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Artificial intelligence-based automated segmentation and radiotherapy dose mapping for thoracic normal tissues.

Jue Jiang1, Chloe Min Seo Choi1,2, Joseph O Deasy1

  • 1Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States.

Physics and Imaging in Radiation Oncology
|February 19, 2024
PubMed
Summary

Artificial intelligence (AI) deformable image registration (DIR) and organ segmentation-based AI dose mapping (AIDA) provides rapid and accurate radiotherapy dose assessment for thoracic organs. This AI approach demonstrates feasibility for clinical radiotherapy applications.

Keywords:
Artificial intelligenceAutomated dose mappingCBCTLung cancerRegistration-segmentation

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Area of Science:

  • Radiotherapy physics and technology
  • Medical imaging and image analysis
  • Artificial intelligence in medicine

Background:

  • Objective assessment of delivered radiotherapy (RT) to thoracic organs necessitates efficient and precise deformable dose mapping.
  • Current methods can be time-consuming, impacting clinical workflow efficiency.

Purpose of the Study:

  • To implement and evaluate an AI-driven deformable image registration (DIR) and organ segmentation-based AI dose mapping (AIDA) system.
  • To assess the accuracy and speed of AIDA for dose mapping of the esophagus and heart in lung cancer patients.

Main Methods:

  • An automated pipeline integrating rigid alignment, AI-based organ segmentation on cone-beam CT (CBCT), and AI-DIR for dose mapping was developed.
  • AIDA dose metrics were calculated for 72 patients with locally advanced non-small cell lung cancer treated with concurrent chemoradiotherapy.
  • AIDA-derived dose metrics were compared against planned dose and manual contour-based dose mapping (manual DA).

Main Results:

  • AIDA processing time was approximately 2 minutes per patient.
  • AI segmentation achieved high accuracy for esophagus and heart, with mean Dice Similarity Coefficients (DSC) of 0.80 and 0.94, respectively.
  • AIDA identified a significantly lower heart dose compared to planned dose (p=0.04), with more frequent dose deviations (>=1Gy) observed compared to manual DA.

Conclusions:

  • Rapid estimation of thoracic tissue radiotherapy dose from CBCT is feasible using the AIDA system.
  • AIDA-derived metrics and segmentations showed comparable performance to manual dose assessment.
  • The findings support the potential utility of AIDA for enhancing radiotherapy applications.