imaging biomarkers and pacs ontologies bernard gibaud medicis, ltsi, u1099 inserm faculté de...

Imaging biomarkers and PACS ontologies

Bernard Gibaud

MediCIS, LTSI, U1099 InsermFaculté de médecine, Rennes

[email protected]

EuSoMII, 25 September 2014, Warsaw (Poland)

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Goal of the presentation

• To highlight possibilities that future image data sharing infrastructures should provide to optimize the production, sharing, use and reuse if imaging biomarkers

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Overview

• Introduction (definition of imaging biomarkers)

• Part 1. Change of paradigm (led by imaging biomarkers)

• Part 2. Infrastructures for producing, sharing and using imaging biomarkers

• Part 3. Imaging biomarkers modeling using ontologies

• Conclusion 3


Introduction

Definition

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Imaging biomarkers

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Paper prepared by the ESR Subcommittee on Imaging Biomarkers (chairperson: Bernard Van Beers)


Imaging biomarkers

• Definition of biomarkers (Atkinson 2001)*

– « characteristics that are objectively measured and evaluated as indicators of

• normal biological processes, • pathological processes, • pharmaceutical responses to a therapeutic intervention »

• Definition of (quantitative) imaging biomarkers– Derived from medical images– Quantitative, objective, reproducible– « qualified » for specific clinical uses

* Clin Pharmacol & Ther. 2001 Mar;69(3):89-95. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Biomarkers Definitions Working Group.

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Imaging biomarkers

• Used for– Early detection of disease– Staging and grading– Predicting response to treatment– Assessing response to treatment

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Imaging biomarkers

• Of critical importance in research– Focused clinical research (e.g. controlled clinical

trials)– Translational research

• Link/correlate results obtained in various domains• Need to share them at a broad scale

Key aspect of federated imaging biobanks

• Of critical importance in individual patient management (decision criteria)– Diagnosis, prognosis, treatment Key aspect of a structured EHR / tasks planning

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New paradigm led by imaging biomarkers

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General framework

Reality

Human sujectAnimal subject

Specimenetc.

Acquisition

Images

MR imageCT image

PET imageetc.

Imaging biomarkers

Processing

Volume of anatomical structure

Fractal dimensionMean reg. blood

volumeLesion load (MS)

etc.

FactsPlans, etc.

Decision

Diagnosis of ADDiagnosis of MS

Resp to treatmentetc.

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Change of paradigm

• In many clinical situations, obtaining imaging biomarkers may become a primary goal of the imaging procedure

• The clinical goal (clinical question) determines what imaging biomarkers are needed

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Change of paradigm

Scientific question to be

answeredor

Clinical question

Set of required imaging

biomarkers

Decision

Detailed imagingprotocol

ProcessingAcquisition

Detailed subject/spec

imen preparation

12


Consequence (1/2)

• Consensus building– What imaging biomarker in which situation– Conditions of image acquisition (acquisition protocol, image

reconstruction)– Precise definition of image processing

• Work in progress in RSNA’s QIBA initiative– Quantitative Imaging Biomarkers Alliance– QIBA Mission: « Improve the value and practicality of

quantitative biomarkers by reducing variability across devices, patients and time »

– Development of « profiles »13


Consequence (2/2)

• Information modeling– Imaging biomarkers– As part of Structured Reports

• To be managed in clinical PACS and « clinical research supporting systems » – Intelligent task management systems (workflow)– Decision support systems– Quality management systems

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Part 2. Infrastructure for producing, sharing and using

imaging biomarkers

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Infrastructure for producing, sharing and using image biomarkers

• Two aspects– Data structures / information models– System infrastructure

• Two application domains– Care delivery / clinical routine– Clinical and translational research

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Data structures for imaging biomarkers

clinical application• Mostly

– Measurements included in free text reports– and results (e.g. of RECIST) available in

separated unstructured documents (e.g. pdf, DICOM secondary capture)

• Sometimes– Structured reports – DICOM structured reports

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Still often non-structured, because no subsequent automated processing

System infrastructures for imaging biomarkers

clinical application• Production of imaging biomarkers

– General purpose image processing software of PACS WS

– Specific software (plug-in, remote cloud server)

• Sharing of imaging biomarkers– PACS archive

• Use and re-use of imaging biomarkers– Mostly: human reading (e.g. clinician reading for

medical decision, radiologist reading for comparison with baseline)

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Limited automated reuse because not-structured

Data structures for imaging biomarkers

research application• Always

– Measurements in structured files / databases

• Entered via– Structured forms or eCRF (by end-user)– Structured document produced by image processing

software (specific XML structure, but also generic data structures, e.g. XCEDE*)

– DICOM structured reports (sometimes)• e.g. Quantitative Image Informatics in Cancer Research

(QIICR)

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Always structured because of subsequent automated processing (statistical analysis)

* XCEDE: XML-based Clinical and Experimental Data Exchange

System infrastructures for imaging biomarkers

research application

• Production of imaging biomarkers– Mostly: specific image processing software provided by CRO

(accessible as a web app in mono or multi-center studies)

• Sharing of imaging biomarkers– Clinical research system are often limited to collecting and

processing the results according to the study protocol– Sometimes, complementary « open image archives » (OIA)

to disseminate original and derived image data (e.g. ADNI, Human Connectome Project, Cancer Imaging Archive)

• Use and re-use of imaging biomarkers– Little reuse since data considered « non public »– OIA often limited to original image data, no online metadata-

based data query/retrieve

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Part 3. Imaging biomarkers modeling using ontologies

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Semantic web technologies

• Ontologies and ontology languages

• Ontology editors, e.g. Protégé (Stanford Univ.)

• Query languages, e.g. SPARQL (W3C recomm)

• Reasoners, e.g. FaCT++, Pellet, HermiT

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Ontologies

• Definition (informatics and AI)– « a formal, explicit

specification of a shared conceptualization »

(Gruber 1993)

• Two basic aspects– A shared vocabulary– Formal semantics : axioms

expressed in a logical language


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Formal semantics

• Definitions of classes of objects– Taxonomy of classes: subsumption (i.e. « is a » relation)– Instanciation (relation between an individual and a

class)

• Definitions of properties– Taxonomy of properties– Domain and range, inverse properties, etc.

• Processing by a reasoning engine– Assess satisfiability (consistency)– Classification of ontologies– Classification of instances

Reasoners are not application-specific


Ontological modeling of imaging biomarkers

• Note: It is important to distinguish…– Imaging biomarker as result of a measurement– from its role in some medical decision (e.g.

diagnosis, prognostic)

• Main aspects an ontological model of biomarkers should address– Measure– Relation to reality– Provenance– Context

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Imaging biomarkers: measure

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Relies on long and deep experience in metrology

Imaging biomarkers: measure

• Is the result of some measurement process (manual or implemented in image processing software)

• Indirectly involves a physical object under study, and / or a process under study (dynamic process or longitudinal process) in which this object participates

• Concerns a specific quality of this object, or of the process under study)– Note: This quality may be a complex human construct (e.g.

model-based: fractal dimension, gyrification index)

• Values chosen from a predefined scale of measurement– interval, ratio, ordinal, nominal (categorical)

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Imaging biomarkers: relation to reality

• A Measurement of a quality beared by an object

• Or a Measurement of a temporal quality of the process under study

• (Simple) Examples– Volume of hippocampus (in cm3)– Speed of brain atrophy process – neuronal loss (in

cm3/year)– Mean Fractional Anisotropy over uncinate fasciculus

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Imaging biomarkers: provenance

• Execution of a program implementing some conceptual action (e.g., a segmentation)

• Resources used in this execution (e.g., user, date, software tool, platform)

• Input data (e.g., datasets, ROIs, imaging biomarkers)

• Input parameters (if any)

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Imaging biomarkers: context

• Case 1: Relation to a research question– Measurement process is part of the execution of

research protocol– Context is provided by the research goal and

protocol

• Case 2: Relation to a clinical question– Measurement process is part of the actions

performed to answer the clinical question (possibly detailed via a protocol, and/or a report template)

– Context is provided by the clinical question and associated clinical information

30


Principal ontologies to start from

• Foundational ontologies: BFO or DOLCE– Provide a common modeling framework as well as the

major top-level entities

• Qualities: PATO• Provenance: PROV• Measurement and information artifacts: OBI / IAO• Imaging: RadLex• Imaging datasets: OntoNeuroLog• Imaging biomarkers: QIBO• Medicine in general: SNOMED, ICD, NCIT

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But of inequal quality and completeness

Added value of ontologies for imaging biobanks (1)

• Standard vocabulary for images and imaging biomarkers, facilitating data sharing in large image repositories (imaging biobanks)

• More explicit and more formal representation, enabling – intelligent querying (SPARQL)

– rich inferencing capabilities implemented in generic (i.e. domain-agnostic) reasoners

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Added value of ontologies for imaging biobanks (2)

• Complementarity to existing infrastructures– e.g. a semantic implementation of imaging

biomarkers might complement regular DICOM SR files

– Possible implementation: RDF files accessible as a SPARQL endpoint

• Allows enhancing data sharing infrastructures with new and powerful search capabilities

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Agenda (1/2)

• Develop/extend the relevant domain ontologies– Needs to involve relevant domain experts

• the different radiological specialities• the DICOM standard community• the editors of major image processing packages

– as well as ontologists

difficult task

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Agenda (2/2)

• Deploy experimental systems in research infrastructures and imaging biobanks first.

• Then, consider deployment into clinical PACS, to support – Intelligent task management systems

(workflow)– Decision support systems– Quality management systems

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Conclusion / Summary

• We underlined the importance of imaging biomarkers in research (both clinical and translational research) and care delivery

• We underlined the importance of structured reports as a convenient way to produce and share them for both research and clinical applications

• And finally we introduced ontologies and we discussed their added value in both imaging biobanks and clinical PACS


Thank you for your attention

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imaging biomarkers and pacs ontologies bernard gibaud medicis, ltsi, u1099 inserm faculté de...

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