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Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy
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A Language Vision Model Approach for Automated Tumor Contouring in Radiation Oncology.

Yi Luo1, Hamed Hooshangnejad1,2, Xue Feng3

  • 1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21287, USA.

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|August 28, 2025
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Summary
This summary is machine-generated.

The Oncology Contouring Copilot (OCC) system uses AI and oncologist expertise to improve lung cancer tumor delineation. This AI tool significantly reduces false positives in radiotherapy planning, enhancing cancer care efficiency.

Keywords:
language vision modellung cancerradiotherapytumor contouring

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Area of Science:

  • Medical Imaging and Radiation Oncology
  • Artificial Intelligence in Healthcare
  • Natural Language Processing and Computer Vision

Background:

  • Lung cancer is a leading global cause of cancer mortality.
  • Accurate tumor delineation for radiation therapy is complex and often limited in resource-poor settings.
  • Existing AI solutions face challenges with high false positive rates in medical image analysis.

Purpose of the Study:

  • To develop the Oncology Contouring Copilot (OCC) system to enhance tumor delineation accuracy.
  • To integrate oncologist expertise with AI for efficient and precise oncological workflows.
  • To reduce false positives in radiotherapy planning through AI-human collaboration.

Main Methods:

  • Utilizing Language Vision Models (LVMs) like GPT-4V for automated tumor delineation from CT scans.
  • Combining textual descriptions with visual data to reduce false positives.
  • Leveraging expert oncologist knowledge within the AI framework.

Main Results:

  • Achieved a 35.0% reduction in the false discovery rate.
  • Demonstrated a 72.4% decrease in false positives per scan.
  • Attained an F1-score of 0.652 for tumor delineation accuracy.

Conclusions:

  • The OCC system represents a significant advancement in oncology care, particularly in radiotherapy planning.
  • LVMs and novel prompt techniques effectively minimize AI hallucinations and improve contouring accuracy.
  • OCC offers a scalable framework to streamline workflows and reduce manual processes in radiation oncology.