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Enabling Path of Digital Pathology to Personalized Medicine
Session #74, February 12, 2019
Matthew G. Hanna, MD, Memorial Sloan Kettering Cancer Center
Rajendra Singh, MD, The Mount Sinai Hospital
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Matthew Hanna, MD
Advisory Board, PathPresenter
Rajendra Singh, MD
Founder, PathPresenter
Conflict of Interest
Have no real or apparent conflicts of interest to report
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Agenda
Intro Ecosystem
Educational
Initiatives
Current
Landscape
endQ/A
Digital
Pathology
PathPresenter
FUTURE
AI/ML
Personalized
medicine
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Learning Objectives
Define what is digital pathology and what is its promise
Design an effective program that will train the pathologists of today and
tomorrow in digital pathology
Practice using digital pathology for educational, research, and clinical
purposes
Outline path of pathology to digital pathology to computational pathology
and finally to personalized medicine for patients
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Film photography isnow a niche field
2001 E-book market jumpstarted in 2009.Did not invest in digital solutions.
2011
Failed to identify digital photography as a disruptive technology, had to sell film, patents, scanners to stay alive
2012
Failed to innovate a digital solution.Netflix added streaming services in 2010.
2010
Years Companies Filed for Chapter 11
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Life of Patients’ Specimens
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Digital Pathology Basics
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THE FIVE RIGHTs OF DIGITAL PATHOLOGY
RIGHT PATIENT
RIGHT SLIDE
RIGHT
PATHOLOGIST
RIGHT TIME
“Telepathology”
RIGHT
DIAGNOSIS
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Digital Pathology Ecosystem
Resource: Hanna, M. G., & Pantanowitz, L. (2019). Digital Pathology. In R. Narayan
(Ed.), Encyclopedia of Biomedical Engineering, vol. 2, pp. 524–532. Elsevier.
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Digital Pathology Subsystems
Software
Hardware
Whole slide image
Slide Scanner
Slide scanning
Robotics/slide handling
Optics/Lighting
Tissue detection
WSI viewer
File format
WSI acquisition
WSI repository
Compression
Image analysis
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Digital Pathology ProcessARMS
Acquisition
Retrieval/Storage
Manipulation
Sharing
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1x magnification
20x magnification
40x magnification
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Digital Pathology Benefits
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On-demand Archive/Case management
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Glass slides storage
Move slides from local
To remote
↓ Costs
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Archival glass slide requisitions had a 93% decrease in requests
47,387
186,607
424,901
19,369 20,745
12,336
1,426 -
5,000
10,000
15,000
20,000
25,000
-
50,000
100,000
150,000
200,000
250,000
300,000
350,000
400,000
450,000
2014 2015 2016 2017
Gla
ss S
lid
e R
eq
uests
(n
)
Dig
ita
l S
lid
es
(n
)
Digital slides available Glass slide requests
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Slide storage costs projected savings of $274,000/year
Due to decreased vendor services
(i.e. asset retrieval, storage proximity, labor)
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Image Analysis
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Wang, Khosla, … Beck (2016) https://arxiv.org/abs/1606.05718 Camelyon16 (JAMA, 2017)
Machine Learning
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Big Data meets Pathology
Massive volume of digital data generated from WSI & bioinformatics/molecular data
Critical for personalized medicine, health systems, basic research and “Big data”
Dataset sizes: Computer vision vs. computational pathology
1 whole slide = 100 X 60,000 = 6 billion pixelsFrom Fuchs, 2017
Computational Pathology
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Why is this possible now?
Image datasets
Cognitive algorithms
Fast computing/GPUs
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Challenges
Not always being done with pathologist involvement, mostly being
done in computer science departments
No universal platform to aggregate and share the vast amount of
data generated by pathology across thousands of hospitals, medical
centers and reference laboratories
Available solutions are often scanner specific, lack useful
apps/tools for Pathologists, lack active participation of
pathologists and lack high quality aggregated data
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You would feel comfortable providing primary diagnosis using
digital pathology, with retrieval of glass slides available upon
request
You would feel comfortable providing primary diagnosis using
digital pathology, without availability of glass slides
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What can be accomplished?
• Education
– Trainee/faculty education/CME
• Clinical
– Create efficiencies for pathologists
• Research
– Identify digital prognostic markers
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Education
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Why is there a need?
Lack of open-access to digital slides or are restricted by firewalls to download software
Learning from digital slides is not only having access to digital slides; it should also simulate
current teaching techniques
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CURRENT TEACHING TECHNIQUE
Outdated, Unscalable, Limited
http://www.teachingmicroscopes.com
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Current landscape of Digital Educational Resources
PathXL
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DPA
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CAP Case of the Month
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PathXL
http://www.pathxl.com/pathology-education-tutor
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PathPresenter
• Digital pathology company built by pathologists for pathologists
• Focused on building software to enhance and standardize the learning and teaching experience in pathology
• Provide a multidisciplinary educational environment
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Current PathPresenter Apps for education
• Developed Applications for Public or Institutional Use
MySlideBoxManage your digital slides and
folders.
High YieldLearn and study from hand-picked
cases to prepare for board
examinations, or to brush up on must-
know diagnoses
My PresentationsCreate and manage your presentations
using our extensive Slide library or by
uploading your own slides
QuizSearch, view, or share from thousands
of cases covering all medical
subspecialties
Slide LibrarySearch, view, or share from thousands
of cases covering all medical
subspecialties
Group ChatCreate group with other members and
discuss slides.
AnalysisCreate Analysis and
collaborate to add
annotations and discussions
in Groups
QACross check the Quality of
Diagnosis by assigning to the
expert reviewers
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MySlideBox
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Slide Library
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My Presentations
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High Yield Sections
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Quiz Module
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Steps to Facilitate Education
•Provide an easy to use platform for trainees, faculty, and pathology departments
•Provide high quality content
•Content validated by experts
•Publicized on social media
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Path of Pathology to Personalized Medicine
Bring digital
pathology to
the world
Provide a new
standard medical
presentation
platform
Make
pathologist
comfortable
using digital
pathology
High quality
curated
digital
images
Pathology to
personalized
medicine
Growth in
pCAD
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Use Cases
• PhysiciansReady access to a wealth of data and images, with seamless medical presentations
• InstitutionsBranded platforms with site specific content, monetized to the medical community
• PharmaAgnostic platform coupled with pathology data and AI apps for
real time analysis
LEARN, TEACH, CONSULT, RESEARCH, PUBLISH, CME
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Is it really working?• Usage by country/continent
>162
Countries Users
>62,000
>10,000
Digital Images Apps
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30%
Why this will only increase
Future
PathologistsNumber of
cancer cases 70%
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How will AI affect Pathology Education
De-skilling Access to resources
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Clinical & Research
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Digital Pathology &
Personalized Medicine
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Digital Pathology Slide
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Cancer registrydata
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Clustering corresponded with molecular gene expressions and prognosis
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Survival and Relapse
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https://ai.googleblog.com/2017/03/assisting-pathologists-in-detecting.html
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https://ai.googleblog.com/2018/04/an-augmented-reality-microscope.html
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Assistive screening tools for pathologists
Resource:
https://ai.googleblog.com/2018/04/an-augmented-reality-microscope.html
https://ai.googleblog.com/2018/10/applying-deep-learning-to-metastatic.html
LYNA (LYmph Node Assistant)ARM
(Augmented Reality Microscope)
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Novel Technologies: Multiplex
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(h) autofluorescence
(i) Red = tumor
Green = stroma
(j) Double positivity
yellow = HER2+/Ki-
67+
blue = HER2–/Ki-67–
red = HER2–/Ki-67+
green = HER2+/Ki-67–
Multispectral Imaging. A Review of Its Technical Aspects and
Applications in Anatomic Pathology. J. R. Mansfield
HER2 PR Ki67 ER
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Will AI/New tech Lead to Better Patient Care?
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Digital pathology is increasingly being used and will be a key enabler of personalized medicine.
Many areas of medicine including clinical, education, and research are creating and utilizing digital imaging applications including AI/machine learning.
Pathologists’ current routines is transforming and being impacted by digital pathology.
We need to continue to partner with industry vendors and continue to build applications of WSI for clinical use including primary diagnosis and image analysis to help drive personalized medicine.
Take Home Messages
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The Road to Personalized Medicine
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• Matthew G Hanna, MD
• Rajendra Singh, MD
• Please remember to complete online session evaluation
Questions
