I surveyed the development of Digital Pathology methodology beginning with the 1997 virtual microscope prototype at Hopkins (PMC2233368) to current tools, methods and algorithms designed to display, analyze and classify whole slide imaging data. I will describe the capabilities of current methods, describe how these methods are likely to evolve and how they will be likely to impact Pathology research and practice.
Digital Pathology: Precision Medicine, Deep Learning and Computer Aided Inter...Joel Saltz
In this presentation, I will survey the development of Digital Pathology methodology beginning with the 1997 virtual microscope prototype at Hopkins to current tools, methods and algorithms designed to display, analyze and classify whole slide imaging data. I will describe methods, tools and algorithms to extract information from Pathology images. These tools include ability to traverse whole slide images, segment nuclei, carry out deep learning region classification and characterize relationship between extracted features and morphological structures. I will also describe some of the research efforts that motivate development of these tools, the role Pathomics is playing in precision medicine research as well as the impact of Pathology Informatics on clinical practice and health care quality.
Presentation at the Department of Biomedical Informatics, University Pittsburgh Medical Center, April 27, 2018
Digital Pathology: Precision Medicine, Deep Learning and Computer Aided Inter...Joel Saltz
In this presentation, I will survey the development of Digital Pathology methodology beginning with the 1997 virtual microscope prototype at Hopkins to current tools, methods and algorithms designed to display, analyze and classify whole slide imaging data. I will describe methods, tools and algorithms to extract information from Pathology images. These tools include ability to traverse whole slide images, segment nuclei, carry out deep learning region classification and characterize relationship between extracted features and morphological structures. I will also describe some of the research efforts that motivate development of these tools, the role Pathomics is playing in precision medicine research as well as the impact of Pathology Informatics on clinical practice and health care quality.
Presentation at the Department of Biomedical Informatics, University Pittsburgh Medical Center, April 27, 2018
Digital Pathology at John Hopkins
Practical Research and Clinical Considerations
Alexander Baras
Presented at the Digital Pathology Congress: USA. For more information visit: www.global-engage.com.
Clinical Genomics for Personalized Cancer Medicine: Recent Advances, Challeng...Yoon Sup Choi
I reviewed recent advances, challenges, and opportunities to implement clinical cancer genomics. Case studies of advanced systems, such as Foundation Medicine, MI-ONCOSEQ are introduced for benchmark. A few fundamental limitations to establish personalized oncology are also discussed.
Flow cytometry (FCM) is a technique used to detect and measure physical and chemical characteristics of a population of cells or particles. In this process, a sample containing cells or particles is suspended in a fluid and injected into the flow cytometer instrument.
This is a powerpoint presentation of Immunohistochemistry of lesions of prostate. This presentation will be helpful for postgraduate pathology students and practitioners alike. We are also on youtube. Please visit our channel at https://www.youtube.com/channel/UCwjkzK-YnJ-ra4HMOqq3Fkw
Here in these slides we are going to discuss about the Digital pathology in which we have discuss about the working, role, benefits and requirements of Digital pathology.
Please share this webinar with anyone who may be interested!
Watch all our webinars: https://www.youtube.com/playlist?list=PL4dDQscmFYu_ezxuxnAE61hx4JlqAKXpR
Cancer care is increasingly tailored to individual patients, who can undergo genetic or biomarker testing soon after diagnosis, to determine which treatments have the best chance of shrinking or eliminating tumours.
In this webinar, a pathologist and clinical oncologist discuss:
● how they are using these new tests,
● how they communicate results and treatment options to patients and caregivers, and
● how patients can be better informed on the kinds of tests that are in development or in use across Canada
View the video: https://youtu.be/_Wai_uMQKEQ
Follow our social media accounts:
Twitter - https://twitter.com/survivornetca
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Challenges of FFPE Sample Materials – Where Does Variation in Quantity of Pur...QIAGEN
In this slidedeck, we reveal how to get the most from your FFPE samples. We discuss variability in quantity and purity of DNA purified from FFPE samples manually or with automated procedures, assessed by different quantification and quality control methods. You can also learn more about the QIAxpert system and how it can help you gain reliable quantification of FFPE samples.
Twenty Years of Whole Slide Imaging - the Coming Phase ChangeJoel Saltz
Presentation at Pathology Visions 2017 - https://digitalpathologyassociation.org/2017-pathology-visions-agenda
I will survey the development of Digital Pathology methodology beginning with the 1997 virtual microscope prototype at Hopkins (PMC2233368) to current tools, methods and algorithms designed to display, analyze and classify whole slide imaging data. I will describe the capabilities of current methods, describe how these methods are likely to evolve and how they will be likely to impact Pathology research and practice.
Digital Pathology at John Hopkins
Practical Research and Clinical Considerations
Alexander Baras
Presented at the Digital Pathology Congress: USA. For more information visit: www.global-engage.com.
Clinical Genomics for Personalized Cancer Medicine: Recent Advances, Challeng...Yoon Sup Choi
I reviewed recent advances, challenges, and opportunities to implement clinical cancer genomics. Case studies of advanced systems, such as Foundation Medicine, MI-ONCOSEQ are introduced for benchmark. A few fundamental limitations to establish personalized oncology are also discussed.
Flow cytometry (FCM) is a technique used to detect and measure physical and chemical characteristics of a population of cells or particles. In this process, a sample containing cells or particles is suspended in a fluid and injected into the flow cytometer instrument.
This is a powerpoint presentation of Immunohistochemistry of lesions of prostate. This presentation will be helpful for postgraduate pathology students and practitioners alike. We are also on youtube. Please visit our channel at https://www.youtube.com/channel/UCwjkzK-YnJ-ra4HMOqq3Fkw
Here in these slides we are going to discuss about the Digital pathology in which we have discuss about the working, role, benefits and requirements of Digital pathology.
Please share this webinar with anyone who may be interested!
Watch all our webinars: https://www.youtube.com/playlist?list=PL4dDQscmFYu_ezxuxnAE61hx4JlqAKXpR
Cancer care is increasingly tailored to individual patients, who can undergo genetic or biomarker testing soon after diagnosis, to determine which treatments have the best chance of shrinking or eliminating tumours.
In this webinar, a pathologist and clinical oncologist discuss:
● how they are using these new tests,
● how they communicate results and treatment options to patients and caregivers, and
● how patients can be better informed on the kinds of tests that are in development or in use across Canada
View the video: https://youtu.be/_Wai_uMQKEQ
Follow our social media accounts:
Twitter - https://twitter.com/survivornetca
Facebook - https://www.facebook.com/CanadianSurvivorNet
Pinterest - https://www.pinterest.com/survivornetwork
YouTube - https://www.youtube.com/user/Survivornetca
Challenges of FFPE Sample Materials – Where Does Variation in Quantity of Pur...QIAGEN
In this slidedeck, we reveal how to get the most from your FFPE samples. We discuss variability in quantity and purity of DNA purified from FFPE samples manually or with automated procedures, assessed by different quantification and quality control methods. You can also learn more about the QIAxpert system and how it can help you gain reliable quantification of FFPE samples.
Twenty Years of Whole Slide Imaging - the Coming Phase ChangeJoel Saltz
Presentation at Pathology Visions 2017 - https://digitalpathologyassociation.org/2017-pathology-visions-agenda
I will survey the development of Digital Pathology methodology beginning with the 1997 virtual microscope prototype at Hopkins (PMC2233368) to current tools, methods and algorithms designed to display, analyze and classify whole slide imaging data. I will describe the capabilities of current methods, describe how these methods are likely to evolve and how they will be likely to impact Pathology research and practice.
American Association for Cancer Research Annual Meeting 2022
Analysis of images of routinely acquired tissue specimens promise to provide biomarkers that can be used to predict disease outcome and steer treatment, improve diagnostic reproducibility, and reveal new insights to further advance current human understanding of disease. The advent of AI and ubiquitous high-end computing are making it possible to carry out accurate whole slide image morphological and molecular tissue analyses at cellular and subcellular resolutions. AI methods are can enable exploration and discovery of novel diagnostic biomarkers grounded in prognostically predictive spatial and molecular patterns as well as quantitative assessments of predictive value and reproducibility of traditional morphological patterns employed in anatomic pathology. AI methods may be adapted to help steer treatment through integrative analysis of clinical information along with Pathology, Radiology and molecular data.
Generation and Use of Quantitative Pathology PhenotypeJoel Saltz
Motivation, tools and methods analysis of digital pathology imagery, integration with "omics" and Radiology, use in Precision Medicine. Presentation at the Early Detection Research Network meeting, April 2015, Atlanta GA
2016 Data Commons and Data Science Workshop June 7th and June 8th 2016. Genomic Data Commons, FAIR, NCI and making data more findable, publicly accessible, interoperable (machine readable), reusable and support recognition and attribution
Learning, Training, Classification, Common Sense and Exascale ComputingJoel Saltz
In this talk, I will describe work my group has carried out in development of deep learning methods that target semantic segmentation and object identification tasks in terapixel Pathology datasets and for satellite data. I will describe what we have been able to achieve, how this work can generalize to additional types of problems and will outline how exascale computing could be used to transform and integrate our methods and pipelines. I will then go on to outline broad research program in exascale computing and deep learning that promises to identify common deep learning methods for previously disparate large and extreme scale data tasks.
Integrative Everything, Deep Learning and Streaming DataJoel Saltz
Workshop on Clusters, Clouds, and Data for Scientific Computing, September 6, 2018
The need to create to label information and segment regions in individual sensor data sources and to create synthesizes from multiple disparate data sources span many areas of science, biomedicine and technology. The rapid evolution in sensor technologies – from digital microscopes to UAVs drive requirements in this area. I will describe a variety of use cases, describe technical challenges as well as tools, algorithms and techniques developed by our group and collaborators.
Pathomics Based Biomarkers and Precision MedicineJoel Saltz
Role of Digital Pathology Data Science (Pathomics) in precision medicine. Features from billions or trillions of objects segmented from digital Pathology data can be employed to predict patient outcome and steer treatment.
Presentation at Imaging 2020, Jackson Hole, WY September 2016
Machine Learning and Deep Contemplation of DataJoel Saltz
Spatio temporal data analytics - Generation of Features
1) Sanity Checking and Data Cleaning, 2) Qualitative Exploration, 3) Descriptive Statistics, 4) Classification, 5) Identification of Interesting Phenomena, 6) Prediction, 7) Control and 8)
Save Data for Later (Compression).
Detailed example from Precision Medicine; Pathomics, Radiomics.
Tools to Analyze Morphology and Spatially Mapped Molecular Data - Informatio...Joel Saltz
Description of NCI Information Technology for Cancer Research Project dedicated to 1) development of development of Digital Pathology pipelines, databases, data modeling and visualization methods, 2) support for digital pathology/Radiology/"omics" based precision medicine
Presented at Spring 2015 Information Technology for Cancer Research PI Meeting
Spatio-‐temporal Sensor Integration, Analysis, Classification or Can Exascal...Joel Saltz
Presentation at Clusters, Clouds and Data for Scientific Computing 2014
Integrative analyses of large scale spatio-temporal datasets play increasingly important roles in many areas of science and engineering. Our recent work in this area is motivated by application scenarios involving complementary digital microscopy, radiology and “omic” analyses in cancer research. In these scenarios, the objective is to use a coordinated set of image analysis, feature extraction and machine learning methods to predict disease progression and to aid in targeting new therapies. I will describe tools and methods our group has developed for extraction, management, and analysis of features along with the systems software methods for optimizing execution on high end CPU/GPU platforms. Once having provided our current work as an introduction, I will then describe 1) related but much more ambitious exascale biomedical and non-biomedical use cases that also involve the complex interplay between multi-scale structure and molecular mechanism and 2) concepts and requirements for methods and tools that address these challenges.
Integrative
analyses of large scale spatio-temporal datasets play increasingly important roles in many areas of science and engineering. Our recent work in this area is motivated by application scenarios involving complementary digital microscopy, Radiology and "omic"
analyses in cancer research. In these scenarios, our objective is to use a coordinated set of image analysis, feature extraction and machine learning methods to predict disease progression and to aid in targeting new therapies.
We describe methods
we have developed for extraction, management and analysis of features along with the systems software methods for optimizing execution on high end CPU/GPU platforms. We will also describe biomedical results obtained from these studies and extensions of the
computational methods to broader application areas.
MICCAI - Workshop on High Performance and Distributed Computing for Medical I...Joel Saltz
Keynote talk at HP-MICCAI / MICCAI-DCI 2011, The Joint Workshop on High Performance and Distributed Computing for Medical Imaging, featured at MICCAI 2011, held on September 22nd 2011 in Toronto, Canada
Talk given to the Emory Cancer Control and Population Science Program 2/17/2011 Describing Biomedical Informatics, Integrative Cancer Research, caBIG and CTSA
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
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- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...kevinkariuki227
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
Prix Galien International 2024 Forum ProgramLevi Shapiro
June 20, 2024, Prix Galien International and Jerusalem Ethics Forum in ROME. Detailed agenda including panels:
- ADVANCES IN CARDIOLOGY: A NEW PARADIGM IS COMING
- WOMEN’S HEALTH: FERTILITY PRESERVATION
- WHAT’S NEW IN THE TREATMENT OF INFECTIOUS,
ONCOLOGICAL AND INFLAMMATORY SKIN DISEASES?
- ARTIFICIAL INTELLIGENCE AND ETHICS
- GENE THERAPY
- BEYOND BORDERS: GLOBAL INITIATIVES FOR DEMOCRATIZING LIFE SCIENCE TECHNOLOGIES AND PROMOTING ACCESS TO HEALTHCARE
- ETHICAL CHALLENGES IN LIFE SCIENCES
- Prix Galien International Awards Ceremony
Anti ulcer drugs and their Advance pharmacology ||
Anti-ulcer drugs are medications used to prevent and treat ulcers in the stomach and upper part of the small intestine (duodenal ulcers). These ulcers are often caused by an imbalance between stomach acid and the mucosal lining, which protects the stomach lining.
||Scope: Overview of various classes of anti-ulcer drugs, their mechanisms of action, indications, side effects, and clinical considerations.
Twenty Years of Whole Slide Imaging - the Coming Phase Change
1. Twenty Years of Whole Slide Imaging - the
Coming Phase Change
Joel Saltz MD, PhD
Chair and Professor Department of Biomedical Informatics
Professor Department of Pathology
Stony Brook Medicine
Pathology Visions, October 2, 2017
11. 1999
DARPA – HUBS Project
Joel Saltz, Mike Becich and David Foran
• Exploration of full slide
digitized datasets
• Classification of
hematologic malignancies
• Content Based Image
Retrieval
• Remotely steered
microscope
13. Pathology Image Driven Decision Support
• Improve reproducibility in traditional Pathology
assessments (e.g. Gleason grade, NSCLC subtypes)
• Precise scoring of well known criteria ( tumor
infiltrating lymphocytes, mitoses and IHC staining)
• Development of novel computational methods to
employ Pathology image information to predict
response to cancer treatment and outcomes.
15. Neuroblastoma Classification
FH: favorable histology UH: unfavorable histology
CANCER 2003; 98:2274-81
<5 yr
Schwannian
Development
≥50%
Grossly visible Nodule(s)
absent
present
Microscopic
Neuroblastic
foci
absent
present
Ganglioneuroma
(Schwannian stroma-dominant)
Maturing subtype
Mature subtype
Ganglioneuroblastoma, Intermixed
(Schwannian stroma-rich)
FH
FH
Ganglioneuroblastoma, Nodular
(composite, Schwannian stroma-rich/
stroma-dominant and stroma-poor) UH/FH*
Variant forms*
None to <50%
Neuroblastoma
(Schwannian stroma-poor)
Poorly differentiated
subtype
Undifferentiated
subtype
Differentiating
subtype
Any age UH
≥200/5,000 cells
Mitotic & karyorrhectic cells
100-200/5,000 cells
<100/5,000 cells
Any age
≥1.5 yr
<1.5 yr
UH
UH
FH
≥200/5,000 cells
100-200/5,000 cells
<100/5,000 cells
Any age UH
≥1.5 yr
<1.5 yr
≥5 yr
UH
FH
UH
FH
16. Multi-Scale Machine Learning Based Shimada
Classification System
• Background Identification
• Image Decomposition (Multi-
resolution levels)
• Image Segmentation
(EMLDA)
• Feature Construction (2nd
order statistics, Tonal
Features)
• Feature Extraction (LDA) +
Classification (Bayesian)
• Multi-resolution Layer
Controller (Confidence
Region)
No
Yes
Image Tile
Initialization
I = L
Background? Label
Create Image I(L)
Segmentation
Feature Construction
Feature Extraction
Classification
Segmentation
Feature Construction
Feature Extraction
Classifier Training
Down-sampling
Training Tiles
Within Confidence
Region ?
I = I -1
I > 1?
Yes
Yes
No
No
TRAINING
TESTING
20. Digital Pathology as Precision Medicine
• Statistical analyses and machine learning to link Radiology/Pathology
features to “omics” and outcome biological phenomena
• Image analysis and deep learning methods to extract features from
images
• Support queries against ensembles of features extracted from multiple
datasets
• Identify and segment trillions of objects – nuclei, glands, ducts, nodules,
tumor niches
• Analysis of integrated spatially mapped structural/”omic” information to
gain insight into cancer mechanism and to choose best intervention
21. Quantitative Feature Analysis in Pathology: Emory In Silico
Center for Brain Tumor Research (PI = Dan Brat, PD= Joel
Saltz) 2009 - 2013
22. Using TCGA Data to Study
Glioblastoma
Diagnostic Improvement
Molecular Classification
Predictors of Progression
24. Oligodendroglioma Astrocytoma
Nuclear Qualities
Can we use image analysis of TCGA GBMs TO INFORM
diagnostic criteria based on molecular or clinical endpoints?
Application: Oligodendroglioma Component in GBM
26. Integrative
Morphology/”omics”
Quantitative Feature Analysis in
Pathology: Emory In Silico Center
for Brain Tumor Research (PI =
Dan Brat, PD= Joel Saltz)
NLM/NCI: Integrative
Analysis/Digital Pathology
R01LM011119, R01LM009239
(Dual PIs Joel Saltz, David Foran)
Marcus Foundation Grant – Ari
Kaufman, Joel Saltz
28. • Stony Brook, Institute for Systems Biology, MD Anderson, Emory group
• TCGA Pan Cancer Immune Group – led by ISB researchers
• Deep dive into linked molecular and image based characterization of cancer
related immune response
29. Le Hou – Graduate Student
Computer Science
Vu Nguyen– Graduate Student
Computer Science
Anne Zhao – Pathology Informatics
Biomedical Informatics, Pathology
(now Surg Path Fellow SBM)
Raj Gupta – Pathology Informatics
Biomedical Informatics, Pathology
Deep Learning
and Lymphocytes:
Stony Brook
Digital Pathology
Trainee Team
The future of Digital Pathology
30.
31. Importance of Immune System in Cancer Treatment and Prognosis
• Tumor spatial context and cellular heterogeneity are important in cancer
prognosis
• Spatial TIL densities in different tumor regions have been shown to have
high prognostic value – they may be superior to the standard TNM
classification
• Immune related assays used to determine Checkpoint Inhibitor immune
therapy in several cancer types
• Strong relationships with molecular measures of tumor immune response
– results to soon appear in TCGA Pan Cancer Immune group publications
• TIL maps being computed for SEER Pathology studies and will be
routinely computed for data contributed to TCIA archive
• Ongoing study to relate TIL patterns with immune gene expression
groups and patient response
32. Imaging Based TIL Analysis
Workflow
Deep Learning Training, Validation and Prediction
• Algorithm first trained
on image patches
• Several cooperating
deep learning
algorithms generate
heat maps
• Heat maps used to
generate new
predictions
• Companion molecular
statistical data
analysis pipelines
41. Tools to Analyze Morphology and Spatially Mapped
Molecular Data - U24 CA180924
• Specific Aim 1 Analysis pipelines for multi-
scale, integrative image analysis.
• Specific Aim 2: Database infrastructure to
manage and query Pathomics features.
• Specific Aim 3: HPC software that targets
clusters, cloud computing, and leadership
scale systems.
• Specific Aim 4: Develop visualization
middleware to relate Pathomics feature and
image data and to integrate Pathomics image
and “omic” data.
42. TCIA encourages and supports the cancer
imaging open science community by hosting and
managing Findable
Accessible, Interoperable, and Reusable (FAIR)
images and related data.
http://www.cancerimagingarchive.net/
Clark, et al. J Digital Imag 26.6 (2013): 1045-1057.
Cancer Imaging Archive – Integration of Pathology and
Radiology for Community Clinical Studies
43. TCIA sustainment and scalability
Platforms for quantitative imaging informatics in precision
medicine
Prior, Saltz, Sharma -- U24CA215109-01
• Identify quantitative imaging phenotypes across scale through the use of
Radiomic/Pathomic analyses
• Well-curated data for algorithm testing and validation.
• Integrative Radiology/Pathology Image-Omics studies
• Extend TCIA to support its rapidly growing user community and continue
to promote research reproducibility and data reuse in cancer precision
medical research.
45. ITCR Team
Stony Brook University
Joel Saltz
Tahsin Kurc
Yi Gao
Allen Tannenbaum
Erich Bremer
Jonas Almeida
Alina Jasniewski
Fusheng Wang
Tammy DiPrima
Andrew White
Le Hou
Furqan Baig
Mary Saltz
Raj Gupta
Emory University
Ashish Sharma
Adam Marcus
Oak Ridge National
Laboratory
Scott Klasky
Dave Pugmire
Jeremy Logan
Yale University
Michael Krauthammer
Harvard University
Rick Cummings
46. Funding – Thanks!
• This work was supported in part by
U24CA180924, U24CA215109, NCIP/Leidos
14X138 and HHSN261200800001E from the
NCI; R01LM011119-01 and R01LM009239 from
the NLM
• This research used resources provided by the
National Science Foundation XSEDE Science
Gateways program under grant TG-ASC130023
and the Keeneland Computing Facility at the
Georgia Institute of Technology, which is
supported by the NSF under Contract OCI-
0910735.