Digital Pathology / Image Analysis in Pharmaceutical Discovery and Development- different uses, different concerns
Daniel Weinstock DVM PhD DACVPsanofi aventis U.S., Inc.Bridgewater, N.J. USA
2Pathology Visions, October, 2008 | D. Weinstock
The Digital Image Revolution
Histopathologic assessment (the traditional method):
- glass slides and an optical microscope - subjective semi-quantitative assessment by a pathologist with peer review of results
New approach: - digital image acquisition with computer based image handling and viewing - pathologist driven analysis with generation of objective quantitative, data
(Why) is this such a good thing?
3Pathology Visions, October, 2008 | D. Weinstock
Pathology Applications in a Pharmaceutical Company
Discovery (Research) Target Validation
High Content Screening (HCS)
Animal models
Proof of concept / proof of mechanism studies
Development GLP toxicologic pathology
Biomarker development / validation
Investigational toxicologic pathology
4Pathology Visions, October, 2008 | D. Weinstock
Image Analysis:What kinds of questions?
Characterization of changes in cells / tissues what kinds of changes severity and distribution
Frequency and distribution of a microscopic feature normal versus diseased treated versus untreated
Challenges non-uniformity of samples
Variations in sample source, handling and staining
small sample numbers large sample numbers subtlety of change spectrum of change
5Pathology Visions, October, 2008 | D. Weinstock
Digital Imaging and Image Analysis:Applications, Concerns and Reasons for Use
Repeated measures uniform analysis (application of algorithm)
Quantitative analysis “hard” numbers for diverse scientists (committee decisions)
Large sample numbers prevents “drift”
“Distance” pathology / telepathology remote image sharing collaboration / consultation
GLP principles – image handling, storage and archive
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Digital Images:Acceptance by Pathologists
Quality images data
Speed slide scanning image access, handling field of view, magnification change, etc…
Cost and benefit Integration Ease of use
Ultimate goal: replace glass slide evaluation via microscope with digital image evaluation on computer screens
These issues must be addressed to the satisfaction of the primary users of the technology.
Very good progress to date, but improvement possible.
7Pathology Visions, October, 2008 | D. Weinstock
Image Analysis – Practical Aspects
Team approach needed fusion of engineering and biological skill sets
statisticians needed for complex analytical techniques
Criteria for evaluation modifiable algorithm until final parameters established
Reiterative evaluation and modification of algorithm required
Should be able to review results of each modification
Repeated modification should yield incremental improvements in discrimination
final application of unchangeable algorithm to total image set End point: believable, repeatable, biologically relevant results
e.g. recognition of a nucleus – many ways to do it
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Image Analysis – How To
Digital image files acquired and stored
“working” algorithm applied
1st round results generated can apply to smaller representative image set
evaluation of results and assessment of discrimination
algorithm modification, data set exclusion
Application of 2nd, 3rd, etc… modified algorithms reiterative cycle of modification and data assessment
Final data generation and analysis applied to total set of images
final review of analyzed images for QA is desirable
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Discovery versus Development
Types of questions therapeutic effects (discovery) versus toxicologic effects (development) disease status, model and assay development (discovery)
Types of tissues / experiments species differences
Standard toxicology species (rat, dog, etc.) versus mice (genetically modified, knock-outs, knock-downs, etc.) and other species
group size constraints reagent concerns
Clients (end user) regulatory oriented (development) versus diverse scientific
community (discovery)
GLP compliance essential in Development, not relevant in Discovery
Investigational Toxicologic Pathology – hybrid between the two
10Pathology Visions, October, 2008 | D. Weinstock
11Pathology Visions, October, 2008 | D. Weinstock
Image Analysis Concerns – Tissues
Liver (example tissue)Multiple types of changes possible
Variable combinations of changes – separate, intermixed, etc. Range of severity of each type of change
NecrosisFibrosisInflammationBile duct proliferationothers……
Normal features difficult to differentiateRed blood cellsSinusoids – amount of space affected by degree of exsanguinationKupffer cell – nuclei difficult to discern from inflammatory cells
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Range of Changes in a Lesion
Liver - necrosis
Issues:
Red cells within area of necrosis
Clear spaces within necrosis vs. sinusoids
Pyknotic nuclei vs. Kupffer cell nuclei
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Range of Changes in a Lesion
Liver - bile duct proliferation
Issues
Edge effect.
Differentiation between bile ducts and arterioles.
Relatively uncomplicated change in this field.
14Pathology Visions, October, 2008 | D. Weinstock
Range of Changes in a Lesion
Liver - bile duct proliferation - fibrosis - inflammation
Issues
Differentiation between bile ducts and arterioles.
Complicated by fibrosis and inflammation.
Discrimination between nuclei of inflammatory cells and Kupffer cells
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Range of Changes in a Lesion
Liver - bile duct proliferation - fibrosis - inflammation
Issues
Complex morphology of multiple changes in one focus of interest.
Severity change varies by focus.
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Range of Changes in a Lesion
Liver - necrosis - fibrosis - bile duct proliferation - inflammation
Issues
Similar issues as previous images, but now complicated by multiple contiguous types of changes per field.
17Pathology Visions, October, 2008 | D. Weinstock
Liver - necrosis - bile duct proliferation
Issues
Multiple non contiguous changes.
Bile duct proliferation – uncomplicated.Necrosis – complex morphology in area of change.
Range of Changes in a Lesion
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Image Analysis – “How to…” and “Multiple interactions…”
What’s needed? turn key library with many validated algorithms
can be located distant or local
useful as starting point for further modification
tool box for modification should be local (desktop)
should be user (pathologist / scientist) friendly
easily modified with rapid, repeated application to a test data set format for easy review of results and assessment of
discriminations being made data should be accessible for statistical analysis final results should be biologically relevant
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FAQs – common concerns
What must be done to validate an image analysis algorithm?
What justifies the time and effort investment to develop an image analysis algorithm?
How predictive is a 2 dimensional slice of a tissue (histologic section) for quantification of an effect on a organ? How much sampling is required? What kind of sampling is required? Are we making appropriate comparisons?
What is necessary to power the experiment appropriately?
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Integration of Images and Data
GLP or non-GLP Necessary to be able to associate images with blocks, tissues,
animal identification, treatments, experiments, etc… source information, interface with LIMS (Laboratory Information Management
System) cross reference to lab books
Necessary to be able to associate images with multiple analyses and results
cross reference in reports interface with document generation programs
Storage and retrieval of images and data IS/IT participation essential
searchable (on how many and what criteria?)
image quality / integrity Compression, storage space and location
storage of primary image, annotated images, etc….Trade off: amount of annotation vs. ease of use (data entry time)
potential for retrospective analysis
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Technical Needs
Rapid, automated slide scanning Multiple formats
brightfield
fluorescence
Rapid, seamless change between magnifications Depth perception, polarization? Volumetric determinations?
Pathologist / scientist supervised computer self learning for image analysis
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Other applications
Digital Imaging
- Telepathology - sharing of digital images
Image Analysis - cellular to whole animal
- HCS (High Content Screen)
- Transgenic mice with in vivo light emission (e.g. luciferase)
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Large Scale High Content Screeninge.g. Anti-mitotics
6 hours 18 hourscontrol
What is the relevant measurement?
Discrimination parameters based on experimental observations (data) with appropriate controls is essential.
- Parameters are often not intuitive. - Results must be biologically relevant to mechanism of action.
Morphology varies with time, dose, staining and mechanism of action.
Sophisticated approach with complex analysis (re-analysis) is needed.
24Pathology Visions, October, 2008 | D. Weinstock
Image Analysis: HCS – special issues
Large experiments up to 384 well plates
very large screens, very large data sets
Feature extractions – what, how, etc… Image compression – current use and archive
resources for data storage become important with time
loss of image integrity with compression may be an issue – especially for retrospective analysis
Data normalization inherent variations within an experiment
Data mining – multivariate analysis need for sophisticated statistical analysis – multiple possible
methods
team approach essential
final biological relevance is essential
25Pathology Visions, October, 2008 | D. Weinstock
Whole animal – in vivo Bioimaging
Transgenic animal with luciferase reporter
luciferase (enzyme) is produced in response to specified gene expression
enzyme substrate given intravenously
whole mouse is imaged for in vivo light emission
tissue imaged ex vivo
image analysis used to quantify gene expression based on light emission
Journal of Molecular Endocrinology (2005) 35, 293-304
26Pathology Visions, October, 2008 | D. Weinstock
Evolution of the Process
Technology and applications are in infancy
New, easier, less expensive technology required for widespread acceptance and use
Current investigators will validate the technology for traditional applications
Future investigators who evolve with the technology will likely be ones to define new, unorthodox, innovative applications
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Summary:what a pathologist wants / needs
Digital Images Quality images Rapid manipulations Integrated systems Easy to use
Image analysis Quality data Pathologist / scientist driven Reiterative process for refinement of criteria Easy to use
“You can’t always get what you want…” - Rolling Stones, Hot Rocks, 1964-1971
Consider the constraints of the individual workplace.