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Auto-segmentation for total marrow irradiation.

William Tyler Watkins1, Kun Qing1, Chunhui Han1

  • 1Department of Radiation Oncology, City of Hope National Medical Center, Duarte, CA, United States.

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|September 16, 2022
PubMed
Summary
This summary is machine-generated.

Artificial Intelligence (AI) segmentation significantly improves efficiency in Total Marrow Irradiation (TMI) contouring for planning target volumes (PTVs) and organs at risk (OARs). This AI approach demonstrates accuracy and clinical utility in radiation therapy.

Keywords:
artificial intelligenceauto-contouringauto-segmentationtotal marrow irradiationtotal marrow lymphoid irradiation

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Area of Science:

  • Radiation Oncology
  • Medical Physics
  • Artificial Intelligence in Medicine

Background:

  • Total Marrow Irradiation (TMI) requires precise contouring of numerous structures for accurate dose delivery.
  • Manual contouring is time-consuming and prone to inter-observer variability.
  • Artificial Intelligence (AI) offers potential for automating segmentation tasks in radiation therapy planning.

Purpose of the Study:

  • To evaluate the accuracy and efficiency of AI-driven segmentation for Total Marrow Irradiation (TMI) contouring.
  • To compare AI-generated contours with human-edited contours across the entire body, including head and neck, thorax, abdomen, and pelvis.
  • To assess volumetric, spatial, and dosimetric variations between AI and clinical contour sets.

Main Methods:

  • AI segmentation software was used for whole-body contouring, including 27 organs at risk (OARs) and 4 planning target volumes (PTVs) in 21 patients.
  • Clinical and AI contours were compared using volumetric and spatial metrics (Sørensen-Dice coefficient, Hausdorff Distance).
  • Novel efficiency metrics were introduced, estimating clinical efficiency gains based on contour surface proximity.

Main Results:

  • AI segmentation demonstrated significant efficiency gains, averaging 61.4% for PTVs and 75.6% for OARs.
  • Spatial deviations >1mm were observed in 38.6% of PTV contours and 24.4% of OAR contours.
  • Dosimetric analysis showed minor variations, with 109 out of 467 dose-volume histogram (DVH) curves exceeding 0.05 differences.

Conclusions:

  • AI auto-segmentation software provides a highly efficient solution for TMI treatment planning.
  • Successful whole-body segmentation of PTVs and OARs was achieved, validated by spatial and dosimetric comparisons.
  • AI segmentation is a valuable tool for enhancing efficiency and consistency in TMI contouring.