A convolutional neural network approach for IMRT dose distribution prediction in prostate cancer patients

Tomohiro Kajikawa, Noriyuki Kadoya, Kengo Ito, Yoshiki Takayama, Takahito Chiba, Seiji Tomori, Hikaru Nemoto, Suguru Dobashi, Ken Takeda, Keiichi Jingu

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

The purpose of the study was to compare a 3D convolutional neural network (CNN) with the conventional machine learning method for predicting intensity-modulated radiation therapy (IMRT) dose distribution using only contours in prostate cancer. In this study, which included 95 IMRT-treated prostate cancer patients with available dose distributions and contours for planning target volume (PTVs) and organs at risk (OARs), a supervised-learning approach was used for training, where the dose for a voxel set in the dataset was defined as the label. The adaptive moment estimation algorithm was employed for optimizing a 3D U-net similar network. Eighty cases were used for the training and validation set in 5-fold cross-validation, and the remaining 15 cases were used as the test set. The predicted dose distributions were compared with the clinical dose distributions, and the model performance was evaluated by comparison with RapidPlan™. Dose-volume histogram (DVH) parameters were calculated for each contour as evaluation indexes. The mean absolute errors (MAE) with one standard deviation (1SD) between the clinical and CNN-predicted doses were 1.10% ± 0.64%, 2.50% ± 1.17%, 2.04% ± 1.40%, and 2.08% ± 1.99% for D2, D98 in PTV-1 and V65 in rectum and V65 in bladder, respectively, whereas the MAEs with 1SD between the clinical and the RapidPlan™-generated doses were 1.01% ± 0.66%, 2.15% ± 1.25%, 5.34% ± 2.13% and 3.04% ± 1.79%, respectively. Our CNN model could predict dose distributions that were superior or comparable with that generated by RapidPlan™, suggesting the potential of CNN in dose distribution prediction.

Original languageEnglish
Pages (from-to)685-693
Number of pages9
JournalJournal of radiation research
Volume60
Issue number5
DOIs
Publication statusPublished - 2019 Oct 23
Externally publishedYes

Keywords

  • convolutional neural network
  • deep learning
  • dose prediction
  • intensity-modulated radiation therapy
  • prostate cancer
  • radiation therapy

ASJC Scopus subject areas

  • Radiation
  • Radiology Nuclear Medicine and imaging
  • Health, Toxicology and Mutagenesis

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