2. Computa(onal
Pathology:
Research
Joel
Saltz
MD,
PhD
Chair
Biomedical
Informa(cs
Stony
Brook
University
Associate
Director
for
Informa(cs,
Stony
Brook
Cancer
Center

3. Computa(onal
Pathology
Research
• Computa(onal
Science
–
Context
• High
Dimensional
Fused
Informa(cs
• Internet
of
People
and
Things

4. Computa(onal
Science

5. Detect and track changes in data during production
Invert data for reservoir properties
Detect and track reservoir changes
Assimilate data & reservoir properties into
the evolving reservoir model
Use simulation and optimization to guide future production
Example:
Oil
Field
Management
–
Joint
ITR
with
Mary
Wheeler,
Paul
Stoffa

6. Coupled
Ground
Water
and
Surface
Water
Simula(ons
Multiple codes -- e.g. fluid code, contaminant
transport code
Different space and time scales
Data from a given fluid code run is used in different
contaminant transport code scenarios

7. Pete Beckman – Workshop on Big Data and Extreme Scale Computing

8. Titan
–
Peak
Speed
30,000,000,000,000,000
floa(ng
point
opera(ons
per
second!
Pete Beckman – Workshop on Big Data and Extreme Scale Computing

9. Computa(onal
Pathology:
High
Dimensional
Fused-­‐Informa(cs
• Anatomic/func(onal
characteriza(on
at
fine
and
gross
level
• Integrate
of
anatomic/
func(onal
characteriza(on,
mul(ple
types
of
“omic”
informa(on,
outcome
• Predict
treatment
outcome,
select,
monitor
treatments
• Integrated
analysis
and
presenta(on
of
observa(ons,
features
analy(cal
results
–
human
and
machine
generated
Ex-­‐vivo
Imaging
Pa.ent
Outcome
In
vivo
imaging
“Omic”
Data

10. Correlating Imaging Phenotypes with Genomic
Signatures: Scientific Opportunities
(Imaging Genomics Workshop NCI June 2013)
Clinical Approach and Use
• Development of imaging+analysis methods to
characterize heterogeneity
• within a tumor at one time point
• evolution over time
• among different tumor types
• Development of imaging metrics that:
• can predict and detect emergence of resistance?
• correlates with genomic heterogeneity?
• correlates with habitat heterogeneity?
• can identify more homogeneous sub-types

11. Tumor Heterogeneity
Marusyk 2012

12. Pathology
Analy(cal
Imaging
• Provide
rich
informa(on
about
morphological
and
func(onal
characteris(cs
• Image
analysis,
feature
extrac(on
on
mul(ple
scales
• Spa(ally
mapped
“omics”
• Mul(ple
microscopy
modali(es
Glass Slides Scanning Whole Slide Images Image Analysis

13. • 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)
• New - NCI: 1U24CA180924-01A1 Tools to Analyze Morphology and
Spatially Mapped Molecular Data (PI=Saltz)

14. Direct Study of Relationship Between vs
Lee Cooper,
Carlos Moreno

15. Clustering identifies three
morphological groups• Analyzed 200 million nuclei from 162 TCGA GBMs (462 slides)
• Named for functions of associated genes:
Cell Cycle (CC), Chromatin Modification (CM),
Protein Biosynthesis (PB)
• Prognostically-significant (logrank p=4.5e-4)
FeatureIndices
CC CM PB
10
20
30
40
50
0 500 1000 1500 2000 2500 3000
0
0.2
0.4
0.6
0.8
1
Days
Survival
CC
CM
PB

16. Associations

17. Gene Expression Correlates of GBM with
High Oligo-Astro Ratio
Oligo Related Genes
Myelin Basic Protein
Proteolipoprotein
HoxD1
Nuclear features most
Associated with Oligo
Signature Genes:
Circularity (high)
Eccentricity (low)

18. Microenvironment
and
Master
Regulators
• Extent
of
Necrosis
Related
Expression
of
Master
Regulators
of
the
Mesenchymal
Transi(on
Necrosis and C/EBP-β

19. Computa(on
and
Data
Management:
Requirements
and
Challenges
• Explosion
of
derived
data
– 105x105
pixels
per
image
– 1
million
objects
per
image
– Hundreds
to
thousands
of
images
per
study
• High
computa(onal
complexity
– Image
analysis,
feature
extrac(on,
machine
learning
pipelines
– Spa(al
queries
involve
heavy
duty
geometric
computa(ons

20. Projec(on
–
2025
• 100K
–
1M
pathology
slides/hospital/year
• 2GB
compressed
per
slide
• 1-­‐10
slides
used
for
Pathologist
computer
aided
diagnosis
• 100-­‐10K
slides
used
in
hospital
Quality
control
• Groups
of
100K+
slides
used
for
clinical
research
studies
-­‐-­‐
Combined
with
molecular,
outcome
data

21. HPC:
Tools
for
Image
Analysis,
Feature
Extrac.on,
Machine
Learning
Pipelines

22. Large
Scale
Data
Management
Ø Data
model
capturing
mul(-­‐faceted
informa(on
including
markups,
annota(ons,
algorithm
provenance,
specimen,
etc.
Ø Support
for
complex
rela(onships
and
spa(al
query:
mul(-­‐level
granulari(es,
rela(onships
between
markups
and
annota(ons,
spa(al
and
nested
rela(onships
Ø Highly
op(mized
spa(al
query
and
analyses
Ø Implemented
in
a
variety
of
ways
including
op(mized
CPU/GPU,
Hadoop/HDFS
and
IBM
DB2

23. Spa(al
Centric
–
Pathology
Imaging
“GIS”
Point
query:
human
marked
point
inside
a
nucleus
.
Window
query:
return
markups
contained
in
a
rectangle
Spa.al
join
query:
algorithm
valida(on/comparison
Containment
query:
nuclear
feature
aggrega(on
in
tumor
regions
Fusheng Wang

24. MICCAI 2014
BRAIN TUMOR
Classification and Segmentation Challenges
TCGA
TCIA
IMAGING
CHALLENGE
DIGITAL
PATHOLOGY
CHALLENGE
Phase
1:
Training
June
20
-­‐
July
31
Phase
2:
Leader
Board
Aug
1
-­‐
Aug
29
Phase
3:
Test
Sept
8
-­‐
Sept
12
For
more
informa+on
about
these
challenges
and
a
related
workshop
on
September
14,
2014
at
MICCAI
in
Boston,
see:
cancerimagingarchive.net
MICCAI:
Medical
Image
Compu.ng
and
Computer
Aided
Interven.ons
-­‐
MICCAI2014.org
TCGA:
The
Cancer
Genome
Atlas
-­‐
cancergenome.nih.gov
TCIA:
The
Cancer
Image
Archive
-­‐
cancerimagingarchive.net

25. Digital
Pathology/Brain
Tumor
Image
Segmenta(on
(BRATS)
• Use
data
currently
available
through
data
archive
resources
of
the
Na(onal
Ins(tutes
of
Health
(NIH),
namely,
the
Cancer
Genome
Atlas
(TCGA)
and
the
Cancer
Image
Archive
(TCIA)
• Digital
Pathology
challenge
will
use
digital
slides
related
to
pa(ents
whose
genomics
data
are
available
from
TCGA.
Similarly,
BRATS
2014
Challenge
will
use
clinical
MRI
image
data,
also
from
the
TCGA
study
subjects.
• Coordinated
Pathology/Radiology
2015
challenge
–
feature
selec.on
and
sta.s.cal/machine
learning
algorithms
to
leverage
Radiology,
Pathology
and
“omic”
features
to
predict
outcome,
response
to
treatment

26. Computa(onal
Pathology:
Popula(ons

27. Suffolk County PPS IT Architecture
Suffolk
County
Providers
Suffolk
county
PPS
Master
Pa.ent
Index
(MPI)
Suffolk
county
PPS
Health
Informa.on
Exchange
(HIE)
E-­‐HNLI
RHIO
(HIE)
Suffolk
County
PPS
Pa.ent
Portal
Stony
Brook
Medicine
Suffolk
County
Big
Data
Plaaorm
Suffolk
County
PPS
Popula.on
Management
Tools
EMRs
or
clinical
Informa.on
System
EMRs
or
clinical
Informa.on
System
eForms
Pa(ent
Wellness
Alerts
Mobile
Monitoring
Pa(ent
Educa(on
Clinical
Records
Collabora(on
Predic(ve
Analy(cs
Event
Engine
Structured
Data
Financial
Data
Legacy
Data
Machine
Learning
NLP
Unstructured
Data
Wearables
Data
Social
Data
Anomaly
Detec(on
Rules
Device
Data
HL7/CCD
Open
Data
Clinical
Data
for
Pa.ent
Care
Jim Murry CIO, Charles Boisey

28. Suffolk
PPS
Organiza(onal
Structure
for
exchange
of
clinical
data
and
alerts
for
pa(ent
visits
through
e-­‐HNLI
Stony
Brook
Medicine
Suffolk
PPS
HIE
(SB
Clinical
Network
IPA,
LLC)
Health
Systems
Hospitals
Community
Health
Centers
Behavioral
Healthcare
Providers
Skilled
Nursing
Facili.es
CHHA’s/
LTHHC
Physician
Groups
Health
Homes
Community
-­‐Based
Agencies
Pharmacies
Those not part of the
Stony Brook Medicine
Network
Other
Healthcare
Providers
Develop-­‐
mental
Disability
Providers
6
Suffolk
county
RHIO
(e-­‐HNLI)
Jim Murry CIO, Charles Boisey

29. The Internet of People and
Things
• Distributed mHealth devices, sensors, point of care
devices, EHRs computers and databases
• Collections of interacting services
• Ubiquitous access to all clinical, laboratory, sensor,
radiology, pathology, treatment data
• Iteratively scan patient information to evaluate
interventions
• Aggregate and iterative mine patient information to
evaluate how to optimize treatment
• Predictive/interactive analytics that anticipate
problems and launch preventive measures
• QC/QA on data and process

30. Minimize Surprise
• Evaluate, track, quantify progression of known
disease states
• Track, evaluate risk factors and carry out diagnostic
screenings where risk factors are significant
• Active learning to formulate correct questions to ask
• When unanticipated catastrophic event occurs, or
disease is first found in advanced state carry out
systematic retrospective population study
– Identify what was different about “surprise”
patients and unaffected cohorts

31. Our work at Emory: Find hot spots in readmissions
within 30 days
– Integrative analysis - crucial lab data role - to
characterize co-morbidities and clinical course
– What fraction of patients with a given principal
diagnosis will be readmitted within 30 days?
– What fraction of patients with a given set of
diseases will be readmitted within 30 days?
– How does severity and time course of co-
morbidities affect readmissions?
EMR Data Analytics: Tools for Clinical
Phenotyping and Population Health

32. Johns Hopkins Medical
Institutions
Department of Pathology
Johns Hopkins
(1999)
Joel Saltz MD, PhD – Director Pathology Informatics
Jim Nichols, MD -- Assistant Professor JHU and
head of POCT Program
Merwyn Taylor, PhD -- Instructor, Informatics
Division, Dept of Pathology, JHU
LaboratoryWithout Walls

33. Johns Hopkins Medical
Institutions
POCT Anywhere
● Provide patients with up-to-date clinical data,
interpretations of clinical data and health
related educational materials
* Integrated archive of patient clinical information,
education materials used by patients, families and
health care providers
● Maintain collection of medical information
gathered at patient’s home, in clinics and
during hospitalizations Alert clinicians about
abnormal values, non-compliance
● Interactive monitoring of POC device

34. Where
Does
Pathology
Fit
In?
• Capture
and
analysis
of
laboratory
data
is
Pathology
• Sensor
data
can
be
thought
of
as
generalized
lab
data
• Clinical
Pathology:
data
quality,
process
control,
sta(s(cal
analyses,
analy(c
vs
biological
varia(on
• Predic(ons
improved
by
including
novel
tests
–
reduc(on
of
“omics”
to
rou(ne
clinical
tes(ng
• Pharmacogenomics
is
just
the
beginning
….

35. Where
does
Computa(onal
Pathology
Fit
In?
• Machine
learning
and
predic(ve
analy(cs
algorithms
applied
to
popula(on
health
• Context
sensi(ve
modeling
of
how
integrated
data
from
mul(ple
sources
influences
probability
distribu(ons
associated
with
different
health
condi(ons
• Applied
popula(on
“omics”
• Integra(on
and
analysis
of
data
from
pa(ent
sensors
• Integra(on
and
analysis
of
spa(al
data
sources

36. Thanks!