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







Published Version (Please cite this version)


Publication Info

Wang, Chunhao, Chenyang Liu, Yushi Chang, Kyle Lafata, Yunfeng Cui, Jiahan Zhang, Yang Sheng, Yvonne Mowery, et al. (n.d.). Dose-Distribution-Driven PET Image-Based Outcome Prediction (DDD-PIOP): A Deep Learning Study for Oropharyngeal Cancer IMRT Application. Frontiers in Oncology, 10. 10.3389/fonc.2020.01592 Retrieved from

This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.



Chunhao Wang

Assistant Professor of Radiation Oncology
  • Deep learning methods for image-based radiotherapy outcome prediction and assessment
  • Machine learning in outcome modelling
  • Automation in radiotherapy planning and delivery


Kyle Jon Lafata

Thaddeus V. Samulski Associate Professor of Radiation Oncology

Kyle Lafata is the Thaddeus V. Samulski Associate Professor at Duke University in the Departments of Radiation Oncology, Radiology, Medical Physics and Electrical & Computer Engineering. After earning his PhD in Medical Physics in 2018, he completed postdoctoral training at the U.S. Department of Veterans Affairs in the Big Data Scientist Training Enhancement Program. Prof. Lafata has broad expertise in imaging science, digital pathology, computer vision, biophysics, and applied mathematics. His dissertation work focused on the applied analysis of stochastic differential equations and high-dimensional radiomic phenotyping, where he developed physics-based computational methods and soft-computing paradigms to interrogate images. These included stochastic modeling, self-organization, and quantum machine learning (i.e., an emerging branch of research that explores the methodological and structural similarities between quantum systems and learning systems). 

Prof. Lafata has worked in various areas of computational medicine and biology, resulting in 39 peer-reviewed journal publications, 15 invited talks, and more than 50 national conference presentations. At Duke, the Lafata Lab focuses on the theory, development, and application of multiscale computational biomarkers. Using computational and mathematical methods, they study the appearance and behavior of disease across different physical length-scales (i.e., radiomics ~10−3 m, pathomics ~10−6 m, and genomics ~10−9 m) and time-scales (e.g., the natural history of disease, response to treatment). The overarching goal of the lab is to develop and apply new technology that transforms imaging into basic science findings and computational biomarker discovery.


Yunfeng Cui

Associate Professor of Radiation Oncology

Clinical research interests in lung stereotactic body radiotherapy (SBRT), brain stereotactic radiosurgery (SRS), applications of PET imaging in radiation therapy, clinical trial quality assurance.


Yang Sheng

Assistant Professor of Radiation Oncology

My research interest focuses on machine learning and AI application in radiation oncology treatment planning, including prostate cancer, head-and-neck cancer and pancreatic cancer etc.


Yvonne Marie Mowery

Butler Harris Assistant Professor in Radiation Oncology

David Manfield Brizel

Leonard Prosnitz Distinguished Professor of Radiation Oncology

Head and neck cancer has constituted both my principal clinical and research foci since I came to Duke University in 1987. I designed and led a single institution phase 3 randomized clinical trial, initiated in 1989, which was one of the first in the world to demonstrate that radiotherapy and concurrent chemotherapy (CRT) was more efficacious than radiotherapy alone (RT) for treating locally advanced head and neck cancer. CRT has since been established as the non-surgical standard of care for locally advanced head and neck cancer. Reduction of treatment-induced toxicity has also been a major interest of mine because more intensive therapeutic regimens improve efficacy but also increase morbidity. I was the principal investigator of the pivotal multinational randomized trial of amifostine in head and neck cancer, which established proof of principle for pharmacologic radioprotection and led to FDA approval of this drug for protection against radiation induced xerostomia in the treatment of head and neck cancer in 1999. I have also investigated role of recombinant human keratinocyte growth factor KGF in the amelioration of mucositis in both preclinical and clinical settings.
I have an ongoing commitment to the study of in situ tumor physiology and biology. I was one of the initial investigators to initiate direct measurement of tumor oxygenation in humans on a systematic basis. This work revealed a prognostic relationship between tumor hypoxia and local-regional failure and survival in head and neck. Parallel studies of tumor oxygenation in soft tissue sarcomas resulted in the first published literature to demonstrate that hypoxia at a primary tumor site was associated with a significant increase in the risk of subsequent distant metastatic recurrence after completion of treatment. We have also demonstrated that elevated lactate concentrations in head and neck cancer primary tumors is associated with an increased risk of metastatic failure in patients undergoing primary surgical therapy for head and neck cancer.
These interests and accomplishments provide the foundation for my present efforts, which are devoted to the development of functional metabolic imaging, both MRI and PET. We are using imaging to characterize the inherent, non-treatment induced variability of several physiologic and metabolic parameters in both tumors and normal tissues and to measure treatment induced changes in them. The long- term intent is to improve our abilities to predict treatment outcome, to better understand the relationships between physical dose delivery and the risk of toxicity, and to choose more customized treatment strategies for our patients that will increase the chances of cure and decrease the risks of serious side effects

Unless otherwise indicated, scholarly articles published by Duke faculty members are made available here with a CC-BY-NC (Creative Commons Attribution Non-Commercial) license, as enabled by the Duke Open Access Policy. If you wish to use the materials in ways not already permitted under CC-BY-NC, please consult the copyright owner. Other materials are made available here through the author’s grant of a non-exclusive license to make their work openly accessible.