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

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.