PRACTICE EVOLUTION: Decentralized Computer-Assisted IHC Image Analysis

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PRACTICE EVOLUTION Decentralized
Computer-Assisted IHC Image Analysis

• Liron Pantanowitz, MD, FCAP
• Director of Pathology Informatics
• Richard C. Friedberg, MD PhD, FCAP
• Chairman, Department of Pathology
• Baystate Health, Springfield, MA
• Tufts University School of Medicine

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Why Are We Doing This?

• Practice Background
• Todays Environment
• Increased technological innovation
• Increased biological information
• Increased clinical demand
• Convergence of two independent long term trends

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Key Trend 1 in the Practice of Anatomic Pathology

• Evolution along Clinical Pathology lines
• Greater concern with analytical precision,
  reproducibility, accuracy, specificity,
  reliability
• Qualitative becoming quantitative
• Stains becoming assays
• Results directly tied to treatment, not just
  prognosis
• Diminishing guild mentality with anointed
  experts
• Examples
• IHC ELISA
• Her2/neu Herceptin

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Key Trend 2 in the Practice of Anatomic Pathology

• Evolution along Radiology/Imaging lines
• Analog images establish the field
• Market technology forces start trend to digital
  imaging
• Initially, scanning of analog images
• Later, digitally acquired images
• Digitalization of images allows new applications
• Significant workload throughput implications
• Examples
• PACS
• Convergence imaging
• Windowing
• Dynamic images
• Telediagnostics

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Expectations

• Eventually
• Every image-based pathologist will use
  computer-assisted analytic tools to assay
  specimens
• Intelligently designed PACS will revolutionize
  pathology workflow
• Increased reliance upon pathology

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Breast Cancer Immunohistochemistry (IHC)

• Determining breast tumor markers (ER, PR
  HER-2/neu) for prognostic predictive purposes
  by IHC /or FISH is the standard of practice.
• IHC score/quantification by manual microscopy is
  currently accepted as the traditional gold
  standard.
• Surgical Pathology workflow involves
• Pre-analytic preparation (e.g. tissue fixation
  processing)
• Analysis (i.e. staining of controls patient
  slides)
• Post-analytical component (e.g. quantification
  reporting)
• Discrepancies between HER2 IHC FISH mainly
  reflect errors in manual interpretation not
  reagent limitations (Bloom Harrington. AJCP
  2004 121620-30).
• Inter- intra-observer differences in scoring
  occur
• Most notably with borderline weakly stained
  cases
• Related to fatigue subjectivity of human
  observers

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Accuracy is Required

• Accuracy the amount by which a measured value
  adheres to a standard.
• The need for precise ER, PR HER2/neu status in
  breast cancer is required to ensure appropriate
  therapeutic intervention.
• Lay press have communicated concerns over
  inaccuracies in breast biomarker testing.
• Threat of having to refer such testing to
  reference laboratories.
• Is computer assisted image analysis (CAIA) a
  better (i.e. more accurate reproducible) method
  for scoring IHC?

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Guidelines

• ASCO/CAP Guideline Recommendations for HER2/neu
  testing in breast cancer (Wolff et al. Arch
  Pathol Lab Med 2007 13118)
• Image analysis can be an effective tool for
  achieving consistent interpretation
• A pathologist must confirm the image analysis
  result
• Image analysis equipment (including optical
  microscopes) must be calibrated, subjected to
  regular maintenance internal QC evaluation
• Image analysis procedures must be validated
• Canadian National Consensus Meeting on HER2/neu
  testing in breast cancer (Hanna et al. Current
  Oncology 2007 14149-53)
• Use of image analysis systems can be useful to
  enhance reproducibility of scoring
• Pathologists must supervise all image analyses
• FDA clearance for CAIA in vitro diagnostic use of
  HER-2/neu, ER, and PR IHC has been obtained by
  several companies

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CAIA vs. Manual ScoreRemmele Schicketanz.
Pathol Res Pract 1993 189862-6

• Subjective grading of slides is a simple, rapid
  and useful method for the determination of tissue
  receptor content and must not be replaced by
  expensive and time-consuming computer-assisted
  image analysis in daily practice.

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Data on CAIA IHC

• Early studies showed CAIA was no better than
  visual analysis
• (Schultz et al. Anal Quant Cytol Histol 1992
  1435-40)
• Few studies have shown that manual CAIA are
  comparable
• (Diaz et al. Ann Diagn Pathol 2004 823-7)
• Most studies found CAIA to be superior to manual
  methods
• (Taylor Levenson. Histopathology 2006
  49411-24 McClelland et al. Cancer Res 1990
  503545-50 Kohlberger et al. Anticancer Res
  1999 192189-93 Wang et al. Am J Clin Pathol
  2001 116495-503 Turner et al. USCAP 2008
  abstract 1694).
• Provides effective qualitative quantitative
  evaluation
• More consistent than manual digital microscopy
• More precise (scan per scan) than pathologists
• One study showed agreement between different CAIA
  systems Chroma Vision ACIS Applied Imaging
  Ariol SL-50
• (Gokhale et al. Appl Immunohistochem Mol Morphol
  2007 15451-5)

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Published Considerations

• Expense of CAIA may be hard to justify where
  volumes are low
• Image analysis frequently requires interactive
  input by the pathologist
• Increased time requirements
• Systems may be discrepant when tumor cells have
  low levels of staining
• Interfering non-specific staining within selected
  areas
• Images must be free from artifacts
• Small amounts of stained tissue can erroneously
  generate lower scores

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CAIA Systems

• ImageJ (NIH developed freeware)
• Adobe Photoshop software
• (Lehr et al J Histochem Cytochem 1997
  451559-65)
• Automated Cellular Imaging System (Chroma Vision)
• Pathiam (BioImagene)
• Applied Imaging Ariol (Gentix Systems)
• Spectrum (Aperio)

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Image Analysis Algorithms

• Object-Oriented Image Analysis (morphology-
  based)
• Involves color normalization, background
  extraction, segmentation, classification
  feature selection
• Separation of tissue elements (e.g. tumor
  epithelium) from background (e.g. stroma) permits
  selection of areas of interest filtering out of
  unwanted areas
• Region of Interest (ROI) is subject to further
  image analysis (computation of diagnostic score)
• Quantification of results

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Digital Algorithm
Courtesy of BioImagene
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Courtesy of BioImagene
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Courtesy of BioImagene
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Validation Implementation at Baystate Health

• Distant medical centers
• Significant breast IHC caseload
• Need to mimic daily practice
• avoid central (single user) image analysis
• Bandwidth limitations
• Whole slide imager availability
• Professional reluctance to read digital images

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Key Components

• Multimedia PC upgrade
• Spot Diagnostic digital cameras for each
  workstation
• Pathiam (BioImagene) web-based application
• Server (Oracle database application image
  file storage)
• Training Validation

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WORKFLOW
CONTROL IHC
PATIENT IHC
FOV ANALYSIS
REPORT GENERATION
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NEED FOR STANDARDIZATION
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Calibrated Workstations
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FOV IHC Analysis

• FFPE breast cases routinely stained for ER, PR
  HER2-neu
• Standardized camera acquisition settings
  (calibration)
• Pathologists (n3) acquired 3-5 FOVs (each at 20x
  Mag.)
• Uniform jpg image file formats used (4 Mb)
• Post-processing image manipulation was avoided
• Control parameter set defined/IHC run
  (default/modified)
• ER/PR nuclear staining analyzed using the Allred
  scoring system (i.e. proportion intensity score
  TS)
• HER-2/neu membranous staining evaluated per
  ASCO/CAP 2007 recommendations (0, 1, 2, 3)
• Manual vs. CAIA comparison tracked (IHC score,
  time problems)
• FISH for HER2/neu obtained on several cases

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ER/PR Correlation (N29)
Bio- marker Concordant Cases Discordant Cases
ER 16 0
ER - 4 2
PR 14 0
PR - 4 3
3 cases
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HER-2/Neu Results (N28)
Manual Scoring
Score 0/1 2 3
0/1 16 1
2 3 1
3 4
CAIA
FISH RESULTS Negative (Ratio 1.04)
Abnormal (Ratio 6.5)
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HER-2/Neu FISH Correlation
Manual Score CAIA Score FISH Result
0 0 Negative (1.06)
0 0 Negative (0.93)
1 1 Negative (1.04)
1 0 Negative (1.00)
1 0 Negative (1.07)
1 0 Negative (1.66)
2 1 Negative (1.04)
3 2 Abnormal (6.5)
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Challenging Cases

• Infiltrating Lobular Carcinoma
  Cytoplasmic Staining

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Lessons Learned

• Decentralized CAIA for IHC designed to mimic
  daily surgical pathology workflow in practice is
  feasible
• Image acquisition requires standardization
• Tissue heterogeneity may impact FOV selection
  (whether biological or due to IHC variation)
• Pathologists must supervise CAIA systems

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Future Prospects

• Adopt virtual workflow-centric systems feasible
  for routine practice (that may potentially show
  better results)
• E.g. Whole slide imaging (WSI) to eliminate the
  need to standardize different systems
• Automatic ROI selection image analysis
• Shortened analysis time
• AP-LIS CAIA system integration
• To improve workflow
• Permit disparate systems to access the same
  digital images case data
• Learning algorithms
• Systems that improve with experience following
  pathologist feedback
• Clinical outcome studies are needed
• In one study, CAIA for ER IHC yielded results
  that did not differ from human scoring against
  patient outcome gold standards (Turbin et al.
  Breast Cancer Res Treat 2007)

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Acknowledgements

• Christopher N. Otis, MD
• Giovana M. Crisi, MD
• Andrew Ellithorpe, MHS
• Peter Marquis, BA
• BioImagene

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