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Whole slide imaging information is potentially available for virtually all cancer patients as Pathology studies are, with very few exceptions, carried out for cancer patients. Information extracted from digitized pathology images (Pathomics data) can increasingly be employed to generate precise characterizations of a patient’s cancer – identification and classification of cells and characterization of tumor microenvironment. This information can be used, in the context of clinical, molecular and Radiology information to create imaging biomarkers used to predict outcome and steer treatment; this information can also be employed to clinical decision support systems to improve reproducibility and precision of traditional Pathology reports.

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The IEDM will develop and provide the methods and tools to integrate and analyze detailed morphology and spatially mapped molecular data and to allow researchers to gain crucial insights into their scientific problems with the aid, and further development, of engineering tools including artificial intelligence and machine learning.

The need for deep, quantitative understanding of biomedical systems is crucial to unravel the underlying mechanisms and pathophysiology. Technologies developed by the IEDM, particularly this thrust, will enable researchers to assemble and visualize detailed, multi-scale descriptions of tissue morphologic changes originating from a wide range of microscopy instruments and provide the computational and pattern recognition tools to integrate these descriptions with corresponding genomic, proteomic, glycemic and clinical signatures. Together these capabilities will facilitate and propel research and discovery in a range of pivotal, cutting-edge biomedical projects with the aid of the latest engineering advances in digital technology including machine learning and artificial intelligence.

Pathomics, or the automated quantification of a pathology image-based phenotype, is increasingly seen as a key enabler for precision medicine. Over the past 20 years digital pathology has developed into a rapidly growing field with applications in translational research. Pathomics analyses characterize cells and tissue obtained in pathology studies. The results of a pathomics study is fundamentally different from a pathologist’s report. A pathologist report describes what the pathologist observes when inspecting tissue, while pathomics features provide a quantitative and reproducible characterization of that tissue. Examples of pathomics features include: 1) spatial characterization of tumor and stroma regions, 2) shapes and textures of nuclei, 3) classifications of cell type, and 4) quantitative characterization of lymphocytic infiltration. Pathomics analyses have been shown to provide value in a variety of correlative and prognostic studies.

Quantitative characterization of tumor infiltrating lymphocytes (TILs), for example, is of rapidly increasing importance in precision medicine. With the growth of cancer immunotherapy, these characterizations are likely to be of increasing clinical significance, as understanding each patient’s immune response becomes more important. High densities of TILs correlate with favorable clinical outcomes including longer disease-free survival or improved overall survival (OS) in multiple cancer types. Recent studies further suggest that the spatial context and the nature of cellular heterogeneity within the tumor microenvironment, in terms of the immune infiltrate into the tumor center and invasive margin, are important in cancer prognosis. 

Prognostic factors, most notably the immunoscore, that quantify spatial TIL densities in different tumor regions, have high prognostic value. Hence, assessments of tumor-associated lymphocytes are increasingly important both in the clinical assessment of pathology slides, and in translational research into the role of these lymphocytic populations.

Faculty and other researchers in the Stony Brook Departments of Pathology and Medicine have conducted leading research in understanding cancer molecular biology for pancreatic cancer, urothelial and prostate cancer, breast cancer, and leukemia, as well as colorectal cancer. They are developing diagnostic biomarkers and therapeutic targets, and potentially intervening in these processes through drug discovery.


Faculty Contributors

Euvgenia Alexandrova, Pathology

Eric BrouzesBiomedical Engineering

Chao Chen, Computer Science

Jun Chung , Pathology

Geoff Girnun , Pathology 

John Haley , Pathology

Jingfang Ju , Pathology

Richard Kew , Pathology

Tahsin KurcBiomedical Informatics

Yupo Ma , Pathology

Natalia Marchenko , Pathology

Luis Martinez , Pathology

David Matus, Biochemistry & Cell Biology


Richard Moffitt, Biomedical Informatics

David Montrose , Pathology

Scott Powers , Pathology

Joel Saltz, Biomedical Informatics

Dimitris SamarasComputer Science

I.V. RamakrishnanComputer Science

Kanokporn Rithidech, Pathology

Adam Rosebrock , Pathology

Kenneth Shroyer , Pathology

Flaminia Talos , Pathology/Urology

Patricia Thompson , Pathology

Fusheng WangComputer Science