New University of Cincinnati Cancer Center research published in the Journal of Statistical Theory and Practice describes the potential for a more robust and accurate breast cancer survival model that combines statistical methods with a deep learning framework. The goal is to give physicians a better tool to make personalized care decisions for each patient.
How does the new survival model differ from current approaches?
Each case of breast cancer is unique and depends upon many variables, including clinical, biological and genetic factors. Traditional survival models are useful to estimate each patient's risk and expected outcomes, but they cannot fully account for how the complex relationships between these factors affect survival prediction for a given patient.
"We wanted to explore whether combining statistical methods with deep learning could capture these relationships and improve the overall survival prediction," said corresponding author Wedad Alatebi, a doctoral student in the Department of Biostatistics, Health Information and Data Science in UC's College of Medicine. "Deep learning can be described as teaching a computer to recognize complex patterns in patient data."
Coauthor Anand Seth, PhD, said the "fusion" strategy combining multiple statistical approaches allows a much more precise prediction outcome. The deep learning framework allows patients who are seemingly similar to be stratified into more specific risk groups.
"That way, you can individualize therapy for a specific group of patients," said Seth, research manager in the Department of Biostatistics, Health Information and Data Science in UC's College of Medicine.
How does the model account for genetic information?
How did the new model perform on a real-world dataset?
What are the next steps for the research?
Alatebi and the team will further evaluate the model using larger and more diverse patient datasets. Their long-term goal is to develop a tool that could help physicians to better identify patient risk and support more personalized treatment and follow-up decisions.
"These deep learning-based models are not meant to replace physicians," she said. "They are tools that could help physicians understand complex patient information and support their decisions. This combination of statistics, machine learning and cancer research has been exciting for me to help us improve how we understand patient outcomes."
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