Transcript Whole Slide Image Based Interpretation of

Whole Slide Image Based
Interpretation of
Immunohistochemistry Stains in
Challenging Prostate Needle
Biopsies
Jeffrey L Fine MD, Jonhan Ho MD, Yukako Yagi, Drazen
Jukic MD PhD, John R Gilbertson MD, Sheldon I Bastacky
MD, Dana M Grzybicki MD PhD, Leslie Anthony, Robb
Wilson, and Anil V Parwani MD PhD
Objectives
• Review whole slide image “landscape”
• Present research project
• Discuss implications arising from the study
Whole Slide Image (WSI)
• Digital facsimile of an entire glass
microscope slide that is viewed by “virtual
microscopy” (VM) software
• WSI are also known as “Virtual Slides” or
“Digital Slides”
The Landscape: WSI Systems
• Systems are currently self contained
– Image acquisition, management, storage, and
utilization (viewing and image analysis)
• Bar code capabilities limited to “reading”
“Obvious” Current Trends
• Decreasing cost for robots and storage
• Increasing speed for robots
– Raw capture speed
– Better “shortcuts”
• Involvement of traditional microscopy players
– Olympus, Zeiss, Nikon
Nascent Trends
• Vendor concern about “workflow” and
“integration”
– How to slip a robot into an existing APLIS and
histology workflow
• Digital pathology workstations
– Monitors (how many and how large)
– Display calibration
Clinically-Oriented Research:
WSI “Clinical Evaluation Group”
• Core affiliated group:
– 4 pathologists; 1 fellow; study coordinator; data
coordinators; imaging technicians; LIS personnel
• Additional pathologists, depending upon study
• Prior studies
– Quality Assurance
– Primary Diagnosis
Current Project
• Goal: Validation of WSI technology for
interpretation of immunohistochemistry (IHC)
stains
• Why?
– UPMC has a centralized IHC laboratory that supports
two academic hospitals
– Electronic distribution (via WSI) could decrease turnaround time for IHC stains
• Better patient care; better service to clinicians
• Decreased healthcare cost (shorter length of stay?)
– WSI could permit automated image analysis of IHC
Traditional workflow
1. Slides stained
2. Slides sorted and gathered
– When a group of stains is complete they can
be shipped to pathologist
3. Slides packed and shipped (courier)
4. Received slides are sorted (again) and
distributed to pathologists
WSI workflow
1. Slides are stained
2. Stained slides are placed into a slide
scanning robot which reads their bar
codes and does the heavy lifting (naming
of file; copying of file to server; etc.)
3. Pathologist views the slides directly over
the internet
4. Glass slides catch up later (optional?)
Prostate Needle Biopsies
• Availability at UPMC Shadyside
• Small set of “usual” IHC stains
– p63; cytokeratin 903; racemase
• Typically signed out in an itemized fashion
– Detailed information about each part or block
• Very challenging IHC interpretation
Cytokeratin 903
“immuno stain”
stains cytoplasm of basal cells
p63
“immuno stain”
stains nuclei of basal cells
(positive = noninvasive)
racemase
(aka AMACR)
“immuno stain”
stains cytoplasm of
glandular cells in prostate
Retrospective Study:
Possible UPMC Environment
• Stage I
– Pathologist has glass H&E which requires IHC
staining for definitive diagnosis
• Stage II
– Pathologist receives WSI of IHC stains and interprets
them
• Stage III
– Glass IHC stains are eventually received and are
checked by the pathologist
• Consensus conferences
Study Design
• 100 cases screened
– 30 difficult foci found
• Each study “case” represents one focus
Technology
• High throughput WSI system
– T2 (Aperio Technologies, Vista, CA, USA)
• Viewing
– Either WWW-based viewer or standalone
viewer (both supplied by WSI vendor)
– “Standard” desktop PCs and microscopes
• Server
– Nothing special (5 users and ~17 – 20 GB)
Data Collection
•
•
•
•
•
•
Stain by stain interpretation (stages 2 – 3)
Overall Diagnosis
Confidence in diagnosis
Time required to make diagnosis (roughly)
Complexity of case
Quality of each slide or image
– Explanations for any defects or shortcomings,
including network speed
Stain Interpretations
• Positive
• Negative
• Can’t Tell (“?”)
– Subcategories to help determine why the
pathologist couldn’t intrepret the stain
Additional Data Collection
• Consensus Diagnosis
– Is mild disagreement OK (atypical vs. cancer)
– How did this compare with original diagnosis
• Any relevant features or notes about case
– Image defects (de-focused areas; color
reproduction; etc.)
– Poor stain quality (not the image’s fault)
Results
Intra-observer Agreement
(Stain Interpretation WSI vs Glass)
• Five Pathologists
• Average Intra-observer agreement
– 80.6% (standard deviation 4.5%)
– Range (75.7% - 86.0%)
Additional Results:
Image Defects
• Pre-existing QC procedure did not detect
several defective images
• Edge Defects
• Rotation Defects
Edge Defect
Rescanned
Rotation
H&E
Immuno
Immuno
H&E
Discussion
Validation
• Does this study validate WSI for interpretation of
IHC stains?
– Pathologists agreed with themselves about 80% of
the time
– Need to find most common sources of disagreement
and see if they can be addressed
• It does highlight several points that need to be
addressed prior to using WSI technology for real
clinical applications
WSI Quality Control
• Each WSI must be checked for common
defects
– This has to be automated eventually
• All slides are not equal
– IHC stains are susceptible to edge defects
– Frozen section slides are hard to get focused
• Image quality standards do not exist yet
for WSI
Modification to WSI Process
• Created a QC procedure (manual)
– Includes solutions/fixes
– Performed by technical support staff
• Documentation of QC activities (aka QA)
– Log files
– Monitor image quality
• Minimize sub-optimal or defective WSI that are
“released” to pathologists
Workflow
• Glass was felt to be faster
• Current pathology systems do not
accommodate WSI
– Look up case in pathology system and click
on available slides
Viewer Limitations
(Most Systems)
• Image navigation
– (slow click and drag)
– cannot rotate image easily (GI; skin; IHC stains)
• Presentation speed is slow
– (pixels are visible until image can load completely)
• Lack of clinical data integration
– (who’s slide is this?)
Study Flaws
• Pathologist subjects
– Informatics fellows; non-GU pathologist; GUtrained sub-specialists
– Almost all pathologists were “informatics”
pathologists
• No standard display or VM software
– 2 options for VM software
– No “gold standard” for monitor/PC
• Loose track of time
Future Work
• Address flaws
– Pathologist selection
– Attention to software and computer used to
participate in study
• Other applications
– Frozen Sections
Conclusions
• This study provided experience in the
attempted production of “clinical grade”
pathology images
– Experience has altered our QC procedures
– Further tools are needed (automation,
integration, etc.)
Conclusions
• If validated (not yet), WSI technology could
permit electronic distribution of IHC stains
– Reduced turn around time could improve service and
reduce healthcare cost
– Centralized laboratories could support multiple
hospitals or pathologist groups
• Automated image analysis could be a future
source of added value
Conclusions
• WSI technology is entering a new phase
– Machines/systems are adequate for small
scale educational and research use
– WSI systems are not yet capable of
integration with existing pathology systems
• This study (when published) can stimulate
vendors and mainstream pathologists
effectively transition to the next level
Acknowledgements
• Rebecca Crowley MD
• Michael Becich MD PhD
• Russ Silowash
• Jon Duboy