Dose-Distribution-Driven PET Image-Based Outcome Prediction (DDD-PIOP): A Deep Learning Study for Oropharyngeal Cancer IMRT Application

Dose-Distribution-Driven PET Image-Based Outcome Prediction (DDD-PIOP): A Deep Learning Study for Oropharyngeal Cancer IMRT Application

PurposeTo develop a deep learning-based AI agent, DDD-PIOP (Dose-Distribution-Driven PET Image Outcome Prediction), for predicting 18FDG-PET image outcomes of oropharyngeal cancer (OPC) in response to intensity-modulated radiation therapy (IMRT).MethodsDDD-PIOP uses pre-radiotherapy 18FDG-PET/CT ima...

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Bibliographic Details
Main Authors: Chunhao Wang, Chenyang Liu, Yushi Chang, Kyle Lafata, Yunfeng Cui, Jiahan Zhang, Yang Sheng, Yvonne Mowery, David Brizel, Fang-Fang Yin
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-08-01
Series:Frontiers in Oncology
Subjects:
Positron Emission Tomography
deep learning
oropharangeal cancer
IMRT (intensity modulated radiation therapy)
outcome prediction
Online Access:https://www.frontiersin.org/article/10.3389/fonc.2020.01592/full

Internet

https://www.frontiersin.org/article/10.3389/fonc.2020.01592/full

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