An expandable informatics framework for enhancing central cancer registries with digital pathology specimens, computational imaging tools, and advanced mining capabilities
David J Foran1, Eric B Durbin2, Wenjin Chen3, Evita Sadimin1, Ashish Sharma4, Imon Banerjee4, Tahsin Kurc5, Nan Li4, Antoinette M Stroup6, Gerald Harris6, Annie Gu4, Maria Schymura7, Rajarsi Gupta5, Erich Bremer5, Joseph Balsamo5, Tammy DiPrima5, Feiqiao Wang5, Shahira Abousamra8, Dimitris Samaras8, Isaac Hands9, Kevin Ward10, Joel H Saltz5
1 Center for Biomedical Informatics, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA; Department of Pathology and Laboratory Medicine, Rutgers-Robert Wood Johnson Medical School, Piscataway, NJ, USA
2 Kentucky Cancer Registry, Markey Cancer Center, University of Kentucky, Lexington, KY, USA; Division of Biomedical Informatics, Department of Internal Medicine, College of Medicine, Lexington, KY, USA
3 Center for Biomedical Informatics, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
4 Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
5 Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
6 New Jersey State Cancer Registry, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
7 New York State Cancer Registry, New York State Department of Health, Albany, NY, USA
8 Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
9 Division of Biomedical Informatics, Department of Internal Medicine, College of Medicine, Lexington, KY, USA
10 Georgia State Cancer Registry, Georgia Department of Public Health, Atlanta, GA, USA
David J Foran
Center for Biomedical Informatics, Rutgers Cancer Institute of New Jersey, 195 Little Albany Street, New Brunswick, NJ 08903-2681.
Source of Support: None, Conflict of Interest: None
Background: Population-based state cancer registries are an authoritative source for cancer statistics in the United States. They routinely collect a variety of data, including patient demographics, primary tumor site, stage at diagnosis, first course of treatment, and survival, on every cancer case that is reported across all U.S. states and territories. The goal of our project is to enrich NCI’s Surveillance, Epidemiology, and End Results (SEER) registry data with high-quality population-based biospecimen data in the form of digital pathology, machine-learning-based classifications, and quantitative histopathology imaging feature sets (referred to here as Pathomics features). Materials and Methods: As part of the project, the underlying informatics infrastructure was designed, tested, and implemented through close collaboration with several participating SEER registries to ensure consistency with registry processes, computational scalability, and ability to support creation of population cohorts that span multiple sites. Utilizing computational imaging algorithms and methods to both generate indices and search for matches makes it possible to reduce inter- and intra-observer inconsistencies and to improve the objectivity with which large image repositories are interrogated. Results: Our team has created and continues to expand a well-curated repository of high-quality digitized pathology images corresponding to subjects whose data are routinely collected by the collaborating registries. Our team has systematically deployed and tested key, visual analytic methods to facilitate automated creation of population cohorts for epidemiological studies and tools to support visualization of feature clusters and evaluation of whole-slide images. As part of these efforts, we are developing and optimizing advanced search and matching algorithms to facilitate automated, content-based retrieval of digitized specimens based on their underlying image features and staining characteristics. Conclusion: To meet the challenges of this project, we established the analytic pipelines, methods, and workflows to support the expansion and management of a growing repository of high-quality digitized pathology and information-rich, population cohorts containing objective imaging and clinical attributes to facilitate studies that seek to discriminate among different subtypes of disease, stratify patient populations, and perform comparisons of tumor characteristics within and across patient cohorts. We have also successfully developed a suite of tools based on a deep-learning method to perform quantitative characterizations of tumor regions, assess infiltrating lymphocyte distributions, and generate objective nuclear feature measurements. As part of these efforts, our team has implemented reliable methods that enable investigators to systematically search through large repositories to automatically retrieve digitized pathology specimens and correlated clinical data based on their computational signatures.