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Estimating how contouring differences affect normal tissue complication probability modelling.

Miguel Garrett Fernandes1, Johan Bussink1, Robin Wijsman2

  • 1Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands.

Physics and Imaging in Radiation Oncology
|January 31, 2024
PubMed
Summary
This summary is machine-generated.

Discrepancies in organ at risk contouring impact normal tissue complication probability (NTCP) model performance, especially with steeper NTCP curves. Low-dose parameters are more robust to contouring errors in non-small cell lung cancer (NSCLC) patients.

Keywords:
Automatic contouringHeartMonte CarloNSCLCNTCPRadiotoxicity

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Area of Science:

  • Radiation oncology
  • Medical physics
  • Computational biology

Background:

  • Normal tissue complication probability (NTCP) models are crucial for predicting treatment toxicity in radiation oncology.
  • Automatic contouring methods are frequently used in retrospective datasets for organ at risk (OAR) segmentation, potentially introducing contour discrepancies.
  • Understanding the impact of these contouring variations on NTCP model performance is essential for reliable clinical application.

Purpose of the Study:

  • To develop and apply a methodology for quantifying the impact of contour discrepancies on NTCP model performance.
  • To evaluate this methodology using heart contours from non-small cell lung cancer (NSCLC) patients.
  • To assess the influence of contour variability on toxicity prediction accuracy.

Main Methods:

  • A simulation-based approach was used, designating one contour set as ground truth to simulate outcomes via a predefined NTCP relationship.
  • Dosimetric parameters from different contour sets (manual, deep learning, atlas-based) were used to fit toxicity models and compare their performance.
  • The study analyzed a dataset of 605 stage IIA-IIIB NSCLC patients with available manual, deep learning, and atlas-based heart contours.

Main Results:

  • The impact of contour differences on NTCP model performance was dependent on the NTCP model's slope, the dosimetric parameter used, and cohort size.
  • Steeper NTCP curves amplified the effect of contour discrepancies on model performance.
  • For the analyzed dataset, dose-volume histogram parameters in the lower dose range demonstrated greater robustness to contouring variations.

Conclusions:

  • The proposed methodology enables the assessment of contouring model suitability for NTCP model development.
  • For heart contouring in comparable datasets, a minimum average Dice similarity coefficient (DSC) of 88.5 ± 4.5% is recommended for shallow NTCP relationships.
  • Higher DSC values are necessary for steep NTCP relationships to ensure reliable NTCP model performance.