SBIR Phase I: Advanced Cancer Analytics Platform for Highly Accurate and Scalable Survival Models to Personalize Oncology Strategies

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The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will develop personalized clinical decision-making in cancer care. An estimated 17 million cases of cancer are diagnosed globally each year. Over $90 billion per year is spent in total on cancer-related health care in the U.S., and cancer patients pay over $4 billion out of pocket for health care. Therapeutic strategy selection and clinical trial research targeted to oncology become exponentially complex when unique types of cancer are considered, as well as how they may uniquely impact gender, race, ethnicity, and age of affected populations. The proposed technology will develop advanced bioinformatics models and visualization tools to guide decision-making by oncologists. It will develop and use advanced survival models targeting cancer types, other biological and chemical factors, and patient demographics. This Small Business Innovation Research (SBIR) Phase I project will focus on three objectives. 1) We will develop and validate transfer learning models that leverage large data sets from high-incidence cancer types to improve results of cancer types with sparse data. 2) We will leverage these data in a disease-agnostic platform using a recurrent neural network to account for temporal variation to predict survivability. 3) We will develop visualization tools for clinicians to understand causal relationships. This system will use several innovations: a) Transfer Learning to Scale Available Data: Since cancer survival modeling is limited in many cancer types due to lack of data, we will demonstrate the feasibility of transfer learning in this context. b) Single Recurrent Neural Network: We will implement a recurrent neural network to improve performance and allow a single network to be trained across all cancer types and patient population characteristics. c) Control Feature Mediation Analysis: We will develop accurate survival models with an understanding of the sensitivity to inputs. d) Clinician-Driven Interpretation and Visualization Tools: The framework needs interpretation and visualization features to reduce data into reports easily digestible for clinical decision-making.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.