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Using Auto-Segmentation to Reduce Contouring and Dose Inconsistency in Clinical Trials: The Simulated Impact on RTOG

Maria Thor1, Aditya Apte1, Rabia Haq1

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International Journal of Radiation Oncology, Biology, Physics
|November 16, 2020
PubMed
Summary

Radiation therapy heart doses were higher than reported due to inconsistent segmentation. Auto-contouring with deep learning (DL) improves accuracy and may reduce mortality in clinical trials.

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Area of Science:

  • Radiation Oncology
  • Medical Imaging
  • Clinical Trials

Background:

  • Contouring inconsistencies in clinical radiation therapy trials are understudied.
  • Inconsistent heart segmentation may lead to inaccurate dose reporting.

Purpose of the Study:

  • To investigate contouring inconsistencies in the RTOG 0617 trial.
  • To compare heart doses using deep-learning (DL) auto-contouring versus trial data.
  • To test the hypothesis that trial heart doses were higher than reported due to segmentation issues.

Main Methods:

  • Applied DL auto-contouring to RTOG 0617 trial data (442 patients).
  • Resegmented all hearts using a DL pipeline and quality assured them.
  • Compared dose metrics (V5%, V30%, mean heart dose) between DL and trial hearts.
  • Assessed 18 volume similarity metrics and correlated them with dose differences.

Main Results:

  • DL hearts had significantly higher dose metrics (e.g., mean heart dose: 15 Gy vs 12 Gy).
  • DL heart dose metrics were stronger predictors of overall survival.
  • Thirteen similarity metrics explained dose differences, with axial center of mass distance being the strongest predictor.

Conclusions:

  • Trial heart doses in RTOG 0617 were significantly higher than reported, potentially impacting mortality.
  • Auto-segmentation using DL algorithms can improve contouring consistency and dose accuracy.
  • DL auto-segmentation is likely to enhance the quality of clinical radiation therapy trials.