Fully automated dose prediction using generative adversarial networks in prostate cancer patients.

ABSTRACT: PURPOSE:Although dose prediction for intensity modulated radiation therapy (IMRT) has been accomplished by a deep learning approach, delineation of some structures is needed for the prediction. We sought to develop a fully automated dose-generation framework for IMRT of prostate cancer by entering the patient CT datasets without the contour information into a generative adversarial network (GAN) and to compare its prediction performance to a conventional prediction model trained from patient contours. METHODS:We propose a synthetic approach to translate patient CT datasets into a dose distribution for IMRT. The framework requires only paired-images, i.e., patient CT images and corresponding RT-doses. The model was trained from 81 IMRT plans of prostate cancer patients, and then produced th

SUBMITTER: Murakami Y 

PROVIDER: S-EPMC7197852 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature