Designing Personalized Treatment Plans for Breast Cancer

• Published in: Information systems research 2021-09, Vol.32 (3), p.932-949
• Main Authors: Chen, Wei, Lu, Yixin, Qiu, Liangfei, Kumar, Subodha
• Format: Article
• Language: English
• Subjects: Breast cancer; Cancer therapies; clinical decision support; design science; Health care; Health services; healthcare information technologies; Information systems; Mammography; Medical treatment; Optimization; personalized medicine; Prediction models; Radiation effects; Radiation therapy; Systems design; treatment planning
• ISSN: 1047-7047; 1526-5536
• DOI: 10.1287/isre.2021.1002

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 optimization of treatment plans for individual patients. We propose a novel framework that enables personalization and customization of treatment plans for early stage breast cancer patients undergoing radiotherapy. Using a series of simulation experiments benchmarked with real-world clinical data, we demonstrate that the treatment plans generated from our proposed framework consistently outperform those from the existing practices in balancing the risk of local tumor recurrence and radiation-induced adverse effects. Our research sheds new light on how to combine domain knowledge and patient data in developing effective decision-support tools for clinical use. Although our research is specifically geared toward radiotherapy planning for breast cancer, the design principles of our framework can be applied to the personalization of treatment plans for patients with other chronic diseases that typically involve complications and comorbidities. 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 he