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Grant Details

Grant Number: 5UH3CA225021-04 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: 2021


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

A DICOM Framework for Machine Learning and Processing Pipelines Against Real-time Radiology Images.
Authors: Kathiravelu P. , Sharma P. , Sharma A. , Banerjee I. , Trivedi H. , Purkayastha S. , Sinha P. , Cadrin-Chenevert A. , Safdar N. , Gichoya J.W. .
Source: Journal of digital imaging, 2021 08; 34(4), p. 1005-1013.
EPub date: 2021-08-17.
PMID: 34405297
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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
Related Citations

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
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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
Related Citations

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
Related Citations

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
Related Citations

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
Related Citations

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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