digital health connect...than 4 of 5 pat ients. the performance of hu and gl cm at tributes w...

DIGITAL HEALTH CONNECT7 Juin 2019 - Sierre

…un maillon fort de l’innovation en Valais

Industrie 4.0 E-health E-energy E-tourisme

La digitalisation … élément-clé

Health Institutions &

Professionals

R&D Institutes

Academies

Business

Digital Health Ecosystem

Pôle d’innovation d’excellence, générateur de valeurs économiques nouvelles

Renforcer le système de santé par l’innovation.

L’inspirer par les besoins du terrain et les nouvelles technologies pour créer des valeurs ajoutées fortes.

HES-SO Valais-Wallis

eHealth Research in Valais

Prof. Laurent Sciboz

laurent.sciboz

@laurentsciboz


Le Valais, premier pôle suisse dédié aux technologies de l’information

• Favorise les synergies entre instituts de recherche et entreprises

• 60 PME et startups

• 5 instituts de recherche

• Plus de 600 collaborateurs

• Proche des besoins des patients :• localement (Hôpital du Valais, CRR Suva, Logival, ....),

• au niveau national (CHUV, HUG, EPFL, ETH, University Hospital Zurich, ...) et

• au niveau international (Stanford, Harvard, Imperial College, Carnegie Mellon, NLM, ....)


HTIC : Health Technology Innovation Center


Quelques exemples :

Extreme-scale Analytics via Multimodal Ontology Discovery & EnhancementProf. Henning Müller

Develop algorithms and tools to link visual content and associateddiagnoses/meta-d from various hospitals (starting from histopathology images)

Call: European Union Horizon 2020, ICT-12-2018-2020• Success rate: 78 submissions,6 projects granted

ProjectsupportedbyEuropeanUnionHorizon2020grantagreement825292

6


Quelques exemples : VISIBILE

The future of radiomics: understanding, Validating and Increasing the Specificity of Imaging Biomarkers for personaLizEd medicine (VISIBLE)

• Medical Image Analysis (MIA) for non-invasive personalized medicine

• Medical knowledge discovery from digital phenotype analysis

• Close collaboration with CHUV (Prof. Prior)

Our tasks

• Develop and validate novel imaging biomarkers

and specific deep learning designs for MIA

Funding: SNSF

Leader

• Prof. Adrien Depeursinge

themaximumof thescoreprovided by theSVMs. A maximumareaunder theROCcurve(AUC) of 0.81 wasobtained with theregionalRiesz attributes, which suggests that prediction wascorrect for morethan 4 of 5 patients. Theperformance of HU and GLCM attributeswasclosetorandom(0.54and0.6for HUandGLCMs, respectively).Ontheother hand, predictiveSVM modelsbasedontheresponsesoftheRieszfilters, averagedover theentirelungs, hadanAUCof 0.72.

Our system'sperformancewasalsocomparedwiththeinterpreta-tionsof 2 fellowship-trained cardiothoracic fellows, each having 1 yearof experience. Interobserver agreement wasassessed with theCohenκstatistics30 andthepercentageof agreement (ie, number of timesthe2 observer agreed). Thecomparisonsaredetailed in Tables3 and 4.Theoperating points of the2 independent observers are reported inFigure4 (top right). A detailed analysisof the6 cases that weremis-classified by our system is shown in Table 5 with representative CTimages, includingpredictionsfromthecomputer andthe2fellowscom-pared with theconsensusclassification. Thesystempredicted 2 classicUIPcasesasatypical UIPand3atypical UIPcasesasclassicUIP.Acom-prehensive analysis of all 33 cases is illustrated in the SupplementalTable, Supplemental Digital Content 1, http://links.lww.com/RLI/A189.

Overall, 7incorrect predictionsweremadeby thefellowsand6incor-rect predictionsby thecomputer. Thefellowsandthecomputer madeonly 2commonerrors(cases1and13).

DISCUSSION

Wedevelopedanovel computational methodfor theautomatedclassificationof classicversusatypical UIPbasedonregional volumet-rictextureanalysis.Thisconstitutes,tothebestof ourknowledge,afirstattempt toautomatically differentiatetheUIPsubtypeswithcomputa-tional methods. AnSVM classifier yieldedascorethat predictsif theUIPisclassicor atypical. Theclassifier wasbasedonagroupof attri-butes that characterize theradiological phenotypeof the lung paren-chyma, specifically the morphological properties (ie, texture) of theparenchyma.Becausediffuselungdiseasescanvary inthedistributionand severity of abnormalities throughout the lungs, weextracted ourquantiativeimagefeaturesfrom36anatomical regionsof thelung. Toourknowledge,addingthisspatial characterizationtothecomputationalmodel isalso innovative, and it isparticularly relevant for assessingdiffuselungdisease.

FIGURE3. The36subregionsof thelungslocalized theprototyperegional distributionsof thetextureproperties. Figure3canbeviewed onlineincolorat www.investigativeradiology.com.

FIGURE4. TheROCanalysisof thesystem'sperformance. ClassicUIPisthepositiveclass. Left, Comparison of variousfeaturegroupsusing thedigitallungtissueatlas.Three-dimensionalRieszwaveletsprovideasuperiorAUCof 0.81.Right, Importanceof theanatomicalatlaswhencomparedwithanapproachbased on theglobal tissuepropertiesand comparisonof thecomputer'sand cardiothoracic fellows' performance. Bottom, Probabilitydensityfunctionsof thecomputer scorefor classic(red) and atypical UIP(blue) based on regional Riesztextureanalysisand thecomputer'soperating pointhighlighted in theupper right subfigure. Atypical UIPisassociated withanegativescore, which impliesthat positivescorespredict classicUIPswithhighspecificity. Figure4 can beviewed onlinein color at www.investigativeradiology.com.

Depeursingeet al InvestigativeRadiology • Volume00, Number 00, Month 2015

4 www.investigativeradiology.com ©2014WoltersKluwer Health, Inc. All rightsreserved.

Copyright © 2014 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited.

Computational Models of Biomedical Tissue in Radiology Images

for Non-Invasive Personalized Medicine Adrien Depeursinge1,2, Yashin Dicente1, Oscar Jimenez1, Ranveer Joyseeree1, Roger Schaer1, Henning Muller1

1Institute of Information Systems, HES-SO, Switzerland 2Biomedical Imaging Group, EPFL, Switzerland

Contact and more information: [email protected], http://medgift.hevs.ch/

Background

• Personalized medicine aims at enhancing the quality of life and

prognosis by tailoring treatment and medical decisions based on

the molecular composition of diseased tissue

• Current limitations [1]:

• Molecular analysis of tissue composition

is very invasive (biopsy), slow and costly

• Cannot capture molecular heterogeneity

Computational Models of Biomedical Tissue

• Modern multi-dimensional radiological images contain much more

information than the naked eye can appreciate [2]

(e.g., 3D structural properties of biomedical tissue)

• Goal: use image processing and machine learning to build

disease-specific imaging biomarkers [3]:

• Capture intralesional heterogeneity in a non-invasive way

• Predict diagnosis, prognosis, treatment response, genetic profile

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Intr

atu

mo

r H

etero

gen

eit

y R

evealed

by m

ultir

eg

ion

Seq

uen

cin

g

n e

ngl j m

ed 36

6;10

n

ejm

.org

march

8, 2

012

887

tion th

rough lo

ss of SE

TD2 m

ethyltran

sferase func-

tion d

riven b

y three d

istinct, reg

ionally sep

arated

mutatio

ns o

n a b

ackgro

und o

f ubiq

uito

us lo

ss of

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TD2 a

llele on ch

rom

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me 3

p.

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ent

evolu

tion w

as observed

fo

r th

e

X-ch

rom

oso

me–

enco

ded

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ne H

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emeth

-

ylase KDM

5C, h

arborin

g d

isruptive m

utatio

ns in

R1 th

rough R

3, R

5, and R

8 th

rough R

9 (m

issense

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eshift d

eletion) an

d a sp

lice-site mutatio

n

in th

e metastases (F

ig. 2

B a

nd 2

C).

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R F

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ctio

nal In

tratum

or H

etero

gen

eit

y

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amm

alian targ

et of rap

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R) k

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rimary

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In t r at umor Het er ogenei t y Reveal ed by mul t i r egion Sequencing

n engl j med 366;10 nejm.org march 8, 2012 887

tion through loss of SETD2 methyltransferase func-

tion driven by three distinct, regionally separated

mutations on a background of ubiquitous loss of

the other SETD2 allele on chromosome 3p.

Convergent evolution was observed for the

X-chromosome–encoded histone H3K4 demeth-

ylase KDM5C, harboring disruptive mutations in

R1 through R3, R5, and R8 through R9 (missense

and frameshift deletion) and a splice-site mutation

in the metastases (Fig. 2B and 2C).

mTOR Funct ional Int r atumor Heterogeneit y

The mammalian target of rapamycin (mTOR) ki-

nase carried a kinase-domain missense mutation

(L2431P) in all primary tumor regions except R4.

All tumor regions harboring mTOR (L2431P) had

B Regional Distribution of Mutations

C Phylogenetic Relationships of Tumor Regions D Ploidy Profiling

A Biopsy Sites

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PrivateUbiquitous Shared primary Shared metastasis

Private

Ubiquitous

Lungmetastases

Chest-wallmetastasis

Perinephricmetastasis

M110 cm

R7 (G4)

R5 (G4)

R9

R3 (G4)

R1 (G3) R2 (G3)

R4 (G1)

R6 (G1)

Hilum

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Primarytumor

Shared primary

Shared metastasis

M2b

M2a

Propidium Iodide Staining

No. of Cells

The New England Journal of Medicine

Downloaded from nejm.org at UNIVERSITE DE GENEVE on June 2, 2014. For personal use only. No other uses without permission.

Copyright © 2012 Massachusetts Medical Society. All rights reserved.

R1 R2 R3 R5 R8 R9 R4

Methods

• 2D/3D steerable wavelets to characterize local multi-scale and

multi-directional structural properties of biomedical tissue [4]

• Locate the properties in the anatomy to construct personalized

radiological phenotypes [5]

• Integration into cloud-based infrastructures [6]

Targeted Diseases

• Lung, brain, liver, breast, bone cancer [7-9]

• Interstitial lung diseases [5]

• Pulmonary embolism [10]

References

[1] Intratumor heterogeneity and branched evolution revealed by multiregion

sequencing, Gerlinger et al., New Eng. Journal of Med., 366(10), 883-892, 2012. [2] Radiomics: extracting more information from medical images using advanced

feature analysis, Lambin et al., European Journal of Cancer, 48, 441-446, 2012 [3] Three-dimensional solid texture analysis and retrieval in biomedical imaging:

review and opportunities, Depeursinge et al., Medical Image Analysis, 18, 176-196, 2014

[4] Rotation-covariant texture learning using steerable Riesz wavelets, Depeursinge et al., IEEE Trans on Image Proc, 23, 898-908, 2014 [5] Automated classification of usual interstitial pneumonia using regional volumetric texture analysis in high-resolution CT, Depeursinge et

al., Investigative Radiology, 50(4), 261-267, 2015 [6] Automated tracking of quantitative assessments of tumor burden in clinical trials, Rubin et al., Translational Oncology, 7(1), 23-35, 2014

[7] Predicting adenocarcinoma recurrence using computational texture models of nodule components in lung CT, Depeursinge et al., Medical Physics, 42(4), 2054-2063, 2015

[8] Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features, Gevaert et al., Radiology, 273,

168-174, 2014 [9] Predicting visual semantic descriptive terms from radiological image data: preliminary results with liver lesions in CT, Depeursinge et al.,

IEEE Trans on Medical Imaging 33(8), 1-8, 2014 [10] Benefits of texture analysis of dual energy CT for computer-aided pulmonary embolism detection, Foncubierta-Rodriguez et al., 35th

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2013), 3973-3976, 2014


laurent.sciboz

@laurentsciboz

Prof. Laurent Sciboz

Institute of Information Systems

[email protected]

Techno-Pôle 3 – CH-3960 Sierre

digital health connect...than 4 of 5 pat ients. the performance of hu and gl cm at tributes w...

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