Grant Number:	1U01CA220401-01A1 Interpret this number
Primary Investigator:	Cooper, Lee
Organization:	Emory University
Project Title:	Informatics Tools for Quantitative Digital Pathology Profiling and Integrated Prognostic Modeling
Fiscal Year:	2018

PROJECT SUMMARY Accurate biomarker-driven prognostic stratification, response prediction, and cohort enrichment are critical for realizing precision treatment strategies and population health management approaches that optimize quality of life and survival for cancer patients. Genomics holds promise for improving classification and prognostication of malignancies, yet oncology practice continues to rely heavily on immunohistochemistry (IHC) as a fundamental tool due to its practicality and ability to provide protein-level and subcellular localization information. The goal of this proposal is to create an open-source software resource for the quantitative analysis of IHC stained tissues and effective integration of IHC, genomic, and clinical features for cancer classification and prognostication. This proposal builds on our collective experience in computer-assisted analysis of microscopic images (including IHC images), development of machine-learning methods to address the challenges of classification and prognostication with heterogeneous and high-dimensional data, and leadership in collection and large-scale analysis of cancer outcomes involving collaboration with multiple medical centers. This effort for the first time will create tools to integrate quantitative IHC imaging, clinical, and genomic information that will in turn enable the research community to explore strategies for the classification of malignancies and prediction of outcomes. The proposed tools will be developed and extensively validated in close collaboration with clinical, genomic, and digital pathology data from the NCI-supported Lymphoma Epidemiology of Outcomes (LEO) cohort study. The software tools produced by this proposal will enable the characterization of subcellular protein expression in cell nuclei, membranes and cytoplasmic compartments. Spatial features of protein expression heterogeneity, along with patient-level summaries of protein expression will be used to develop machine-learning classifiers for cancer subtypes, using diffuse large b-cell lymphomas as a driving application. Technology for automatic tuning of machine learning algorithms will enable a broad class of clinically and biologically motivated users to utilize these tools in their investigations. We will also provide an interactive dashboard that enables users to integrate genomic and IHC-based features to explore prognostic models of patient survival. These tools will be released and documented under an open-source model, integrated with HistomicsTK (https://histomicstk.readthedocs.io/en/latest/), and available to the broader cancer research community.




A panoptic segmentation dataset and deep-learning approach for explainable scoring of tumor-infiltrating lymphocytes.
Authors: Liu S. , Amgad M. , More D. , Rathore M.A. , Salgado R. , Cooper L.A.D. .
Source: Npj Breast Cancer, 2024-06-28 00:00:00.0; 10(1), p. 52.
EPub date: 2024-06-28 00:00:00.0.
PMID: 38942745
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Tissue Contamination Challenges the Credibility of Machine Learning Models in Real World Digital Pathology.
Authors: Irmakci I. , Nateghi R. , Zhou R. , Vescovo M. , Saft M. , Ross A.E. , Yang X.J. , Cooper L.A.D. , Goldstein J.A. .
Source: Modern Pathology : An Official Journal Of The United States And Canadian Academy Of Pathology, Inc, 2024 Mar; 37(3), p. 100422.
EPub date: 2024-01-06 00:00:00.0.
PMID: 38185250
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Translating prognostic quantification of c-MYC and BCL2 from tissue microarrays to whole slide images in diffuse large B-cell lymphoma using deep learning.
Authors: Tavolara T.E. , Niazi M.K.K. , Feldman A.L. , Jaye D.L. , Flowers C. , Cooper L.A.D. , Gurcan M.N. .
Source: Diagnostic Pathology, 2024-01-19 00:00:00.0; 19(1), p. 17.
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A population-level digital histologic biomarker for enhanced prognosis of invasive breast cancer.
Authors: Amgad M. , Hodge J.M. , Elsebaie M.A.T. , Bodelon C. , Puvanesarajah S. , Gutman D.A. , Siziopikou K.P. , Goldstein J.A. , Gaudet M.M. , Teras L.R. , et al. .
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PMID: 38012314
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Automated Deep Learning-Based Diagnosis and Molecular Characterization of Acute Myeloid Leukemia using Flow Cytometry.
Authors: Lewis J.E. , Cooper L.A.D. , Jaye D.L. , Pozdnyakova O. .
Source: Biorxiv : The Preprint Server For Biology, 2023-09-25 00:00:00.0; , .
EPub date: 2023-09-25 00:00:00.0.
PMID: 37808719
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A population-level computational histologic signature for invasive breast cancer prognosis.
Authors: Amgad M. , Hodge J. , Elsebaie M. , Bodelon C. , Puvanesarajah S. , Gutman D. , Siziopikou K. , Goldstein J. , Gaudet M. , Teras L. , et al. .
Source: Research Square, 2023-05-26 00:00:00.0; , .
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Tissue contamination challenges the credibility of machine learning models in real world digital pathology.
Authors: Irmakci I. , Nateghi R. , Zhou R. , Ross A.E. , Yang X.J. , Cooper L.A.D. , Goldstein J.A. .
Source: Medrxiv : The Preprint Server For Health Sciences, 2023-05-02 00:00:00.0; , .
EPub date: 2023-05-02 00:00:00.0.
PMID: 37205404
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Machine learning classification of placental villous infarction, perivillous fibrin deposition, and intervillous thrombus.
Authors: Goldstein J.A. , Nateghi R. , Irmakci I. , Cooper L.A.D. .
Source: Placenta, 2023 Apr; 135, p. 43-50.
EPub date: 2023-03-15 00:00:00.0.
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Machine learning classification of placental villous infarction, perivillous fibrin deposition, and intervillous thrombus.
Authors: Goldstein J.A. , Nateghi R. , Irmakci I. , Cooper L.A.D. .
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An Automated Pipeline for Differential Cell Counts on Whole-Slide Bone Marrow Aspirate Smears.
Authors: Lewis J.E. , Shebelut C.W. , Drumheller B.R. , Zhang X. , Shanmugam N. , Attieh M. , Horwath M.C. , Khanna A. , Smith G.H. , Gutman D.A. , et al. .
Source: Modern Pathology : An Official Journal Of The United States And Canadian Academy Of Pathology, Inc, 2023 Feb; 36(2), p. 100003.
EPub date: 2023-01-09 00:00:00.0.
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NuCLS: A scalable crowdsourcing approach and dataset for nucleus classification and segmentation in breast cancer.
Authors: Amgad M. , Atteya L.A. , Hussein H. , Mohammed K.H. , Hafiz E. , Elsebaie M.A.T. , Alhusseiny A.M. , AlMoslemany M.A. , Elmatboly A.M. , Pappalardo P.A. , et al. .
Source: Gigascience, 2022-05-17 00:00:00.0; 11, .
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Explainable nucleus classification using Decision Tree Approximation of Learned Embeddings.
Authors: Amgad M. , Atteya L.A. , Hussein H. , Mohammed K.H. , Hafiz E. , Elsebaie M.A.T. , Mobadersany P. , Manthey D. , Gutman D.A. , Elfandy H. , et al. .
Source: Bioinformatics (oxford, England), 2022-01-03 00:00:00.0; 38(2), p. 513-519.
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Learning from crowds in digital pathology using scalable variational Gaussian processes.
Authors: López-Pérez M. , Amgad M. , Morales-Álvarez P. , Ruiz P. , Cooper L.A.D. , Molina R. , Katsaggelos A.K. .
Source: Scientific Reports, 2021-06-02 00:00:00.0; 11(1), p. 11612.
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GestAltNet: aggregation and attention to improve deep learning of gestational age from placental whole-slide images.
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Source: Laboratory Investigation; A Journal Of Technical Methods And Pathology, 2021-03-05 00:00:00.0; , .
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Interactive classification of whole-slide imaging data for cancer researchers.
Authors: Lee S. , Amgad M. , Mobadersany P. , McCormick M. , Pollack B.P. , Elfandy H. , Hussein H. , Gutman D.A. , Cooper L.A. .
Source: Cancer Research, 2020-12-21 00:00:00.0; , .
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Artificial Intelligence and Algorithmic Computational Pathology: Introduction with Renal Allograft Examples.
Authors: Farris A.B. , Vizcarra J. , Amgad M. , Cooper L.A.D. , Gutman D. , Hogan J. .
Source: Histopathology, 2020-11-19 00:00:00.0; , .
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Authors: Sakamoto T. , Furukawa T. , Lami K. , Pham H.H.N. , Uegami W. , Kuroda K. , Kawai M. , Sakanashi H. , Cooper L.A.D. , Bychkov A. , et al. .
Source: Translational Lung Cancer Research, 2020 Oct; 9(5), p. 2255-2276.
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Genome-defined African ancestry is associated with distinct mutations and worse survival in patients with diffuse large B-cell lymphoma.
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Source: Cancer, 2020-08-01 00:00:00.0; 126(15), p. 3493-3503.
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