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Associations Between Radiation Oncologist Demographic Factors and Segmentation Similarity Benchmarks: Insights From a

Kareem A Wahid1,2, Onur Sahin1, Suprateek Kundu3

  • 1Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.

JCO Clinical Cancer Informatics
|June 13, 2024
PubMed
Summary

The quality of radiotherapy auto-segmentation training data is crucial. Our study found that tumor segmentation significantly impacts quality, challenging assumptions about demographic factors influencing accuracy.

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Area of Science:

  • Medical Imaging and Radiation Oncology
  • Artificial Intelligence in Healthcare
  • Clinical Data Analysis

Background:

  • High-quality auto-segmentation training data is essential for radiotherapy.
  • Factors influencing clinician-derived segmentation quality are not well understood.
  • This study quantifies factors affecting segmentation accuracy.

Purpose of the Study:

  • To quantify factors influencing the quality of clinician-derived radiotherapy segmentations.
  • To identify key determinants of segmentation accuracy across various disease sites.
  • To evaluate the impact of tumor-related structures on segmentation quality.

Main Methods:

  • Utilized segmentations from radiation oncologists across five disease sites (breast, sarcoma, H&N, GYN, GI).
  • Assessed segmentation quality by comparing with expert-derived consensus using Dice Similarity Coefficient (DSC).
  • Employed generalized linear mixed-effects models to analyze associations between variables and segmentation quality.

Main Results:

  • A median of 55% of OAR and 31% of tumor segmentations exceeded expert interobserver variability cutoffs.
  • Tumor-related structures significantly negatively impacted segmentation quality across multiple disease sites.
  • No consistent relationships were found between segmentation quality and demographic variables.

Conclusions:

  • Conventional assumptions about factors influencing segmentation quality require re-evaluation.
  • Tumor segmentation presents a significant challenge in achieving high-quality radiotherapy training data.
  • Further research is needed to understand and improve segmentation accuracy.