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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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An Expandable Informatics Framework for Enhancing Central Cancer Registries with Digital Pathology Specimens,

David J Foran1,2, Eric B Durbin3,4, Wenjin Chen1

  • 1Center for Biomedical Informatics, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA.

Journal of Pathology Informatics
|February 9, 2022
PubMed
Summary
This summary is machine-generated.

State cancer registries are being enhanced with digital pathology and Pathomics features to improve cancer research. This integration allows for objective analysis of tumor characteristics and patient population stratification.

Keywords:
Cancer registriescomputational imagingdeep-learningdigital pathology

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Area of Science:

  • Oncology
  • Bioinformatics
  • Digital Pathology

Background:

  • Population-based cancer registries collect vital patient data.
  • The National Cancer Institute's (NCI) Surveillance, Epidemiology, and End Results (SEER) program is a key data source.
  • There is a need to integrate high-quality biospecimen data with existing registry information.

Purpose of the Study:

  • To enrich SEER registry data with digital pathology and Pathomics features.
  • To develop computational methods for analyzing histopathology images.
  • To create objective, quantitative imaging feature sets for cancer research.

Main Methods:

  • Designed and implemented informatics infrastructure in collaboration with SEER registries.
  • Utilized computational imaging algorithms for index generation and matching.
  • Developed automated methods for population cohort creation and image analysis.

Main Results:

  • Created a repository of digitized pathology images linked to registry data.
  • Deployed visual analytic methods for automated cohort creation and whole-slide image evaluation.
  • Developed advanced algorithms for content-based retrieval of digitized specimens.

Conclusions:

  • Established analytic pipelines for managing digitized pathology and population cohorts.
  • Developed deep-learning tools for quantitative characterization of tumors and nuclear features.
  • Implemented methods for automated retrieval of specimens and correlated clinical data based on computational signatures.