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L’imagerie	
  clinique,	
  états	
  des	
  lieux	
  
et	
  enjeux	
  à	
  venir,	
  l’hôpital	
  et	
  la	
  
recherche	
  clinique.
Bastien	
  Rance
Source:	
  http://www.businessinsider.com/everything-­‐that-­‐happens-­‐in-­‐one-­‐minute-­‐on-­‐
the-­‐internet-­‐2017-­‐9?IR=T
A	
  world	
  
of	
  data
EHRs	
  adoption	
  (actual,	
  or	
  undergoing)
• 83% of	
  US	
  physicians	
  (2015)
• 75% of	
  US	
  hospitals	
  (2015)	
  
• 89%	
  of	
  French	
  health	
  institutions	
  (2016)
A	
  field of	
  data
Clinical	
  Data	
  Warehouse
Clinical	
  Data	
  Warehouse
(CDW)
Diagnosis
Clinical	
  items
Billing	
  codes
Biology	
  (lab)
Nurse	
  transmission
Imaging	
  reports
Pathology	
  reports
Drug	
  prescription
Standardized	
  format
Queryable
Electronic	
  Health	
  Record
(EHR)
Biobank Chemotherapy
Radiotherapy
4
Clinical	
  Data	
  Warehouse	
  at	
  HEGP
Unstructured data: transformation is needed before reuse
Concept # patients # observations
EHR concepts 602,198 124,852,989
Biology (Laboratory) 452,006 132,525,661
Nursing transmission 309,322 18,495,958
Billing (disease) codes 396,285 8,183,118
Rx prescription 191,531 7,243,484
Text reports 546,725 4,039,333
Imaging reports 351,702 1,325,270
Pathology codes 98,401 1,496,635
5
What about	
  images	
  ?
Data	
  
Warehouse
PACS
Images	
  are	
  not	
  stored	
  in	
  Clinical	
  Data	
  Warehouses
à Issues	
  of	
  volume	
  and	
  representation
Imaging	
  reports	
  are	
  integrated
PACS	
  Size	
  at	
  HEGP
PACS	
  	
  at	
  HEGP	
  (800	
  beds)
• 100	
  TB	
  for	
  2000-­‐2015	
  (about	
  15	
  TB/yr	
  and	
  
growing)
• 2,5	
  M	
  exams
• 400	
  billion	
  images
Images	
  integration
CHALLENGE	
  1:	
  DATA	
  EXTRACTION	
  
AND	
  CLINICAL ANNOTATION
*	
  Les	
  LESIONS	
  CIBLES	
  sont	
  définies	
  de	
  la	
  manière	
  suivante:	
  
Au	
  niveau	
  du	
  poumon:	
  
-­‐ Cible	
  1:	
  Nodule	
  du	
  lobe	
  inférieur	
  gauche	
  de	
  14	
  mm	
  de	
  plus	
  grand	
  axe.	
  
Au	
  niveau	
  du	
  médiastin:	
  
-­‐ Cible	
  2:	
  Adénomégalie de	
  la	
  loge	
  de	
  Baréty de	
  46	
  mm	
  de	
  plus	
  grand	
  axe.	
  
-­‐ Cible	
  3:	
  Adénomégalie de	
  la	
  fenêtre	
  aortopulmonaire de	
  35	
  mm	
  de	
  plus	
  
grand	
  axe.
[…]
CONCLUSION	
  
1)	
  La	
  somme	
  des	
  plus	
  grandes	
  longueurs	
  pour	
  le	
  scanner	
  cycle	
  3	
  est	
  donc	
  
mesurée	
  à	
  	
  14+46+35+43+34+26	
  =	
  198	
  mm. Par	
  rapport	
  au	
  scanner	
  de	
  référence	
  
du	
  21/02/2004	
  dont	
  la	
  somme	
  est	
  mesurée	
  à	
  209	
  mm,	
  l'évolution	
  est	
  de	
  -­‐5%.	
  
L'évolution	
  des	
  cibles	
  mesurables	
  est	
  donc	
  stable	
  (SD).	
  
2)	
  Absence	
  d'évolution	
  non-­‐équivoque	
  des	
  lésions	
  non-­‐cibles	
  (SD).	
  
3)	
  Absence	
  de	
  nouvelle	
  lésion	
  non	
  cible	
  (No).	
  
4)	
  La	
  réponse	
  globale	
  est	
  (SD-­‐SD-­‐No)	
  soit	
  SD.	
  
Stabilité	
  de	
  l'atélectasie	
  lobaire	
  supérieure	
  droite	
  secondaire	
  à	
  l'obstruction	
  
quasi-­‐complète	
  de	
  la	
  bronche	
  lobaire	
  par	
  l'adénopathie.	
  
Metastatic	
  
Renal	
  Clear	
  
Cell	
  
Carcinoma
patients
RECIST	
  
follow-­‐up
Semi-­‐
structured	
  
text	
  report
Extracting	
  information	
  from	
  
semi-­‐structured	
  texts
*	
  Les	
  LESIONS	
  CIBLES	
  sont	
  définies	
  de	
  la	
  manière	
  suivante:	
  
Au	
  niveau	
  du	
  poumon:	
  
-­‐ Cible	
  1:	
  Nodule	
  du	
  lobe	
  inférieur	
  gauche	
  de	
  14	
  mm de	
  plus	
  grand	
  axe.	
  
Au	
  niveau	
  du	
  médiastin:	
  
-­‐ Cible	
  2:	
  Adénomégalie de	
  la	
  loge	
  de	
  Baréty de	
  46	
  mm de	
  plus	
  grand	
  axe.	
  
-­‐ Cible	
  3:	
  Adénomégalie de	
  la	
  fenêtre	
  aortopulmonaire de	
  35	
  mm	
  de	
  plus	
  
grand	
  axe.
[…]
CONCLUSION	
  
1)	
  La	
  somme	
  des	
  plus	
  grandes	
  longueurs	
  pour	
  le	
  scanner	
  cycle	
  3	
  est	
  donc	
  
mesurée	
  à	
  	
  14+46+35+43+34+26	
  =	
  198	
  mm. Par	
  rapport	
  au	
  scanner	
  de	
  référence	
  
du	
  21/02/2004	
  dont	
  la	
  somme	
  est	
  mesurée	
  à	
  209	
  mm,	
  l'évolution	
  est	
  de	
  -­‐5%.	
  
L'évolution	
  des	
  cibles	
  mesurables	
  est	
  donc	
  stable	
  (SD).	
  
2)	
  Absence	
  d'évolution	
  non-­‐équivoque	
  des	
  lésions	
  non-­‐cibles	
  (SD).	
  
3)	
  Absence	
  de	
  nouvelle	
  lésion	
  non	
  cible	
  (No).	
  
4)	
  La	
  réponse	
  globale	
  est	
  (SD-­‐SD-­‐No) soit	
  SD.	
  
Stabilité	
  de	
  l'atélectasie	
  lobaire	
  supérieure	
  droite	
  secondaire	
  à	
  l'obstruction	
  
quasi-­‐complète	
  de	
  la	
  bronche	
  lobaire	
  par	
  l'adénopathie.	
  
Metastatic	
  
Renal	
  Clear	
  
Cell	
  
Carcinoma
patients
RECIST	
  
follow-­‐up
Semi-­‐
structured	
  
text	
  report
Extracting	
  information	
  from	
  
semi-­‐structured	
  textsIdentification	
  and	
  
extraction	
  of	
  features
of	
  interest through a	
  
rule-­‐based system
5,000+
Semi-­‐structured	
  
Radiology	
  reports
RECIST	
  
extractor
RECIST	
  
Explorer
Queriable
Dynamic
PACS
Radiology	
  image	
  archives
Leveraging semi-­‐structured text
Clinical	
  Data	
  Warehouse
Work	
  by	
  G.	
  Simavonian
RECIST	
  Explorer
From text to	
  structured-­‐information
15
Descripteurs	
  
de	
  forme
Descripteurs	
  
statistiques
Texture
GLCM
Texture
GLRLM
Texture
GLSZM
Texture
NGTDM
5	
  filters
3	
  scales
2	
  grey levels
15	
  features
23	
  features
ROI
690	
  
features
390	
  
features
390	
  
features
150	
  
features
13	
  
features
75	
  
features
13	
  features
13	
  features
5	
  features
13	
  features
Logiciel	
  Radiomics (Labo	
  imagerie)
1708
paramètres
Radiomics
Par	
  ROI/	
  par	
  patient
Radiomics
Features
Patients
(in	
  preparation)	
  A	
  Bouchouicha,	
  D	
  Balvay,	
  B	
  Rance,	
  L	
  Fournier.	
  Radiomics of	
  metastatic	
  clear-­‐cell	
  renal	
  carcinoma:	
  
reproducibility	
  and	
  agreement	
  of	
  radiomics features	
  in	
  CT.
Limited	
  publicly	
  shared	
  datasets
Datasets with
metadata and	
  
annotation
Neurology
CHALLENGE	
  2:	
  FAIRNESS
FAIR	
  Data
Findable
Accessible
Interoperable
Reusable
https://www.force11.org/group/fairgroup/fairprinciples
Boulakia et	
  al.	
  Distilling	
  structure	
  in	
  Taverna scientific	
  
workflows:	
  A	
  refactoring	
  approach.	
  BMC	
  Bioinformatics	
  15	
  
Suppl 1(Suppl 1):S12
DOI:	
  10.1186/1471-­‐2105-­‐15-­‐S1-­‐S12
Provenance	
  information
Traced using provenance	
  ontologies
Scientific	
  workflow
Example:
Taverna
CHALLENGE	
  3:	
  HIGH	
  THROUGHPUT	
  
ANALYSIS
Classification
Clustering
Non	
  supervised
The	
  machine	
  
learns	
  using	
  
« distance »	
  
between	
  
objects
Supervised
The	
  machine	
  
learns	
  using	
  
annotated	
  
examples
The	
  Deep-­‐Learning	
  Revolution
The	
  Deep-­‐Learning	
  Revolution
151	
  patients	
  with	
  low-­‐grade	
  glioma.
Modified	
  convolutional	
  neural	
  network	
  (CNN)	
  
structure	
  with	
  6	
  convolutional	
  layers	
  and	
  a	
  fully	
  
connected	
  layer	
  with	
  4096	
  neurons
Discovery	
  data	
  set	
  of	
  75	
  patients	
  and	
  an	
  
independent	
  validation	
  data	
  set	
  of	
  37	
  patients.
A	
  total	
  of	
  1403	
  handcrafted	
  features	
  and	
  98304	
  
deep	
  features	
  were	
  extracted	
  from	
  preoperative	
  
multi-­‐modality	
  MR	
  images
1007	
  posteroanterior chest	
  radiographs.
The	
  datasets	
  were	
  split	
  into	
  training	
  (68.0%),	
  
validation	
  (17.1%),	
  and	
  test	
  (14.9%)
AlexNet and	
  GoogLeNet DCNNs
Deep-­‐learning :	
  
The	
  CNN*	
  achieves	
  performance	
  on	
  par	
  with	
  all	
  tested	
  experts	
  across	
  
both	
  tasks,	
  demonstrating	
  an	
  artificial	
  intelligence	
  capable	
  of	
  
classifying	
  skin	
  cancer	
  with	
  a	
  level	
  of	
  competence	
  comparable	
  to	
  
dermatologists
*	
  convolutional	
  neural	
  networks
129,450	
  clinical images
2,032	
  different diseases
ChexNet :	
  détection	
  de	
  pneumopathie	
  par	
  Deep Learning	
  |	
  https://arxiv.org/pdf/1711.05225.pdf
(Very)	
  Short	
  story	
  of	
  deep-­‐learning
1958 -­‐ F.	
  Rosenblatt (1958).	
  « The	
  Perceptron	
  :	
  a	
  Probabilistic Model	
  
for	
  Information	
  Storage	
  and	
  Organisation	
  in	
  the	
  brain »,	
  
Psychological Review,	
  vol.	
  65	
  (1958).
1969 -­‐ M.	
  Minsky,	
  S.	
  Papert,	
  «	
  Perceptrons	
  »,	
  MIT	
  press (1969).
1982 -­‐ J.	
  J.	
  Hopfield,	
  « Neural	
  networks	
  and	
  physical systems with
emergent collective	
  computational abilities »,	
  Proc.	
  Natl.	
  Acad Sci.,	
  
vol.	
  79,	
  p.	
  2554	
  (1982).
2015 -­‐ Y.	
  Le	
  Cun,	
  Y.	
  Bengio,	
  G.	
  Hinton	
  « Deep Learning »,	
  Nature,	
  vol.	
  
521,	
  p.	
  436	
  (2015).
Image:	
  https://appliedgo.net/perceptron/
Image:	
  https://appliedgo.net/perceptron/
Deep-­‐learning – demonstration
Tensorflow Playground
Variables
Deep-­‐learning – demonstration
Tensorflow Playground
Statistical Learning
Statistical	
  learning	
  of	
  spatiotemporal	
  patterns	
  from	
  longitudinal	
  manifold-­‐valued	
  networks.	
  
Koval et	
  al.	
  arXiv:1709.08491v1	
  [stat.ML]	
  25	
  Sep	
  2017
Conclusion
Large	
  collections	
  of	
  images	
  and	
  tools	
  largely	
  
available
Traceability,	
  Accessibility,	
  Provenance…	
  (FAIR)
Collaborations between	
  communities	
  needed	
  
(Imaging,	
  NLP,	
  CS,	
  statistics)
New	
  analytical	
  methods	
  rising
Need	
  to	
  be	
  critical	
  but	
  considerate
Acknowledgements
Pr.	
  Laure	
  Fournier
Afef	
  Bouchouicha
Daniel	
  Balvay
Gabriel	
  Simavonian

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14 00-20171207 rance-piv_c

  • 1. L’imagerie  clinique,  états  des  lieux   et  enjeux  à  venir,  l’hôpital  et  la   recherche  clinique. Bastien  Rance
  • 3. EHRs  adoption  (actual,  or  undergoing) • 83% of  US  physicians  (2015) • 75% of  US  hospitals  (2015)   • 89%  of  French  health  institutions  (2016) A  field of  data
  • 4. Clinical  Data  Warehouse Clinical  Data  Warehouse (CDW) Diagnosis Clinical  items Billing  codes Biology  (lab) Nurse  transmission Imaging  reports Pathology  reports Drug  prescription Standardized  format Queryable Electronic  Health  Record (EHR) Biobank Chemotherapy Radiotherapy 4
  • 5. Clinical  Data  Warehouse  at  HEGP Unstructured data: transformation is needed before reuse Concept # patients # observations EHR concepts 602,198 124,852,989 Biology (Laboratory) 452,006 132,525,661 Nursing transmission 309,322 18,495,958 Billing (disease) codes 396,285 8,183,118 Rx prescription 191,531 7,243,484 Text reports 546,725 4,039,333 Imaging reports 351,702 1,325,270 Pathology codes 98,401 1,496,635 5
  • 6. What about  images  ? Data   Warehouse PACS Images  are  not  stored  in  Clinical  Data  Warehouses à Issues  of  volume  and  representation Imaging  reports  are  integrated
  • 7. PACS  Size  at  HEGP PACS    at  HEGP  (800  beds) • 100  TB  for  2000-­‐2015  (about  15  TB/yr  and   growing) • 2,5  M  exams • 400  billion  images
  • 8.
  • 10. CHALLENGE  1:  DATA  EXTRACTION   AND  CLINICAL ANNOTATION
  • 11. *  Les  LESIONS  CIBLES  sont  définies  de  la  manière  suivante:   Au  niveau  du  poumon:   -­‐ Cible  1:  Nodule  du  lobe  inférieur  gauche  de  14  mm  de  plus  grand  axe.   Au  niveau  du  médiastin:   -­‐ Cible  2:  Adénomégalie de  la  loge  de  Baréty de  46  mm  de  plus  grand  axe.   -­‐ Cible  3:  Adénomégalie de  la  fenêtre  aortopulmonaire de  35  mm  de  plus   grand  axe. […] CONCLUSION   1)  La  somme  des  plus  grandes  longueurs  pour  le  scanner  cycle  3  est  donc   mesurée  à    14+46+35+43+34+26  =  198  mm. Par  rapport  au  scanner  de  référence   du  21/02/2004  dont  la  somme  est  mesurée  à  209  mm,  l'évolution  est  de  -­‐5%.   L'évolution  des  cibles  mesurables  est  donc  stable  (SD).   2)  Absence  d'évolution  non-­‐équivoque  des  lésions  non-­‐cibles  (SD).   3)  Absence  de  nouvelle  lésion  non  cible  (No).   4)  La  réponse  globale  est  (SD-­‐SD-­‐No)  soit  SD.   Stabilité  de  l'atélectasie  lobaire  supérieure  droite  secondaire  à  l'obstruction   quasi-­‐complète  de  la  bronche  lobaire  par  l'adénopathie.   Metastatic   Renal  Clear   Cell   Carcinoma patients RECIST   follow-­‐up Semi-­‐ structured   text  report Extracting  information  from   semi-­‐structured  texts
  • 12. *  Les  LESIONS  CIBLES  sont  définies  de  la  manière  suivante:   Au  niveau  du  poumon:   -­‐ Cible  1:  Nodule  du  lobe  inférieur  gauche  de  14  mm de  plus  grand  axe.   Au  niveau  du  médiastin:   -­‐ Cible  2:  Adénomégalie de  la  loge  de  Baréty de  46  mm de  plus  grand  axe.   -­‐ Cible  3:  Adénomégalie de  la  fenêtre  aortopulmonaire de  35  mm  de  plus   grand  axe. […] CONCLUSION   1)  La  somme  des  plus  grandes  longueurs  pour  le  scanner  cycle  3  est  donc   mesurée  à    14+46+35+43+34+26  =  198  mm. Par  rapport  au  scanner  de  référence   du  21/02/2004  dont  la  somme  est  mesurée  à  209  mm,  l'évolution  est  de  -­‐5%.   L'évolution  des  cibles  mesurables  est  donc  stable  (SD).   2)  Absence  d'évolution  non-­‐équivoque  des  lésions  non-­‐cibles  (SD).   3)  Absence  de  nouvelle  lésion  non  cible  (No).   4)  La  réponse  globale  est  (SD-­‐SD-­‐No) soit  SD.   Stabilité  de  l'atélectasie  lobaire  supérieure  droite  secondaire  à  l'obstruction   quasi-­‐complète  de  la  bronche  lobaire  par  l'adénopathie.   Metastatic   Renal  Clear   Cell   Carcinoma patients RECIST   follow-­‐up Semi-­‐ structured   text  report Extracting  information  from   semi-­‐structured  textsIdentification  and   extraction  of  features of  interest through a   rule-­‐based system
  • 13. 5,000+ Semi-­‐structured   Radiology  reports RECIST   extractor RECIST   Explorer Queriable Dynamic PACS Radiology  image  archives Leveraging semi-­‐structured text Clinical  Data  Warehouse
  • 14. Work  by  G.  Simavonian RECIST  Explorer From text to  structured-­‐information
  • 15. 15 Descripteurs   de  forme Descripteurs   statistiques Texture GLCM Texture GLRLM Texture GLSZM Texture NGTDM 5  filters 3  scales 2  grey levels 15  features 23  features ROI 690   features 390   features 390   features 150   features 13   features 75   features 13  features 13  features 5  features 13  features Logiciel  Radiomics (Labo  imagerie) 1708 paramètres Radiomics Par  ROI/  par  patient
  • 16. Radiomics Features Patients (in  preparation)  A  Bouchouicha,  D  Balvay,  B  Rance,  L  Fournier.  Radiomics of  metastatic  clear-­‐cell  renal  carcinoma:   reproducibility  and  agreement  of  radiomics features  in  CT.
  • 17. Limited  publicly  shared  datasets Datasets with metadata and   annotation Neurology
  • 18.
  • 21. Boulakia et  al.  Distilling  structure  in  Taverna scientific   workflows:  A  refactoring  approach.  BMC  Bioinformatics  15   Suppl 1(Suppl 1):S12 DOI:  10.1186/1471-­‐2105-­‐15-­‐S1-­‐S12 Provenance  information Traced using provenance  ontologies Scientific  workflow Example: Taverna
  • 22. CHALLENGE  3:  HIGH  THROUGHPUT   ANALYSIS
  • 23. Classification Clustering Non  supervised The  machine   learns  using   « distance »   between   objects Supervised The  machine   learns  using   annotated   examples
  • 25. The  Deep-­‐Learning  Revolution 151  patients  with  low-­‐grade  glioma. Modified  convolutional  neural  network  (CNN)   structure  with  6  convolutional  layers  and  a  fully   connected  layer  with  4096  neurons Discovery  data  set  of  75  patients  and  an   independent  validation  data  set  of  37  patients. A  total  of  1403  handcrafted  features  and  98304   deep  features  were  extracted  from  preoperative   multi-­‐modality  MR  images 1007  posteroanterior chest  radiographs. The  datasets  were  split  into  training  (68.0%),   validation  (17.1%),  and  test  (14.9%) AlexNet and  GoogLeNet DCNNs
  • 26. Deep-­‐learning :   The  CNN*  achieves  performance  on  par  with  all  tested  experts  across   both  tasks,  demonstrating  an  artificial  intelligence  capable  of   classifying  skin  cancer  with  a  level  of  competence  comparable  to   dermatologists *  convolutional  neural  networks 129,450  clinical images 2,032  different diseases
  • 27. ChexNet :  détection  de  pneumopathie  par  Deep Learning  |  https://arxiv.org/pdf/1711.05225.pdf
  • 28. (Very)  Short  story  of  deep-­‐learning 1958 -­‐ F.  Rosenblatt (1958).  « The  Perceptron  :  a  Probabilistic Model   for  Information  Storage  and  Organisation  in  the  brain »,   Psychological Review,  vol.  65  (1958). 1969 -­‐ M.  Minsky,  S.  Papert,  «  Perceptrons  »,  MIT  press (1969). 1982 -­‐ J.  J.  Hopfield,  « Neural  networks  and  physical systems with emergent collective  computational abilities »,  Proc.  Natl.  Acad Sci.,   vol.  79,  p.  2554  (1982). 2015 -­‐ Y.  Le  Cun,  Y.  Bengio,  G.  Hinton  « Deep Learning »,  Nature,  vol.   521,  p.  436  (2015).
  • 33. Statistical Learning Statistical  learning  of  spatiotemporal  patterns  from  longitudinal  manifold-­‐valued  networks.   Koval et  al.  arXiv:1709.08491v1  [stat.ML]  25  Sep  2017
  • 34. Conclusion Large  collections  of  images  and  tools  largely   available Traceability,  Accessibility,  Provenance…  (FAIR) Collaborations between  communities  needed   (Imaging,  NLP,  CS,  statistics) New  analytical  methods  rising Need  to  be  critical  but  considerate
  • 35. Acknowledgements Pr.  Laure  Fournier Afef  Bouchouicha Daniel  Balvay Gabriel  Simavonian