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Automatic dose prediction using deep learning and plan optimization with finite-element control for intensity

Yichao Shen1, Xingni Tang1, Sara Lin2

  • 1Department of Radiation Oncology, Taizhou Hospital, Taizhou, Zhejiang, People's Republic of China.

Medical Physics
|September 25, 2023
PubMed
Summary

Finite element-based optimization improves deep learning dose predictions for intensity-modulated radiation therapy (IMRT) plans. This automated approach reduces organ at risk doses while maintaining planning target volume coverage.

Keywords:
automatic intensity-modulation radiation therapy planningdeep-learning dose predictionfinite element

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Area of Science:

  • Medical Physics
  • Radiotherapy
  • Artificial Intelligence

Background:

  • Deep learning (DL) models mimic voxel doses for automated radiotherapy planning.
  • Plan optimization using voxel-dose features requires further investigation.

Purpose of the Study:

  • To evaluate a direct optimization strategy using finite elements (FEs) following DL dose prediction for intensity-modulated radiation therapy (IMRT) planning.

Main Methods:

  • A double-UNet DL model predicted dose distributions for 220 cervical cancer patients.
  • Finite elements (FEs) within organs at risk (OARs) and body regions were used to define optimization objectives.
  • A two-step optimization process constrained OAR and body-avoidance regions, with comparisons to direct DL prediction.

Main Results:

  • FE-based optimization significantly reduced mean doses to OARs (bladder, rectum, small intestine, femoral heads) while ensuring PTV homogeneity and conformity.
  • The FE-optimized plans (Method 1) yielded lower mean doses compared to direct DL prediction plans (Method 2) for critical OARs.

Conclusions:

  • Finite element-based direct optimization effectively lowers OAR doses and ensures adequate PTV coverage after DL dose prediction.
  • This automated method provides rapid, manual-adjustment-free plan optimization, especially beneficial for low-dose regions.