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Leveraging deep learning-based segmentation and contours-driven deformable registration for dose accumulation in

Molly M McCulloch1, Guillaume Cazoulat1, Stina Svensson2

  • 1Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.

Frontiers in Oncology
|November 21, 2022
PubMed
Summary

Accurate dose accumulation in liver cancer radiation therapy (RT) is crucial. Contour-driven deformable image registration (DIR) methods in intensity-modulated radiation therapy (IMRT) show greater dose discrepancies than intensity-only methods, impacting treatment outcome predictions.

Keywords:
GI toxicitydeformable image registration (DIR)dose accumulationimage-guided radiation therapy (IGRT)liver cancer

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Area of Science:

  • Radiation Oncology
  • Medical Physics
  • Image-Guided Therapy

Background:

  • Accurate dose delivery is essential for effective liver cancer radiation therapy (RT).
  • Discrepancies between planned and delivered radiation doses to gastrointestinal (GI) structures can affect treatment outcome predictions.
  • Deformable image registration (DIR) is critical for assessing these dose discrepancies in image-guided RT.

Purpose of the Study:

  • To develop an automated workflow for dose accumulation in liver cancer RT using a treatment planning system (TPS).
  • To evaluate the accuracy of this workflow with different DIR algorithms.
  • To assess the impact of dose discrepancies on normal tissue complication probabilities (NTCP).

Main Methods:

  • Retrospective analysis of 56 liver cancer patients treated with external beam RT.
  • Auto-segmentation of liver, stomach, and duodenum using deep learning on planning CTs and daily CT-on-rails (CTOR).
  • Dose accumulation using three DIR methods: intensity-only, intensity + contours, and biomechanical (contours only).

Main Results:

  • Deep learning segmentation accelerated dose accumulation.
  • Contour-driven DIR methods resulted in significantly higher dose variance for normal tissues compared to intensity-only DIR.
  • Significant dose differences (>2Gy) in the Gross Tumor Volume (GTV) were observed with contour-driven DIR.
  • Duodenum toxicity risk showed high sensitivity to dose discrepancies.

Conclusions:

  • An automated dose accumulation workflow was successfully implemented in a commercial TPS for liver cancer RT.
  • Contour-driven DIR methods reveal larger planned-to-accumulated dose discrepancies than intensity-only methods.
  • Contour-driven DIR better estimates complex GI organ deformations, crucial for accurate dose assessment.