging approaches for precise TDLN identification.
  • To compare optical imaging agents and PET/MRI for TDLN visualization.
  • To assess lymphatic drainage and glucose metabolism in TDLNs.

Main Methods:

  • Injected Patent Blue V and IRDye® 800CW PEG for optical imaging in mice with MC38 adenocarcinomas.
  • Administered 18F-FDG for PET/MRI to assess TDLN lymphatic drainage and glucose metabolism.
  • Performed ex vivo analysis including optical imaging, biodistribution, and autoradiography.

Main Results:

  • Patent Blue V excelled in macroscopic identification but lacked non-invasive sensitivity.
  • 18F-FDG PET/MRI accurately identified TDLNs and differentiated lymph nodes based on glucose metabolism.
  • Ex vivo analyses confirmed the in vivo PET/MRI findings.

Conclusions:

  • 18F-FDG PET/MRI is a feasible method for non-invasive in vivo, intraoperative, and ex vivo TDLN identification.
  • Patent Blue V aids macroscopic localization of lymphatic drainage.
  • Both imaging modalities are valuable, cost-effective tools for preclinical TDLN identification.

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