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Deep learning for automatic head and neck lymph node level delineation provides expert-level accuracy.

Thomas Weissmann1,2, Yixing Huang1,2, Stefan Fischer1,2

  • 1Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.

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Summary
This summary is machine-generated.

Deep learning models can accurately autodelineate head and neck lymph node levels (HN_LNL) for radiotherapy research. This open-source solution offers a standardized approach, matching expert contouring quality.

Keywords:
artificial intelligenceautosegmentationdeep learninghead and necklymph node levelneural networkradiotherapytarget volume

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Area of Science:

  • Medical Imaging
  • Radiotherapy Research
  • Artificial Intelligence in Medicine

Background:

  • Deep learning-based head and neck lymph node level (HN_LNL) autodelineation is crucial for radiotherapy but underinvestigated.
  • A lack of open-source solutions hinders large-scale research in HN_LNL autosegmentation.

Purpose of the Study:

  • To develop and evaluate an open-source deep learning model for HN_LNL autodelineation.
  • To compare the accuracy of deep learning autosegmentation with expert delineations and assess its suitability for research.

Main Methods:

  • An nnU-net 3D-fullres/2D-ensemble model was trained on 35 expert-delineated CTs for 20 HN_LNL.
  • A separate cohort of 20 CTs served as the test set for blinded expert evaluation.
  • A postprocessing step was introduced to align autocontours with CT slice planes, and its effect on accuracy was analyzed.

Main Results:

  • Blinded expert ratings showed no significant difference between deep learning segmentations and expert contours.
  • Deep learning segmentations with slice plane adjustment received higher expert ratings (81.0) compared to those without (77.2).
  • Geometric accuracy of deep learning segmentations was comparable to intraobserver variability (Dice: 0.76 vs. 0.77).

Conclusions:

  • A nnU-net model enables accurate HN_LNL autodelineation with limited data, suitable for large-scale research.
  • Geometric accuracy metrics are an imperfect surrogate for blinded expert evaluation in autosegmentation.
  • The developed model provides a valuable open-source tool for standardized HN_LNL autosegmentation in research settings.