Designing Personalized Treatment Plans for Breast Cancer

Breast cancer remains the leading cause of cancer deaths among women around the world. Contemporary treatment for breast cancer is complex and involves highly specialized medical professionals collaborating in a series of information-intensive processes. This poses significant challenges to personalization and customization of treatment plans for individual patients. In this research, we follow the information systems design science paradigm and propose a novel framework for decision support of treatment planning for early stage breast cancer patients undergoing radiotherapy. The core of our framework consists of a predictive model that predicts patient outcome of a treatment plan based on clinical and patient characteristics, and an optimization model that optimizes the treatment plan based on predicted outcomes of different plans. Using a series of simulation experiments, we show that the treatment plans generated from our framework consistently outperform those from the existing practices in balancing the risk of local tumor recurrence and radiation-induced adverse effects, thereby reducing the treatment cost associated with these adverse effects. Our research contributes to the growing literature that examines the potential of healthcare information technologies in delivering cost-effective care. Further, we also contribute to healthcare practices by providing models and tools that have pragmatic value as part of the clinical care delivery system.