Longitudinal datasets provide a unique view of the evolution of a disease over both time and space. With the use of datasets such as these, we are building complex data visualization tools that can be easily understood by all researchers, medical providers, and patients. The system that we are setting up will provide a way for various institutions to share and integrate their own data with others in an effort to better understand the dynamics of each disease. The forecasting models that we are building from these sets will help us to answer several questions such as:
1. Is cancer progression predictable? Is it random? Can we quantify the predictability/randomness?
2. How does treatment affect the pathways of progression? How does it affect the speed of progression?
3. What are the most common pathways from liver disease to liver cancer? Who is most at risk?
4. Will metastatic adrenal gland resection in lung cancer provide similar benefits as metastatic liver resection in colon cancer?
Longitudinal Patient Data
Metastatic progression data of a breast cancer cohort in collaboration with Dr. Larry Norton and Dr. Elizabeth Comen from Memorial Sloan Kettering Cancer Center and Dr. Naoto Ueno from MD Anderson Cancer Center
Click for Models
1. A stochastic Markov chain model to describe lung cancer growth and metastasis
2. Spreaders and Sponges Define Metastasis in Lung Cancer: a Markov Chain Monte Carlo Mathematical Model
3. Adrenal Metastases in Lung Cancer: Clinical Implications of a Mathematical Model
4. Entropy, complexity, and Markov diagrams for random walk cancer models
5. Spatiotemporal Progression of Metastatic Breast Cancer: A Markov chain model highlighting the role of early metastatic sites