2013_문헌초독회 발표

1. ARTIFICIAL INTELIGENCE in Nuclear Medicine
The latest trend of statistical approach for ‘Big Data’ in medicine
R3, Choi Hongyoon

2. CONTENTS
INTRODUCTION
Why A.I. for medicine?
New Statistical Approach
REVIEWS
Multivariate analysis for
‘prediction’
ACTUAL PRACTICE
Real Application
New Design for PET/MR studies
ARTIFICIAL INTELIGENCE
in Nuclear Medicine

3. INTRODUCTION
푸딩얼굴인식
Face
Pattern
Database
Results

4. INTRODUCTION
SoundHound
Music & Discovery
흥얼거림
Pattern
Database
Results

5. INTRODUCTION
GOOGLE Image

6. INTRODUCTION

7. INTRODUCTION
주식예측프로그램
Economic Variables
Pattern
Database
Results

8. INTRODUCTION
WHY We…?

9. INTRODUCTION
Image Variables
Pattern
Database
Results
SUV, Clinical Factors, Texture, etc.
Diagnosis (CAD)
 Survival / Morbidity / Prediction of Tx response
Previous Data : EMR, PACS , …

10. INTRODUCTION
TREND of medical statistics
Chance 2012;25(3):31-34

11. INTRODUCTION
Benign vs. Malignancy
SUV : p = 0.23
Size : p = 0.43
TLG : p = 0.17
MTV : p = 0.22
CEA : p = 0.44

12. Prediction From Big DATA
Statistical methods and Clinical ideas

13. REVIEW
Application of machine learning in medicine
Lung nodule characteristics/ Clinical factors
Logistic Regression (Training 7008 nodules/ Validation 5021 nodules)
Malignancy Prediction : ROC 0.90
McWilliams et al. NEJM, 2013.

14. REVIEW
Application of machine learning in medicine
Malignancy Prediction : ROC 0.90
McWilliams et al. NEJM, 2013.
Logistic
Lung
nodules
Size
Age, Sex
Location
Character
Predicting
Lung
Cancer

15. REVIEW
Application of machine learning in medicine
• Images
• Genetic Data (SNPs, Sequencing,…)
• Clinical Features
Ingredient
• Regression (e.g. Logistic)
• Kernel (e.g.)Support Vector Machine
• Artificial Neural Network
• Ensamble (e.g. Random Forest)
Methods
• Diagnosis
• Survival
• Treatment Prediction
• Imaging : Segmentation / Registration
Results

16. REVIEW
Arsanjani R. et al. Improved Accuracy of Myocardial Perfusion SPECT for the
Detection of Coronary Artery Disease Using a Support Vector Machine
Algorithm. J Nucl Med 2013.
Prels O. et al. Neural Network Evaluation of PET Scans of the Liver:
A Potentially Useful Adjunct in Clinical Interpretation. Radiology 2011.

17. SVM
REVIEW
Myocardial SPECT for Dx. of CAD
Automatic Softwares
No diagnostic score based on multiple quantitative features
Support Vector Machine (SVM)
Kernel-based machine learning algorithm
SVM for Myocardial SPECT
Functional
MPS
Variables
(EF, SDS, …)
Predicting
Severe
Stenosis

18. REVIEW
Support Vector Machine
Non-linear classification (High-dimensional Kernel based)
Hyperplane (Decision-plane)
SVM for Myocardial SPECT
EF
Thikcening
High-
Risk
Low-
Risk
Training
Test Patient is
High-Risk Group
Simulated on Libsvm, http://www.csie.ntu.edu.tw/~cjlin/libsvm/

19. REVIEW
Support Vector Machine
SVM for Myocardial SPECT
Nonlinear Hyperplane
Kernel Based (Higher Dimension)
Logistic Regrssion
Error ~40%
SVM
Error ~7%
Simulated on Libsvm, http://www.csie.ntu.edu.tw/~cjlin/libsvm/

20. REVIEW
METHODS
957 Myocardial SPECT studies
Training Group (n=125) :
25 LLk, 25 0-vessel, 25 1-vessel, 25 2-vessel, 25 3-vessel
Testing Group (n=832)
Myocardial SPECT : Tc-99m sestamibi rest/stress
Stress Total Perfusion Deficit (TPD)
Ischemic Changes (ISCHs)
Poststress EF changes (EFCs)
Motion and thickening changes (MTC)
SVM for Myocardial SPECT

21. REVIEW
METHODS
Visual Scoring :
2 Board NM physicians / SDS scoring
Support Vector Machine
Training : TPD, ISCHs, EFC or MTC combination
Polynomial kernel
Testing : Probability / CAD vs. non-CAD prediction
Gold Standard :
LMA >50% or Other Coronary a. >70% stenosis
SVM for Myocardial SPECT

22. REVIEW
RESULTS
SVM for Myocardial SPECT
Combination Methods >> Single Feature

23. REVIEW
SVM for Myocardial SPECT
RESULTS
Combination Machine Learning > or = Boardman’s Reading

24. REVIEW
SVM for Myocardial SPECT
CONCLUSION
Improvement of diagnostic performance using
multiple functional & perfusion parameters
Full automated risk evaluation

25. REVIEW
Arsanjani R. et al. Improved Accuracy of Myocardial Perfusion SPECT for the
Detection of Coronary Artery Disease Using a Support Vector Machine
Algorithm. J Nucl Med 2013.
Prels O. et al. Neural Network Evaluation of PET Scans of the Liver:
A Potentially Useful Adjunct in Clinical Interpretation. Radiology 2011.

26. REVIEW
FDG PET for hepatic metastasis
Sensitivity : 86% MR vs. 71% FDG-PET
Variability of hepatic metastases &
heterogeneous liver parenchymal uptake
ANN for FDG PET of the Liver
ANN
FDG PET
Variables
(SUVs, Clinical
Factors)
Predicting
Liver
Metastasis
/Compared
with MR

27. REVIEW
Artificial neural network
ANN for FDG PET of the Liver
Neuron  Perceptron
SUV
> 2.5
Size
>
1cm
g(x)
Liver
SUV
< 3.0
(x30)
(x10)
(x-10)
>35 : 1
<35 : 0
Benign
Node Hidden Layer Output

28. REVIEW
Artificial neural network
ANN for FDG PET of the Liver
SUV
> 2.5
Size
>
1cm
Liver
SUV
< 3.0
A
…
g(x)
g(x)
g(x)
g(x)
g(x)
Metast
asis
Training Set 
Weighting Factors
Maximize correct output
Testing Set
Range 0 - 1

29. REVIEW
METHODS
Patients: 98 FDG PET scans and liver MR
Input Nodes
Lesion indepdent : Ages, Liver SUV&SD, spleen SUV SD, gluteus
maximus SUV SD, BMI, glucose level (9 nodes)
Lesion specific: lesion SUV & SD (2 nodes)
Neural Network
11 nodes  Hidden layer 5 (arbitrarily)  1 Output
ANN for FDG PET of the Liver

30. REVIEW
METHODS
Physician Performance
Two NM/abdominal imaging experties
Blind to NN data / Unblind to NN data
Reference Standard
Biopsy + followup MR
ANN for FDG PET of the Liver

31. REVIEW
RESULTS
ANN for FDG PET of the Liver
Lesion Depedent
newtork (AUC 0.905)
Observer 1 (0.786  0.924)
Blind
unblind
Observer 2 (0.796  0.881)
Blind
unblind

32. REVIEW
ANN for FDG PET of the Liver
RESULTS
May 2008
ANN score 0.70
August 2008
ANN score 0.88
December 2008
ANN score 1.00

33. REVIEW
CONCLUSION
Lesion independent nodes
10 cases : absent of visually apparent lesions  mets
Interpretation of systemic / global abnormalities
Reinterpretation after NN
Significantly improved visual reading
ANN for FDG PET of the Liver

34. REVIEW
Applications
Thyroid nodule : Indeterminate nodule
Applications
NEJM, 2012

35. REVIEW
Applications
49 Clinical sites, 4812 FNAB 
577 Indeterminate nodules
Gene-expression Classifier (167 genes, SVM-based)
Chudova D, et al. J Clin Endo Metab 2010.
Applications
NEJM, 2012
92% sensitivity
52% specificity

36. REVIEW
Applications
Applications
J Clin Oncol 2009
SVM
Several
Biomarkers
for Lung
Cancer
Survival

37. REVIEW
Applications
148 Stage IB NSCLC, IHC results
73 training set + 75 validation set
5Yr survival 1 ; death 0
Different Classifier : SVM1, 2, 3
different IHC features
Applications
J Clin Oncol 2009

38. REVIEW
Applications
Applications
J Clin Oncol 2009
Validation Set

39. REVIEW
Benign vs. Malignancy
SUV : p = 0.23
Size : p = 0.43
TLG : p = 0.17
MTV : p = 0.22
CEA : p = 0.44
Applications

40. REVIEW
Applications
Brain Studies
Applications
Zhang D, et al. NeuroImage 2012.
SVM
PET
MRI
CSF
AD vs.
Normal
Template –
based multiple
VOIs

41. REVIEW
Applications
Brain Studies
Applications
Gray KR, et al. NeuroImage 2013.
Random
-Forest
PET
MRI
CSF
AD vs.
Normal
Random Forest

42. REVIEW
Applications
Brain Studies
Applications
Important Areas
Gray KR, et al. NeuroImage 2013.
Combined Random Forest : Se 87.9% ; Sp 90.0%

43. Actual data – Simple Experience
Difficult, but we can do it.

44. ACTUAL DATA
Simple Experience
Pre-CCRT locally advanced
rectal cancer
From NCC
CCRT Or

45. ACTUAL DATA
J Nucl Med 2011.
J Nucl Med 2012.
Lung
cancer
Esop
cancer

46. ACTUAL DATA
Simple Experience
Pre-CCRT locally advanced rectal cancer
From NCC
Tumor Segmentation
(SUV threshold, Isocontour)
Image-based Features
SUVmax, SUVmean, Kurtosis, Skewness
Textural Features: Contrast, Correlation, Entropy,
Homogeneity, Energy
Prediction for
Dworak Tumor
regression grade

47. ACTUAL DATA
Simple Experience
Pre-CCRT locally advanced rectal cancer
From NCC
Hypothesis : Different Textural Features in Good responders
T-tests for each of features b/w good and poor responders
SUVmax 8.2
SUVmean 7.5
Kurtosis 1.3
…

48. ACTUAL DATA
Simple Experience

49. ACTUAL DATA
Simple Experience
52 Training set + 20 Test set
Image Features – SVM  Prediction for CCRT response
Features
Subjects
Response

50. ACTUAL DATA
Simple Experience
>> svmStruct=svmtrain(trainingData,response,’kernel_function’,’rbf’);
>> PredictResponse=svmclassify(svmStruct,testData);
Test DATA (n=20)
Results : 17/20  Accurate Prediction for Response

51. Summary
MULTIPLE Data
Random or nonrandom
(pattern?)
‘Arrival Time of E-mails’
Non-random variables 
Rule of specific output
Benign vs. Malignancy
SUV : p = 0.23
Size : p = 0.43
TLG : p = 0.17
MTV : p = 0.22
CEA : p = 0.44
SUV, Size, TLG , …
: Unknown Rule
: Find Rule!

52. SUMMARY
Image Variables
Pattern
Database
Results
SUV, Clinical Factors, Texture, etc.
Diagnosis (CAD)
 Survival / Morbidity / Prediction of Tx response
Previous Data : EMR, PACS , …

53. SUMMARY
PET/MR studies
PET variables and MR variables
Not competitive
e.g.> SUVmax, SUVmean, TLG, MTV
ADC, perfusion MR parameters, Size, …
Combination Studies  Big Data analysis

54. Take Home Message
Multiple variables based prediction.
State-of-the-art statistical approach
PET/MR  Image ‘big data’
Integration using new methods for better
diagnostic performance
A wide range for application / Not so difficult
ANOVA, T-tests, nonparametric tests, …