A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning

With the advancement of treatment modalities in radiation therapy for cancer patients, outcomes have improved, but at the cost of increased treatment plan complexity and planning time. The accurate prediction of dose distributions would alleviate this issue by guiding clinical plan optimization to s...

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Bibliographic Details
Published in:Scientific reports 2019-01, Vol.9 (1), p.1076, Article 1076
Main Authors: Nguyen, Dan, Long, Troy, Jia, Xun, Lu, Weiguo, Gu, Xuejun, Iqbal, Zohaib, Jiang, Steve
Format: Article
Language:English
Subjects:
631/67/1059/485
639/705/1041
639/705/1042
639/705/117
692/4028/67/1059/485
Cancer therapies
Deep Learning
Feasibility Studies
Femur
Humanities and Social Sciences
Humans
Image processing
Male
multidisciplinary
Patients
Prostate cancer
Prostatic Neoplasms - radiotherapy
Radiation therapy
Radiotherapy Planning, Computer-Assisted
Radiotherapy, Intensity-Modulated
Rectum
Science
Science (multidisciplinary)
Segmentation
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Summary:With the advancement of treatment modalities in radiation therapy for cancer patients, outcomes have improved, but at the cost of increased treatment plan complexity and planning time. The accurate prediction of dose distributions would alleviate this issue by guiding clinical plan optimization to save time and maintain high quality plans. We have modified a convolutional deep network model, U-net (originally designed for segmentation purposes), for predicting dose from patient image contours of the planning target volume (PTV) and organs at risk (OAR). We show that, as an example, we are able to accurately predict the dose of intensity-modulated radiation therapy (IMRT) for prostate cancer patients, where the average Dice similarity coefficient is 0.91 when comparing the predicted vs. true isodose volumes between 0% and 100% of the prescription dose. The average value of the absolute differences in [max, mean] dose is found to be under 5% of the prescription dose, specifically for each structure is [1.80%, 1.03%](PTV), [1.94%, 4.22%](Bladder), [1.80%, 0.48%](Body), [3.87%, 1.79%](L Femoral Head), [5.07%, 2.55%](R Femoral Head), and [1.26%, 1.62%](Rectum) of the prescription dose. We thus managed to map a desired radiation dose distribution from a patient’s PTV and OAR contours. As an additional advantage, relatively little data was used in the techniques and models described in this paper.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-018-37741-x