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Knowledge-based three-dimensional dose prediction for tandem-and-ovoid brachytherapy.

Katherina G Cortes1, Karoline Kallis1, Aaron Simon1

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

Brachytherapy
|May 13, 2022
PubMed
Summary

A novel convolution neural network (CNN) system accurately predicts radiation doses for cervical brachytherapy, improving treatment planning. This knowledge-based approach enhances quality control and provides data-driven guidance for radiation oncology.

Keywords:
Cervical cancerConvolutional neural networkDeep learningKnowledge-based dose estimationKnowledge-based planningTandem and ovoids

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Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Artificial Intelligence in Medicine

Background:

  • Cervical brachytherapy requires precise radiation dose delivery to maximize tumor control while minimizing toxicity.
  • Accurate dose prediction is crucial for optimizing treatment plans and ensuring patient safety.

Purpose of the Study:

  • To develop a knowledge-based dose prediction system using a 3D U-NET convolution neural network (CNN).
  • To apply this system for cervical brachytherapy treatments utilizing a tandem-and-ovoid applicator.

Main Methods:

  • A 3D U-NET CNN was trained on 395 patient cases for voxel-wise dose prediction.
  • Model accuracy was assessed using dose differences and isodose dice similarity coefficients.
  • Dose-Volume Histogram (DVH) metrics (HRCTV D90%, bladder/rectum/sigmoid D2cc) were evaluated for prediction accuracy.

Main Results:

  • Voxel-wise dose prediction accuracy showed minimal mean differences across various dose ranges and organs-at-risk.
  • Isodose dice similarity coefficients indicated high agreement between predicted and actual dose distributions (0.94-0.87 in test sets).
  • DVH metric predictions demonstrated high accuracy with small mean errors and standard deviations for HRCTV, bladder, rectum, and sigmoid.

Conclusions:

  • 3D knowledge-based dose predictions offer reliable voxel-level and DVH metric estimates.
  • The system can be utilized for treatment plan quality control in cervical brachytherapy.
  • This data-driven approach can guide the development of more effective radiation therapy plans.