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Grant Details

Grant Number: 4UH3CA225021-03 Interpret this number
Primary Investigator: Saltz, Joel
Organization: State University New York Stony Brook
Project Title: Methods and Tools for Integrating Pathomics Data Into Cancer Registries
Fiscal Year: 2020


The goal of this project is to enrich SEER registry data with high-quality population-based  biospecimen data in the form of digital pathology, machine learning based classifications and  quantitative pathomics feature sets.  We will create a well-curated repository of high-quality  digitized pathology images for subjects whose data is being collected by the registries.  These  images will be processed to extract computational features and establish deep linkages with  registry data, thus enabling the creation of information-rich, population cohorts containing  objective imaging and clinical attributes.  Specific examples of digital Pathology derived feature  sets include quantification of tumor infiltrating lymphocytes and segmentation and  characterization of cancer or stromal nuclei. Features will also include spectral and spatial  signatures of the underlying pathology.  The scientific premise for this approach stems from  increasing evidence that information extracted from digitized pathology images  (pathomic features) are a quantitative surrogate of what is described in a pathology report. The  important distinction being that these features are quantitative and reproducible, unlike human  observations that are highly qualitative and subject to a high degree of inter- and intra-observer  variability. This dataset will provide, a unique, population-wide tissue based view of cancer,  and dramatically accelerate our understanding of the stages of disease progression, cancer  outcomes, and predict and assess therapeutic effectiveness.   This work will be carried out in collaboration with three SEER registries. We will partner  with The New Jersey State Cancer Registry during the development phase of the project (UG3).  During the validation phase of the project (UH3), the Georgia and Kentucky State Cancer  Registries will join the project. The infrastructure will be developed in close collaboration with  SEER registries to ensure consistency with registry processes, scalability and ability support  creation of population cohorts that span multiple registries. We will deploy visual analytic tools  to facilitate the creation of population cohorts for epidemiological studies, tools to support  visualization of feature clusters and related whole-slide images while providing advanced  algorithms for conducting content based image retrieval. The scientific validation of the  proposed environment will be undertaken through three studies in Prostate Cancer, Lymphoma  and NSCLC, led by investigators at the three sites.


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