Computa(onal	
  Pathology:	
  
Research	
  
Joel	
  Saltz	
  MD,	
  PhD	
  
Chair	
  Biomedical	
  Informa(cs	
  Stony	
  ...
Computa(onal	
  Pathology	
  
Research	
  
•  Computa(onal	
  Science	
  –	
  Context	
  
•  High	
  Dimensional	
  Fused	...
Computa(onal	
  Science	
  
Detect and track changes in data during production
Invert data for reservoir properties
Detect and track reservoir changes...
Coupled	
  Ground	
  Water	
  and	
  Surface	
  Water	
  Simula(ons	
  
Multiple codes -- e.g. fluid code, contaminant
tra...
Pete Beckman – Workshop on Big Data and Extreme Scale Computing
Titan	
  –	
  Peak	
  Speed	
  
30,000,000,000,000,000	
  floa(ng	
  
point	
  opera(ons	
  per	
  second!	
  
Pete Beckman...
Computa(onal	
  Pathology:	
  High	
  
Dimensional	
  Fused-­‐Informa(cs	
  
•  Anatomic/func(onal	
  
characteriza(on	
  ...
Correlating Imaging Phenotypes with Genomic
Signatures: Scientific Opportunities
(Imaging Genomics Workshop NCI June 2013)...
Tumor Heterogeneity
Marusyk 2012
Pathology	
  Analy(cal	
  Imaging	
  
•  Provide	
   rich	
   informa(on	
   about	
   morphological	
   and	
  
func(onal...
•  Quantitative Feature Analysis in Pathology: Emory In Silico Center
for Brain Tumor Research (PI = Dan Brat, PD= Joel Sa...
Direct Study of Relationship Between vs
Lee Cooper,
Carlos Moreno
Clustering identifies three
morphological groups•  Analyzed 200 million nuclei from 162 TCGA GBMs (462 slides)
•  Named fo...
Associations
Gene Expression Correlates of GBM with
High Oligo-Astro Ratio
Oligo Related Genes
Myelin Basic Protein
Proteolipoprotein
H...
Microenvironment	
  and	
  Master	
  Regulators	
  
•  Extent	
  of	
  Necrosis	
  Related	
  Expression	
  of	
  
Master	...
Computa(on	
  and	
  Data	
  Management:	
  
Requirements	
  and	
  Challenges	
  
•  Explosion	
  of	
  derived	
  data	
...
Projec(on	
  –	
  2025	
  	
  	
  
•  100K	
  –	
  1M	
  pathology	
  slides/hospital/year	
  
•  2GB	
  compressed	
  per...
HPC:	
  Tools	
  for	
  Image	
  Analysis,	
  Feature	
  
Extrac.on,	
  	
  Machine	
  Learning	
  Pipelines	
  
Large	
  Scale	
  Data	
  Management	
  
Ø Data	
   model	
   capturing	
   mul(-­‐faceted	
   informa(on	
  
including	
...
Spa(al	
  Centric	
  –	
  Pathology	
  Imaging	
  “GIS”	
  
Point	
  query:	
  human	
  marked	
  point	
  	
  
inside	
  ...
MICCAI 2014
BRAIN TUMOR
Classification and Segmentation Challenges
TCGA	
  
TCIA	
  
IMAGING	
  	
  
CHALLENGE	
  
DIGITAL...
Digital	
  Pathology/Brain	
  Tumor	
  
Image	
  Segmenta(on	
  (BRATS)	
  
•  Use	
  data	
  currently	
  available	
  th...
Computa(onal	
  Pathology:	
  
Popula(ons	
  
Suffolk County PPS IT Architecture
Suffolk	
  County	
  
Providers	
  
Suffolk	
  county	
  PPS	
  Master	
  Pa.ent	
  Index...
Suffolk	
  PPS	
  Organiza(onal	
  Structure	
  
for	
  exchange	
  of	
  clinical	
  data	
  and	
  alerts	
  for	
  pa(en...
The Internet of People and
Things
•  Distributed mHealth devices, sensors, point of care
devices, EHRs computers and datab...
Minimize Surprise
•  Evaluate, track, quantify progression of known
disease states
•  Track, evaluate risk factors and car...
Our work at Emory: Find hot spots in readmissions
within 30 days
–  Integrative analysis - crucial lab data role - to
char...
Johns Hopkins Medical
Institutions
Department of Pathology
Johns Hopkins
(1999)
Joel Saltz MD, PhD – Director Pathology In...
Johns Hopkins Medical
Institutions
POCT Anywhere
●  Provide patients with up-to-date clinical data,
interpretations of cli...
Where	
  Does	
  Pathology	
  Fit	
  In?	
  
•  Capture	
  and	
  analysis	
  of	
  laboratory	
  data	
  is	
  Pathology	...
Where	
  does	
  Computa(onal	
  
Pathology	
  Fit	
  In?	
  
•  Machine	
  learning	
  and	
  predic(ve	
  analy(cs	
  al...
Thanks!	
  
Computational Pathology Workshop July 8 2014
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Computational Pathology Workshop July 8 2014

  1. 1. Computa(onal  Pathology:   Research   Joel  Saltz  MD,  PhD   Chair  Biomedical  Informa(cs  Stony   Brook  University   Associate  Director  for  Informa(cs,   Stony  Brook  Cancer  Center  
  2. 2. Computa(onal  Pathology   Research   •  Computa(onal  Science  –  Context   •  High  Dimensional  Fused  Informa(cs   •  Internet  of  People  and  Things  
  3. 3. Computa(onal  Science  
  4. 4. 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  
  5. 5. 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
  6. 6. Pete Beckman – Workshop on Big Data and Extreme Scale Computing
  7. 7. 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
  8. 8. 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              
  9. 9. 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
  10. 10. Tumor Heterogeneity Marusyk 2012
  11. 11. 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
  12. 12. •  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)
  13. 13. Direct Study of Relationship Between vs Lee Cooper, Carlos Moreno
  14. 14. 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
  15. 15. Associations
  16. 16. 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)
  17. 17. Microenvironment  and  Master  Regulators   •  Extent  of  Necrosis  Related  Expression  of   Master  Regulators  of  the  Mesenchymal   Transi(on   Necrosis and C/EBP-β
  18. 18. 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  
  19. 19. 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  
  20. 20. HPC:  Tools  for  Image  Analysis,  Feature   Extrac.on,    Machine  Learning  Pipelines  
  21. 21. 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    
  22. 22. 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
  23. 23. 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  
  24. 24. 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  
  25. 25. Computa(onal  Pathology:   Popula(ons  
  26. 26. 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
  27. 27. 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
  28. 28. 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
  29. 29. 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
  30. 30. 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
  31. 31. 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
  32. 32. 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
  33. 33. 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  ….  
  34. 34. 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    
  35. 35. Thanks!  

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