SMART IMAGING TECHNOLOGIES web-pathology.net

PERSONALIZED CANCER THERAPYINTEGRATION OF KNOWLEDGE

Digital Pathology and Machine Learning for Healthcare Organizations

Personalized Cancer Therapy: Knowledge Path

Personalized Therapy

Medical History and

Personal Information

Genetic Information

Pathology Information

(tumor biomarkers)

Personalized cancer therapy is a treatment strategy centered on the ability to predict which patients are more likely to respond to specific cancer therapies.

This approach is founded upon the idea that tumor biomarkers are associated with patient prognosis and tumor response to therapy.

In addition, patient genetic factors can be associated with drug metabolism, drug response and drug toxicity.

Personalized tumor molecular profiles, tumor disease site and other patient characteristics are then potentially used for determining optimum individualized therapy options.

Source: MD Anderson Cancer Center

Pathology, the “study of disease”, is an essential component for analysis of personalized cancer therapy options

PERSONALIZED CANCER THERAPY

Utilizing Pathology Knowledge: The Challenge

Traditionally, pathology diagnosis is presented in descriptive natural language statements. Often It is verbose professional opinion of human expert with little quantitative information Statistical agreement between human experts is 75%-85%.

Pathology diagnosis is rendered by pathologist observing patterns of cells on tissue slide under the microscope. In order to be useful for comparison and analysis these observations must be:

• Quantified• Objectified

This can be achieved (in theory) by digitizing pathology slide and applying image analysis algorithms to quantify cell pattern expressions.

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Analyzing Pathology Slides: Machine Learning

Machine Learning Neural Networks learn to recognize images in the same way humans do – by example, rather than by formalized “handcrafted features”.

Since 2012 major improvement in visual recognition was achieved with so called deep learning neural networks. Latest generation of Visual Recognition Neural Networks achieve accuracy of recognition of natural objects similar to human observers. This area of technology is experiencing explosive growth.

Using Machine Learning brings number of advantages to visual recognition applications:• No need to formalize complex “handcrafted features”, pathologist

can just point to patterns they need to recognize• No dependency on image analysis engineers (almost)• System can be trained on very large number of samples to achieve

robust recognition• New data samples can be added to model easily to increase

accuracy

PERSONALIZED CANCER THERAPY

Diagnostic Pattern Library: Applications and Benefits

Classified cancer pattern library is valuable digital asset that can be licensed to other parties to train visual recognition and image analysis algorithms.

Visual recognition application can be used to automatically annotate digital pathology slides and link them with the rest of institutional cancer knowledge base. This application can be licensed to third parties to use for the same purposes.

Research and Clinical Applications:• Computer-assisted cancer diagnosis with pre-screening,

suggestive diagnosis options and contextual links to cancer knowledge libraries (similar cases, experts, research, additional tests etc.)

• Data mining and of advanced analytics of historic tissue samples for cancer patients with known outcomes with purpose of building predictive knowledge bases for cancer care and drug discovery

PERSONALIZED CANCER THERAPY

Machine Learning: RequirementsMachine Learning approach to pattern recognition creates new functional requirements for digital pathology software

• Robust visual recognition models need large number of training images which requires more time for annotating than single pathologist can provide. This problem can be solved by utilizing number of pathologists creating annotations for training

Collaborative Training (Crowdsourcing)

• Digital Pathology system should have capability for extracting specially formatted image data sets on demand for training neural networks

Training Data Extraction

• Training of Neural Network requires massive parallel GPU computing power for a short time. This scalable computing power can be economically delivered by scalable cloud infrastructures such as Amazon AWS.

Cloud Deployment

• Digital pathology software should be able to send image areas to neural network application for recognition and visualize responses for user.

API Integration and Visualization Interface

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Our Solution: Pattern Recognition with Machine Learning

• Last generation deep learning convolution networks can identify target tissue patters with 95% accuracy

Deep Learning

• Pathologists can train recognition solution by simply annotating target tissue patters on slides in their workspace

• They can easily set up classes of patterns for identification

Easy Training

• Robust solutions can be trained from multiple slides to identify target tissue patterns reliably across large variety of samples

• Recognition models can be retrained easily if new patterns or different samples should be added

Robust Recognition Models

• Slides in digital archives can be processed automatically for pattern detection and labeled based on findings

• New slides can be analyzed and classified on upload with suggestive classification available when human expert opens the slide

Automatic Processing

• Visualization overlays help quickly locate and review target patterns• Visualization layer provides quantitative information about patterns

Advanced Visualization

• All data is stored in the database and available for search, data mining and analytics Powerful Analytics

Our software can train neural networks and utilize latest deep learning visual recognition solutions from best in class solution providers

PERSONALIZED CANCER THERAPY

Pathologist are located in different places.

Patients may get care at different locations

Accountable Care Model

International Opportunities

Organizations need technology to:• Complete diagnostic workflow seamlessly between different remote locations• Work with different scanners that use different file formats• View slides fast from any location, keep pathologists productive• Access relevant information for slides without LIS• Distribute work to different people, manage workflow and access• Share work and collaborate remotely• Catalog and search information and results• Integrate pathology slides with other patient information

Integration of Pathology Knowledge: Drivers and Needs

INTERGRATION OF KNOWLEDGE

Distributed Workflow

• Slides from multiple remote scanners are automatically uploaded to designated workspaces on central cloud server with user friendly web interface

Pathologist-centric Architecture

• Slides from any scanner are shown in consistent diagnostic view with slide labels and overview

Support for all Scanners

• Application Interface is accessible from any browser / OS with no plugins to install in the browser

“Pure” Web Interface

• System delivers complete case information to pathologists including document attachments

Full Case Information

Our software platform supports seamless diagnostic workflow across geographic boundaries and IT networks.

INTERGRATION OF KNOWLEDGE

Pathologist Productivity

• Pathologist workspace is an integrated “cockpit”, the single place with all tools for completing diagnostic workflow

• Case includes all information needed to render diagnosis • The information fields in workspace are customizable for specific workflow and case use

Integrated Cockpit

• Multiple individual and team workspaces facilitate distribution and transition of cases between team members

Work Spaces

• Configurable notifications alert user when new work arrives

Notifications

• In a few clicks pathologists can create and distribute professional media-rich reports with original images

• Report templates are customizable for specific workflow and case use

Web Reports

• Pathologists can use “canned” text to quickly enter repeatable information

Text Templates

• Flexible tags and attributes help organize information for quick reference and navigation

Tags and Attributes

Our software provides features and interface that make pathologist productive with digital pathology workflow

INTERGRATION OF KNOWLEDGE

Collaboration

• Multiple users can work on the same case at the same time

Multi-Access

• Sharing of slide, case or entire workspace is possible via simple URL• Sharing can be done securely with authorized users only or with larger

groups with simple pre-shared links

Easy Sharing

• Real-time annotations provide rich information instantly visible to multiple viewers at the same time

Rich Annotations

• Report distribution to group of recipients can be done with a single click

• Reports include case data, annotations and original images• PDF or HML format options available• Report distribution is HIPPA compliant

Report Distribution

Our digital pathology solution opens opportunities for productive team-based pathology not possible with glass slides

INTERGRATION OF KNOWLEDGE

Integration: Data Mining and Discovery

• Non SQL flexible indexed database architecture allows integrated storage of different data items across multiple locations

Distributed Database

• Flexible structure allows storing and integrating various data in the single information store

• New data can be added to database structure at any time

Comprehensive Data

• Selection and navigation is possible for any data item in the database• Global search on any data is instant even for millions of items

Instant Search and Navigation

• Data items can be linked with external data sources and knowledge bases such as diagnostic codes, SNOMED classifications or proprietary knowledge bases

Data Linking

We provide instant search, navigation and data mining ability across millions of slides

INTERGRATION OF KNOWLEDGE

Integration: Information Systems

• RESTful API with live examples and templates provide easy integration with third-party applications

Easy API

• Integration with other medical information systems is available via HL7 Integration Engine (Rhapsody by Orion Health)

LIS / EMR Systems

• Third party image analysis application can access images and metadata

Algorithms

• Information in the database can be integrated with other web based knowledge system via standard integration protocols

Knowledge Bases

Our product includes standard industry data exchange protocols and APIs for integration with any third party application

INTERGRATION OF KNOWLEDGE