Supporting the NIDDK Kidney Precision Medicine Project (KPMP):Standing up the U-M Pathology AI / Data Visualization Center and Core Lab - Five years in
retrospectUlysses G. J. Balis, MD, Fellow AIMBE
Professor of Pathology Informatics
KIDNEY PRECISION MEDICINE PROJECT
Histological Interrogation in Tandem with other Data Classes:The Kidney Precision Medicine Project
Atlas of Histology,V. Eroschenko,Wolters Kluwer
Becomes…Matched Clinical Content
DemographicsComorbidities
OutcomeseGFR
ExposuresMedications
Matched Morphological Content
HistologyMolecular Markers
Structural ElementsStructural Ensembles
Matched Molecular Content
Expression PatternsLinkages to Phenotypes
Molec. Def. of DiseaseNovel Targets
Through the use of relatively recent image-query and image analysis tools, histology data may now be promoted to serving as one of the primary starting points for interrogation, and not simply reside in the repository as passive information.
KIDNEY PRECISION MEDICINE PROJECT
Glomerular Cellularity:From 3D image volume to omics
DiabeticReference (sampling)
Angela Sabo
sc/snRNASeq
LMDMetabolomicsProteomics
Digital Pathology
WSI workflow in place
as an enabling step.
1. Operational
2. Autonomous
Quality DetectionUse of simple algorithms
as forcing functions to
drive image quality.
3. Identifying Single FeaturesIterative building of classifier libraries to
exhaustively annotate all histology surface
area, enabling basic image-based search
capability.
4. Multivariate Feature
AnalysisUse of machine learning and deep
learning techniques to convert
knowledge from individual
classifiers to diagnostics concepts
and classes. Ground truth maps
and Segmentation is a key step for
many methodologies!
5. Turn-key Systems Which
Yield Diagnostic
RecommendationsFully autonomous extraction of diagnostic
and prognostic data from multiaxial data
with possible NLP / predicate calculus
assistance in report generation.
A Machine Learning-Centric Foundational Model Leading Towards
Maximal Utilization of WSI Data:
KPMP as a Motivation for Building U-M Pathology’s DVC Core AI Lab
Current Web-Based Prototype High-performance Viewer
Demonstration deployment available at: mydata.kpmp.org
KIDNEY PRECISION MEDICINE PROJECT
Top Level Objectives of the Atlas (from an imaging perspective)
• Images will provide the central anatomic frame of reference, allowing for anchoring of all data types to each other, spatially
• Two key thrusts:• Visualization: for every data type across dimensionality (Resolution and Depth-agnostic)• Analytics: Tools will be tailored to personas
• Will address the need to interrogate various length scales and biological units:• Organ structures• Cells• Sub-cellular spaces
• Image-based analytics will be integral to the atlas
• For future consideration: Power user features:• Macro language for analytical display, and data mining primitives• Python Repository• Jupyter Notebooks
KIDNEY PRECISION MEDICINE PROJECT
Localizing Omics:From transcript to 3D image volume
sc/snRNASeq Imaging
DAPI Phalloidin THPAQP1 MPO CD68 CD3
DAPI Gate
On the need for Ground
Truth Maps…
• Critically Important for
several classes of machine
learning
• Time Consuming to generate
• Requires Subject matter
expertise
• Overall quality can be
degraded by both intra-
observer and inter-observer
annotation variability
• Criteria subject to
interpretation
• Under- and over-sampling
may contribute to both false
negative and false positive
detection, respectively
• Can be an overall
unpleasant and unrewarding
process for the subject
matter expert
Feature Engineering
Quick Example:Micro Metastasis Rapid Annotator
• Useful for detection of rare spatial events
• Can be run as both a pre-signout pipeline and as an interactive detection tool, based upon user-provided predicates
• Real-time performance
Quick Example:Mitotic Figure Counting
• Mitotic figure identification is time consuming
• A pre-screening tool can save pathologist time if
sufficient sensitivity and specific is realized
• Neuropathology and bone & soft tissue services
currently allocate substantial time for this task, with
variable kappa statistics
This is a difficult machine vision task:
Over twenty publications in the biological machine vision space report sensitivities of no greater than 0.88 and AUCs of no greater the and .74 – this is terrible.
With tools now available to the KPMP Digital Visualization Core, AUCs of .98 and better for segmentation and annotation exercises are now possible.
Automated Glomerulus Identification
140 fields of view computed in less than 5 seconds, yielding an AUC of 0.96
Automated Interstitial Region Fractionation
Renal Tubule Vacuolization Classifier
• 16 distinct VIPR spatial pre-filters• Each vector processed through a low-pass
Gaussian pipeline to increase spatial distribution of matching events
• Initial AUC of 0.906 / Final AUC of 0.936, maintained with cross-validation
• VIPR vectors (positive and negative) as below:
+ +
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Educational Tools Linking Histology to Schematic Model Nephrons
VIPR Studio: Live Demonstration
Acknowledgements & Collaborating Groups
• University of Michigan• Jerome Cheng• Ross Smith• Keluo Yao• David McClintock• Jeff Hodgin, MD• Matthias Kretzler• Becky Steck
• Duke University• Laura Barisoni , MD
• University of Oklahoma• Christopher Williams, MD
• Funding Sources:1. 1U2CDK114886-01 NIDDK
Central Hub for Kidney Precision MedicineProject (KPMP)
2. RC2 DK122379-05 NIDDKDevelopment of 21st Century Concepts and Tools for QuantifyingUrethral Failure
Kidney PrecisionMedicine Project
KIDNEY PRECISION MEDICINE PROJECT
