Why: Not all screen-detected cancerous lesions progress to life-threatening disease. To avoid overtreatment, identification of non-progressive lesions is critical.
What: We combine experimental data from human tumors with mathematical modeling to elucidate the evolutionary dynamics of cancer initiation and to identify markers of invasive progression.
How: Multi-regional sequencing assays (genetic and epigenetic), mechanistic mathematical models
Why: Women diagnosed with ductal carcinoma in situ (DCIS) have several management options. To enable informed decision making, quantitative risk predictions are needed.
What: We use model-based approaches to synthesize evidence from observational studies and randomized trials to predict a range of risks for treatment and active surveillance strategies.
How: Mathematical and statistical modeling, Bayesian evidence synthesis, decision analysis
Collaborators: Etzioni lab@ Fred Hutch
Why: Women diagnosed with ductal carcinoma in situ (DCIS) face complex decisions. In addition to guideline-concordant care options, ongoing trials are investigating the viability of active surveillance as an alternative strategy
What: We are developing an interactive web-based decision support tool that helps newly diagnosed DCIS patients navigate the multi-faceted trade-offs between different management options.
How: Risk modeling, knowledge synthesis, uncertainty visualization, patient communication, qualitative research