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Data clustering to select clinically-relevant test cases for algorithm benchmarking and characterization.

Sarah Weppler1,2,3, Colleen Schinkel2,4, Charles Kirkby1,4,5

  • 1Department of Physics and Astronomy, University of Calgary, 2500 University Dr NW, Calgary, Alberta, T2N 1N4, Canada.

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This summary is machine-generated.

This study introduces a framework for selecting representative test cases to efficiently benchmark algorithms, reducing the need for extensive ground-truth data. The method successfully identified key differences in dose violations for head and neck cancer patients.

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Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Computational Biology

Background:

  • Algorithm benchmarking is crucial for clinical implementation but often limited by resource-intensive ground-truth establishment.
  • Current methods may not adequately capture the full spectrum of algorithm performance due to limited test case selection.
  • Efficient and representative test case selection is needed for robust algorithm validation.

Purpose of the Study:

  • To propose and demonstrate a framework for selecting representative test cases to assess algorithm and workflow equivalence.
  • To minimize the number of ground-truth comparisons required for clinically relevant benchmarking.
  • To identify and characterize workflow discrepancies in dose objective violations using clustering.

Main Methods:

  • Clustering of differences between two deformable image registration workflows using Hopkins statistic and k-medoid clustering.
  • Analysis of dose objective violations (e.g., parotid gland D50%, spinal cord/brainstem Dmax, CTV coverage) in 15 head and neck cancer patients.
  • Identification of candidate test cases at cluster centers ('medoids') representing workflow-relevant algorithm differences.

Main Results:

  • Workflow outputs showed inherent clustering (Hopkins statistic = 0.75), indicating natural groupings of dose violation differences.
  • K-medoid clustering identified five distinct clusters, significantly stratifying workflow differences (MANOVA: p < 0.001).
  • Systematic algorithm differences related to parotid gland volumes, contour deformations, and CTV-to-PTV margins were identified (p < 0.05).

Conclusions:

  • The proposed framework successfully clustered workflow outputs and identified five representative test cases for algorithm discrepancies.
  • This approach enhances resource allocation in benchmarking and improves the clinical applicability of algorithm characterization results.
  • The method provides a more efficient and robust strategy for validating algorithms in radiation oncology.