2023 World Conference on Lung Cancer (Posters)-EP17.01. Lung Cancer Information System: Enabling Precision Oncology through Informatics

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The Lung Cancer Information System being developed by the Winship Cancer Institute of Emory University aims to enable precision oncology through informatics. The initiative seeks to integrate various data types to advance precision oncology. The ultimate goal is to create a comprehensive data model called "The Digital Patient." The system includes components such as patient technology, analytics, translational research, informatics, and data science. It also incorporates data from various sources such as electronic medical records, genomics, imaging, and pathology. Challenges in data management, security, compliance, and collaboration are addressed to support precision medicine. The system leverages multi-omic data, combining different omics analysis to find biomarkers, variants, and tumors efficiently. Integration of data modalities like radiology, histopathology, and genomic information is crucial. The system supports cancer center computation, large language models, artificial intelligence, and biomedical informatics for analysis and mining of different data types. It also focuses on data sharing, governance, compliance, and computation. The system aims to support biomarker research, precision clinical trials, data governance, and inclusion and diversity. The flow of data modeling and ingestion into an integrated environment is described, along with the importance of a comprehensive data model in alignment with mCODE (Minimal Common Oncology Data Elements). The system delivers analytic capabilities, visual cohort dashboards, and extensible analyses for researchers. It also enables the review of image data through deep learning models. The lack of comprehensive real-world evidence (RWE) data models is identified as a challenge in precision medicine. The collaboration between academic centers, industry, biotech, and government is considered crucial for success. The goal is to facilitate the identification of clinical, imaging, and multi-omic variables that warrant further investigation in lung cancer research and to drive impactful research collaborations to improve patient outcomes. The project is supported by the National Cancer Institute and involves various teams and collaborators.

Asset Subtitle

Madhusmita Behera

Meta Tag

Speaker Madhusmita Behera

Topic Global Health, Health Services & Health Economics: Digital Solutions

Keywords

Lung Cancer Information System

precision oncology

informatics

data integration

Digital Patient

patient technology

analytics

translational research

electronic medical records

genomics