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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


Abstract

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.



Publications

Deep learning-based image analysis methods for brightfield-acquired multiplex immunohistochemistry images.
Authors: Fassler D.J. , Abousamra S. , Gupta R. , Chen C. , Zhao M. , Paredes D. , Batool S.A. , Knudsen B.S. , Escobar-Hoyos L. , Shroyer K.R. , et al. .
Source: Diagnostic pathology, 2020-07-28; 15(1), p. 100.
EPub date: 2020-07-28.
PMID: 32723384
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AI in Medical Imaging Informatics: Current Challenges and Future Directions.
Authors: Panayides A.S. , Amini A. , Filipovic N.D. , Sharma A. , Tsaftaris S.A. , Young A. , Foran D. , Do N. , Golemati S. , Kurc T. , et al. .
Source: IEEE journal of biomedical and health informatics, 2020 07; 24(7), p. 1837-1857.
PMID: 32609615
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Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor-Infiltrating Lymphocytes in Invasive Breast Cancer.
Authors: Le H. , Gupta R. , Hou L. , Abousamra S. , Fassler D. , Torre-Healy L. , Moffitt R.A. , Kurc T. , Samaras D. , Batiste R. , et al. .
Source: The American journal of pathology, 2020 07; 190(7), p. 1491-1504.
EPub date: 2020-04-08.
PMID: 32277893
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Segmentation and Classification in Digital Pathology for Glioma Research: Challenges and Deep Learning Approaches.
Authors: Kurc T. , Bakas S. , Ren X. , Bagari A. , Momeni A. , Huang Y. , Zhang L. , Kumar A. , Thibault M. , Qi Q. , et al. .
Source: Frontiers in neuroscience, 2020; 14, p. 27.
EPub date: 2020-02-21.
PMID: 32153349
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Two well-differentiated pancreatic neuroendocrine tumor mouse models.
Authors: Wong C. , Tang L.H. , Davidson C. , Vosburgh E. , Chen W. , Foran D.J. , Notterman D.A. , Levine A.J. , Xu E.Y. .
Source: Cell death and differentiation, 2020 01; 27(1), p. 269-283.
EPub date: 2019-06-03.
PMID: 31160716
Related Citations

Multi-objective Parameter Auto-tuning for Tissue Image Segmentation Workflows.
Authors: Taveira L.F.R. , Kurc T. , Melo A.C.M.A. , Kong J. , Bremer E. , Saltz J.H. , Teodoro G. .
Source: Journal of digital imaging, 2019 06; 32(3), p. 521-533.
PMID: 30402669
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Sensitivity analysis in digital pathology: Handling large number of parameters with compute expensive workflows.
Authors: Gomes J. , Barreiros W. , Kurc T. , Melo A.C.M.A. , Kong J. , Saltz J.H. , Teodoro G. .
Source: Computers in biology and medicine, 2019 05; 108, p. 371-381.
EPub date: 2019-03-13.
PMID: 31054503
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Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Images.
Authors: Hou L. , Nguyen V. , Kanevsky A.B. , Samaras D. , Kurc T.M. , Zhao T. , Gupta R.R. , Gao Y. , Chen W. , Foran D. , et al. .
Source: Pattern recognition, 2019 Feb; 86, p. 188-200.
EPub date: 2018-09-13.
PMID: 30631215
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Statistical Analysis of Survival Models Using Feature Quantification on Prostate Cancer Histopathological Images.
Authors: Ren J. , Singer E.A. , Sadimin E. , Foran D.J. , Qi X. .
Source: Journal of pathology informatics, 2019; 10, p. 30.
EPub date: 2019-09-27.
PMID: 31620309
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Unsupervised Domain Adaptation for Classification of Histopathology Whole-Slide Images.
Authors: Ren J. , Hacihaliloglu I. , Singer E.A. , Foran D.J. , Qi X. .
Source: Frontiers in bioengineering and biotechnology, 2019; 7, p. 102.
EPub date: 2019-05-15.
PMID: 31158269
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Recurrence analysis on prostate cancer patients with Gleason score 7 using integrated histopathology whole-slide images and genomic data through deep neural networks.
Authors: Ren J. , Karagoz K. , Gatza M.L. , Singer E.A. , Sadimin E. , Foran D.J. , Qi X. .
Source: Journal of medical imaging (Bellingham, Wash.), 2018 Oct; 5(4), p. 047501.
EPub date: 2018-11-15.
PMID: 30840742
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Adversarial Domain Adaptation for Classification of Prostate Histopathology Whole-Slide Images.
Authors: Ren J. , Hacihaliloglu I. , Singer E.A. , Foran D.J. , Qi X. .
Source: Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2018 Sep; 11071, p. 201-209.
EPub date: 2018-09-26.
PMID: 30465047
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Differentiation among prostate cancer patients with Gleason score of 7 using histopathology whole-slide image and genomic data.
Authors: Ren J. , Karagoz K. , Gatza M. , Foran D.J. , Qi X. .
Source: Proceedings of SPIE--the International Society for Optical Engineering, 2018 Feb; 10579, .
EPub date: 2018-03-06.
PMID: 30662142
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Computer aided analysis of prostate histopathology images to support a refined Gleason grading system.
Authors: Ren J. , Sadimin E. , Foran D.J. , Qi X. .
Source: Proceedings of SPIE--the International Society for Optical Engineering, 2017 Jan; 10133, .
EPub date: 2017-02-24.
PMID: 30828124
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