Developing AI-Derived Multilevel Risk Scores for Oral Cavity and Oropharyngeal Cancer Patients

DISC Laboratory

Unraveling the relationship between social factors and health outcomes in oral and oropharyngeal cancer

Contact PI/ Project Leader: Dr. Shama Karanth

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Project Overview

Each year, oral cavity and oropharyngeal cancers (OCC/OPC) claim the lives of over 12,000 Americans. Despite modest improvements in five-year survival rates, significant gaps remain in timely diagnosis and access to high-quality treatment. These gaps are especially prominent among individuals facing higher levels of socioeconomic risk, people with low incomes, and those living in rural areas. Existing research has not fully characterized how the combined effects of individual and neighborhood-level social factors contribute to treatment and survival outcomes.

To address this research gap, we have established a cohort of thousands of OCC/OPC patients with data on individual and neighborhood-level social factors (economic stability, education access, healthcare access and quality, neighborhood-built environment, and social-community context) and treatment outcomes. We are using artificial intelligence (AI) and machine learning (ML) to develop and validate social risk scores that can be used predict treatment and outcomes. The findings will identify modifiable factors contributing to suboptimal treatment and mortality in OCC/OPC, providing the foundation for scalable, patient-centered interventions that address social risk.

Contact PI/ Project Lead:

  • Dr. Shama Karanth