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Using in vivo EPID images to detect and quantify patient anatomy changes with gradient dose segmented analysis.

Jennifer M Steers1, Jorge Zavala Bojorquez1, Kevin Moore1

  • 1Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, 92037, USA.

Medical Physics
|September 23, 2020
PubMed
Summary

Gradient dose segmented analysis (GDSA) with in vivo electronic portal imaging device (EPID) images effectively predicts patient dose changes during treatment. This method aids in identifying deviations and monitoring machine performance for improved radiotherapy accuracy.

Keywords:
in vivo EPIDpatient specificquality assurance

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Area of Science:

  • Radiation Oncology
  • Medical Physics
  • Image Analysis

Background:

  • Accurate dose delivery is crucial in radiotherapy.
  • In vivo imaging offers real-time patient data.
  • Predicting dose changes during treatment is essential for adaptive radiotherapy.

Purpose of the Study:

  • To evaluate gradient dose segmented analysis (GDSA) combined with in vivo electronic portal imaging device (EPID) images for predicting planning target volume (PTV) mean dose changes.
  • To retrospectively analyze clinical data for treatment site-specific deviations.
  • To assess GDSA's utility in detecting day-to-day machine performance variations.

Main Methods:

  • In vivo EPID transit images were analyzed using GDSA and gamma analysis.
  • Phantom studies simulated errors (e.g., gas bubbles, weight loss, patient shifts).
  • GDSA and gamma parameters were optimized to correlate with PTV mean dose changes from treatment planning system (TPS) data.
  • Retrospective analysis of 852 patients over 23 months assessed clinical deviations and treatment site differences.
  • Time-series analysis evaluated GDSA for tracking daily machine output.

Main Results:

  • GDSA achieved a maximal R² of 0.90 in phantom studies for predicting PTV mean dose changes.
  • For patient data, GDSA predicted PTV mean dose changes with 0.09 ± 0.98% accuracy and improved precision over gamma analysis.
  • Over 95% of fractions showed deviations ≤2%; larger deviations occurred more in later fractions for head-and-neck and lung treatments.
  • Daily averaged GDSA metrics identified machine output changes as small as 1%.

Conclusions:

  • GDSA of in vivo EPID images is valuable for monitoring patient changes (e.g., weight loss, tumor shrinkage) during radiotherapy.
  • GDSA provides a quantitative metric to flag clinically significant PTV mean dose deviations.
  • Daily GDSA analysis can detect systematic treatment deviations due to machine performance issues.