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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Performance of an Artificial Intelligence Foundation Model for Prostate Radiotherapy Segmentation.

Matthew Doucette1, Chien-Yi Liao1, Mu-Han Lin1,2

  • 1Medical Artificial Intelligence and Automation (MAIA) Laboratory, University of Texas Southwestern Medical Center, Dallas, TX.

JCO Clinical Cancer Informatics
|April 14, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) models show limited effectiveness for prostate radiotherapy segmentation. Current general-purpose AI models do not surpass existing methods, requiring further development for clinical use in radiation therapy planning.

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Area of Science:

  • Medical Physics
  • Radiotherapy
  • Artificial Intelligence

Background:

  • Accurate target segmentation is crucial for effective prostate cancer radiation therapy.
  • Artificial intelligence (AI) offers potential for automating and improving segmentation tasks.

Purpose of the Study:

  • To evaluate the performance of a general-purpose AI foundation model, Segment Anything Model 2 (SAM 2), for prostate radiotherapy target segmentation.
  • To assess the impact of varying levels of human intervention on AI segmentation accuracy.

Main Methods:

  • AI segmentation using SAM 2 was performed on computed tomography (CT) images.
  • Segmentation accuracy was evaluated using Dice Similarity Coefficient (DSC) and 95th percentile Hausdorff Distance (HD95).
  • Performance was assessed across different intervals of ground truth slices (every 2nd to 10th slice) for both intact and postoperative prostate cases.

Main Results:

  • SAM 2 performance was comparable to or worse than interpolation methods for both intact and postoperative prostate segmentation.
  • AI segmentation accuracy was significantly better for intact preoperative cases (P < .01) compared to postoperative cases.
  • Increasing the interval between ground truth slices reduced DSC and increased HD95, particularly in postoperative cases.

Conclusions:

  • Current general-purpose AI foundation models are inadequate for prostate radiotherapy segmentation.
  • Further research is needed on fine-tuning and task-specific AI models for clinical application in radiotherapy planning.