Integration of biological and statistical models toward personalized radiation therapy of cancer

Abstract

Radiation Therapy (RT) is one of the most common treatments for cancer. To understand the impact of radiation toxicity on normal tissue, a Normal Tissue Complication Probability (NTCP) model is needed to link RT dose with radiation-induced complications. There are two types of NTCP models: biological and statistical models. Biological models have good generalizability but low accuracy, as they cannot factor in patient-specific information. Statistical models can incorporate patient-specific variables, but may not generalize well across different studies. We propose an integrated model that borrows strength from both biological and statistical models. Specifically, we propose a novel model formulation followed by an efficient parameter estimation algorithm, and investigate statistical properties of the estimator. We apply the integrated model to a real dataset of prostate cancer patients treated with Intensity Modulated RT at the Mayo Clinic Arizona, who are at risk of developing the grade 2+ acute rectal complication. The integrated model achieves an Area Under the Curve (AUC) level of 0.82 in prediction, whereas the AUCs for the biological and statistical models are only 0.66 and 0.76, respectively. The superior performance of the integrated model is also consistently observed over different simulation experiments.

Keywords:

• Model integration
• classification
• radiation toxicity

Additional information

Funding

Funding for this research was provided by the Mayo Clinic and the NSF under grant 1149602.

Notes on contributors

Xiaonan Liu

Xiaonan Liu is a Ph.D. student in industrial engineering at Arizona State University. He received his B.S. from the University of Science and Technology of China in 2013 and M.S. in industrial engineering from Arizona State University in 2015. His research focuses on statistical modeling and machine learning with applications in heathcare and semiconductor manufacturing. He is a student member of IISE, INFORMS, and IEEE.

Mirek Fatyga

Mirek Fatyga is an associate professor in radiation oncology at the Mayo Clinic Arizona. He received his M.S. in physics from the University of Warsaw, Poland and his Ph.D. in nuclear physics from Indiana University. His current research interests include applications of statistical analysis of clinical outcomes in radiation therapy to individualized treatments of cancer patients. He is also serving as a Chair of Physics Division in the Department of Radiation Oncology, Mayo Clinic Arizona. He is a member of the American Association of Physicists in Medicine.

Teresa Wu

Teresa Wu is a professor in industrial engineering at Arizona State University. She received her Ph.D. in industrial engineering from the University of Iowa in 2001. Her current research interests include swarm intelligence, distributed decision support, and health informatics. She is currently serving as the editor-in-chief for IISE Transactions on Healthcare Systems Engineering.

Jing Li

Jing Li is an associate professor in industrial engineering at Arizona State University. She received her B.S. from Tsinghua University in China and an M.A. in statistics and a Ph.D. in industrial and operations engineering from the University of Michigan in 2005 and 2007, respectively. Her research interests are statistical modeling and machine learning for health care applications. She is a recipient of an NSF CAREER award. She is a member of IISE, INFORMS, and IEEE.