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High-frequency Ultrasound Imaging of Mouse Cervical Lymph Nodes
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Automated Lymph Node Localization and Segmentation in Patients with Head and Neck Cancer: Opportunities and

Miriam Rinneburger1, Heike Carolus2, Andra-Iza Iuga1

  • 1Institute of Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany.

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Summary

An AI model for cervical lymph node segmentation shows promise for smaller nodes in head and neck cancer staging. However, enlarged or necrotic nodes require further AI model retraining for accurate clinical staging.

Keywords:
artificial intelligencecomputed tomographydeep learninghead and neck cancerlymph nodesstaging

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Oncology

Background:

  • Accurate lymph node assessment is crucial for head and neck cancer staging.
  • Metastatic lymph nodes can exhibit unique features like necrosis or large size.
  • Current manual segmentation is time-consuming for radiologists.

Purpose of the Study:

  • To evaluate a generic AI cervical lymph node segmentation model's performance.
  • To assess the model's accuracy in head and neck cancer patients.
  • To identify limitations for improved clinical staging.

Main Methods:

  • Retrospective single-center study of 125 head and neck cancer patients.
  • Semi-automatic lymph node segmentation by radiologists, confirmed by a second reader.
  • Comparison of manual segmentations with AI-generated segmentations from a pre-trained model.

Main Results:

  • AI achieved an average recall of 0.70 with 6.5 false positives per scan.
  • Average global Dice score was 0.73, with a Hausdorff distance of 0.88 mm.
  • Performance was lower for enlarged (≥ 15 mm) metastatic lymph nodes (recall 0.36).

Conclusions:

  • The AI model demonstrates good performance for smaller cervical lymph nodes (≤ 15 mm).
  • Localization and segmentation accuracy are adequate for non-enlarged nodes.
  • Retraining is necessary for enlarged and necrotic nodes to improve cN staging accuracy.