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Deep learning-based automatic dose optimization for brachytherapy.

Tao Liu1, Shijing Wen1, Siqi Wang2

  • 1Applied Nuclear Technology in Geosciences Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu, 610059, China; Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.

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Summary

For deep learning-based brachytherapy dose prediction, unprocessed dose data yields the best results. Inverse dose optimization further enhances treatment plan quality by significantly reducing organ-at-risk doses.

Keywords:
BrachytherapyDeep learningDose prediction and optimization

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Area of Science:

  • Medical Physics
  • Radiotherapy
  • Artificial Intelligence in Medicine

Background:

  • Deep learning models, such as the 3D U-Net architecture, show promise for predicting dose distributions in brachytherapy (BT).
  • Optimizing dose prediction and treatment planning is crucial for improving patient outcomes in cervical cancer radiotherapy.

Purpose of the Study:

  • To identify the optimal dose processing method for deep learning-based dose prediction in brachytherapy.
  • To evaluate the effectiveness of inverse dose optimization algorithms in enhancing brachytherapy treatment planning quality.

Main Methods:

  • Retrospective analysis of brachytherapy data from 186 cervical cancer patients.
  • Comparison of dose prediction accuracy using unprocessed data versus various normalization techniques (square-root, logarithmic, linear) with a 3D U-Net model.
  • Assessment of predicted dose distributions using Dice Similarity Coefficient (DSC), Conformity Index (CI), and Homogeneity Index (HI).
  • Application of a gradient-based planning optimization (GBPO) algorithm to further refine the best-performing predicted dose for organ-at-risk (OAR) dose reduction.

Main Results:

  • The 3D U-Net model achieved the highest performance metrics (DSC, CI, HI) when using unprocessed dose data for prediction.
  • Gradient-based planning optimization significantly reduced the D1cc and D2cc doses for the bladder, rectum, and sigmoid (p < 0.05).
  • A slight, statistically insignificant increase in small intestine dose was observed post-optimization.

Conclusions:

  • Dose normalization processing is not recommended when utilizing deep learning models like 3D U-Net for brachytherapy dose prediction in cervical cancer.
  • Inverse dose optimization algorithms can effectively improve the quality of brachytherapy treatment plans by refining predicted dose distributions.