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Deep learning for elective neck delineation: More consistent and time efficient.

J van der Veen1, S Willems2, H Bollen1

  • 1KU Leuven, Dept. Oncology, Laboratory of Experimental Radiotherapy, & UZ Leuven, Radiation Oncology, Belgium.

Radiotherapy and Oncology : Journal of the European Society for Therapeutic Radiology and Oncology
|October 16, 2020
PubMed
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A novel 3D convolutional neural network (CNN) significantly improves the efficiency and consistency of lymph node level delineation in head and neck cancer (HNC) patients. This AI tool reduces time and interobserver variability, making it suitable for clinical practice.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Oncology
  • Radiation Oncology

Background:

  • Manual delineation of lymph node levels in head and neck cancer (HNC) patients for radiation therapy planning is time-consuming and suffers from interobserver variability (IOV).
  • Existing international consensus guidelines for delineation do not fully resolve these challenges.

Purpose of the Study:

  • To develop and validate a 3D convolutional neural network (CNN) for semi-automated delineation of all nodal neck levels in HNC patients.
  • To evaluate the CNN's accuracy, efficiency, and consistency compared to manual delineation.

Main Methods:

  • A 3D CNN was trained on 69 HNC patients' data.
  • Seventeen lymph node levels were manually delineated by two independent observers in 16 new patients.
Keywords:
DelineationElective nodal target volumesHead and neck neoplasmsNeural networks (computer)Observer variationRadiotherapy

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  • Automated delineations from the CNN were generated and subsequently corrected by observers; time and variability (Dice Similarity Coefficient, Mean Surface Distance, Hausdorff Distance) were compared.

    • Main Results:

    • Correcting automated delineations was significantly faster than manual delineation (35 vs 52 min; p < 10⁻⁵).
    • Automated delineation showed high agreement with corrected delineations (DSC >85%) for key lymph node levels.
    • Interobserver variability was significantly reduced with the CNN (MSD: 1.4 mm vs 2.5 mm; p < 10⁻¹¹).

    Conclusions:

    • The developed CNN is more efficient and consistent than manual delineation for elective lymph node levels in HNC.
    • The findings support the implementation of this CNN in clinical practice for improved head and neck cancer treatment planning.