Invited talk Sep 17, 2021 @ TJU

1. Sep 17, 2021 @ Thomas Jefferson Univ
Artificial Intelligence
in Radiation Oncology
Wookjin Choi, PhD
Assistant Professor of Computer Science
Virginia State University
wchoi@vsu.edu

2. Acknowledgements
Memorial Sloan Kettering Cancer Center
• Wei Lu PhD
• Sadegh Riyahi, PhD
• Jung Hun Oh, PhD
• Saad Nadeem, PhD
• Joseph O. Deasy, PhD
• Andreas Rimner, MD
• Prasad Adusumilli, MD
• Chia-ju Liu, MD
• Wolfgang Weber, MD
Stony Brook University
• Allen Tannenbaum, PhD
University of Virginia School of Medicine
• Jeffrey Siebers, PhD
• Victor Gabriel Leandro Alves, PhD
• Hamidreza Nourzadeh, PhD
• Eric Aliotta, PhD
University of Maryland School of Medicine
• Howard Zhang, PhD
• Wengen Chen, MD, PhD
• Charles White, MD
• Seth Kligerman, MD
• Shan Tan, PhD
• Jiahui Wang, PhD
2
NIH/NCI Grant R01 CA222216, R01 CA172638 and NIH/NCI Cancer Center Support Grant P30 CA008748
The ESTRO Falcon project team, Scott Kaylor of EduCase, Benjamin Nelms of Proknow for the multi-
delineator contour data presented in this work

3. AI in Radiation Oncology
3
Huynh et al. Nat Rev Clin Oncol 2020

4. 4
Netherton et al. Oncology 2021
Hype cycle for three major innovations
in radiation oncology
Automatable tasks in radiation oncology
for the modern clinic

5. Outline
 Radiomics - Decision Support Tools
• Lung Cancer Screening
• Tumor Response Prediction and Evaluation
• Aggressive Lung ADC subtype prediction
• Multimodal data: Pathology, Multiomics, etc.
 Auto Delineation and Variability Analysis
• Delineation Variability Quantification
• Dosimetric Consequences of Variabilities
• OARNet, Probabilistic U-Net
5

6. Radiomics
6
 Controllable Feature Analysis
 More Interpretable
Lambin, et al. Eur J Cancer 2012
Aerts et al., Nature Communications, 2014

7. Radiomics Framework
7
Image
Registration
• Multi-level rigid
• Deformable
• Pre/Post-CT
• MSE, MI
Tumor
Segmentation
• Adaptive region growing
• Level set
• Grow cut
• Morphology filter
• Multi-modality
image segmentation
Feature
Extraction
• Intensity distribution
• Spatial variations
(texture)
• Geometric properties
• Jacobian feature
from DVF
• Feature selection
Predictive
Model
• ROC analyses
• Prediction models
• Validation
• Tumor response
• Recurrence
• Survival
Source codes: https://github.com/taznux/radiomics-tools
• Automated Workflow (Python)
- Integrate all the radiomics components
- 3D Slicer, ITK (C++), Matlab, R, and Python
- Scalable: support multicore & GPU computing

8. Radiomics Framework
8
Image
Registration
• Multi-level rigid
• Deformable
• Pre/Post-CT
• MSE, MI
Tumor
Segmentation
• Adaptive region growing
• Level set
• Grow cut
• Morphology filter
• Multi-modality
image segmentation
Feature
Extraction
• Intensity distribution
• Spatial variations
(texture)
• Geometric properties
• Jacobian feature
from DVF
• Feature selection
Predictive
Model
• ROC analyses
• Prediction models
• Validation
• Tumor response
• Recurrence
• Survival
Source codes: https://github.com/taznux/radiomics-tools
Deep Learning Model
• Automated Workflow (Python)
• Integrate all the radiomics components
• 3D Slicer, ITK (C++), Matlab, R, and Python
• Scalable: support multicore & GPU computing

9. Radiomics Framework
9
Image
Registration
• Multi-level rigid
• Deformable
• Pre/Post-CT
• MSE, MI
Tumor
Segmentation
• Adaptive region growing
• Level set
• Grow cut
• Morphology filter
• Multi-modality
image segmentation
Feature
Extraction
• Intensity distribution
• Spatial variations
(texture)
• Geometric properties
• Jacobian feature
from DVF
• Feature selection
Predictive
Model
• ROC analyses
• Prediction models
• Validation
• Tumor response
• Recurrence
• Survival
Source codes: https://github.com/taznux/radiomics-tools
Deep Learning Model
• Automated Workflow (Python)
• Integrate all the radiomics components
• 3D Slicer, ITK (C++), Matlab, R, and Python
• Scalable: support multicore & GPU computing

10. Radiomics Framework
10
Image
Registration
• Multi-level rigid
• Deformable
• Pre/Post-CT
• MSE, MI
Tumor
Segmentation
• Adaptive region growing
• Level set
• Grow cut
• Morphology filter
• Multi-modality
image segmentation
Feature
Extraction
• Intensity distribution
• Spatial variations
(texture)
• Geometric properties
• Jacobian feature
from DVF
• Feature selection
Predictive
Model
• ROC analyses
• Prediction models
• Validation
• Tumor response
• Recurrence
• Survival
Source codes: https://github.com/taznux/radiomics-tools
Deep Learning Model
• Automated Workflow (Python)
• Integrate all the radiomics components
• 3D Slicer, ITK (C++), Matlab, R, and Python
• Scalable: support multicore & GPU computing

11. Radiomics Framework
11
Image
Registration
• Multi-level rigid
• Deformable
• Pre/Post-CT
• MSE, MI
Tumor
Segmentation
• Adaptive region growing
• Level set
• Grow cut
• Morphology filter
• Multi-modality
image segmentation
Feature
Extraction
• Intensity distribution
• Spatial variations
(texture)
• Geometric properties
• Jacobian feature
from DVF
• Feature selection
Predictive
Model
• ROC analyses
• Prediction models
• Validation
• Tumor response
• Recurrence
• Survival
Source codes: https://github.com/taznux/radiomics-tools
Deep Learning Model
• Automated Workflow (Python)
• Integrate all the radiomics components
• 3D Slicer, ITK (C++), Matlab, R, and Python
• Scalable: support multicore & GPU computing

12. Radiomics Framework
12
Image
Registration
• Deep Learning Optical fl
ow
• Action like flow
• Differential warp
• Dynamic filtering
• …
Tumor
Segmentation
• U-Net
• Prob. U-Net
• UANet
• …
Feature
Extraction
• AlexNet
• ResNet
• VGG
• LeNet
• ….
Predictive
Model
• ROC analyses
• Prediction models
• Validation
• Tumor response
• Recurrence
• Survival
• Automated Workflow (Python)
• Integrate all the radiomics components
• 3D Slicer, ITK (C++), Matlab, R, and Python
• Scalable: support multicore & GPU computing

13. Lung Cancer Screening
13
 Early detection of lung cancer by LDCT can reduce mortality
 Known features correlated with PN malignancy
 Size, growth rate (Lung-RADS)
 Calcification, enhancement, solidity → texture features
 Boundary margins (spiculation, lobulation), attachment → shape and
appearance features
Malignant nodules Benign nodules
Size Total Malignancy
< 4mm 2038 0%
4-7 mm 1034 1%
8-20 mm 268 15%
> 20 mm 16 75%

14. ACR Lung-RADS
Category Baseline Screening Malignancy
1 No PNs; PNs with calcification
Negative
<1% chance of malignancy
2
Solid/part-solid: <6 mm
GGN: <20 mm
Benign appearance
<1% chance of malignancy
3
Solid: ≥6 to <8 mm
Part-solid: ≥6 mm with solid component <6 mm
GGN: ≥20 mm
Probably benign
1-2% chance of malignancy
4A
Solid: ≥8 to <15 mm
Part-solid: ≥8 mm with solid component ≥6 and <8 mm
Suspicious
5-15% chance of malignancy
4B
Solid: ≥15 mm
Part-solid: Solid component ≥8 mm
>15% chance of malignancy
4X
Category 3 or 4 PNs with suspicious features (e.g. enlarged lymph
nodes) or suspicious imaging findings (e.g. spiculation)
>15% chance of malignancy
14
ACR: American College of Radiology
Lung-RADS: Lung CT Screening Reporting and Data System

15. Lung Cancer Screening (Methodology)
• TCIA LIDC-IDRI public data set (n=1,010)
• Multi-institutional data
• 72 cases evaluated (31 benign and 41 malignant cases)
• Consensus contour
15
GLCM GLRM
Texture features Intensity features
2D
Shape features
3D

16. Lung Cancer Screening (SVM-LASSO Model )
16
SVM classification
Distinctive feature identification
Malignant?
Predicted malignancy
Feature extraction
Yes
10x10-fold
CV
10-fold
CV
LASSO feature selection
• Size (BB_AP) : Highly correlated with the axial longest diameter
and its perpendicular diameter (r = 0.96, larger – more
malignant)
• Texture (SD_IDM) : Tumor heterogeneity (smaller – more
malignant)

17. Lung Screening (Results: Comparison)
Sensitivity Specificity Accuracy AUC
Lung-RADS
Clinical guideline
73.3% 70.4% 72.2% 0.74
Hawkins et al. (2016)
Radiomics – 23 features
51.7 % 92.9% 80.0% 0.83
Ma et al. (2016)
Radiomics – 583 features
80.0% 85.5% 82.7%
Buty et al. (2016)
DL – 400 SH and 4096 AlexNet features
82.4%
Kumar et al. (2015)
DL: 5000 features
79.1% 76.1% 77.5%
Proposed
Radiomics: two features (Size and Texture)
87.2% 81.2% 84.6% 0.89
17
DL: Deep Learning, SH: Spherical Harmonics
Choi et al., Medical Physics, 2018.

18. ACR Lung-RADS
Category Baseline Screening Malignancy
1 No PNs; PNs with calcification
Negative
<1% chance of malignancy
2
Solid/part-solid: <6 mm
GGN: <20 mm
Benign appearance
<1% chance of malignancy
3
Solid: ≥6 to <8 mm
Part-solid: ≥6 mm with solid component <6 mm
GGN: ≥20 mm
Probably benign
1-2% chance of malignancy
4A
Solid: ≥8 to <15 mm
Part-solid: ≥8 mm with solid component ≥6 and <8 mm
Suspicious
5-15% chance of malignancy
4B
Solid: ≥15 mm
Part-solid: Solid component ≥8 mm
>15% chance of malignancy
4X
Category 3 or 4 PNs with suspicious features (e.g. enlarged lymph
nodes) or suspicious imaging findings (e.g. spiculation)
>15% chance of malignancy
18
ACR: American College of Radiology
Lung-RADS: Lung CT Screening Reporting and Data System

19. Spiculation Quantification (Motivation)
• Blind Radiomics
• Semantic Features
• Semi-automatic Segmentation
- GrowCut and LevelSet
19
Radiologists spiculation score (RS) for different pulmonary nodules
1 2 3 4 5
Choi et al. in CMPB 2021

20. Spiculation Quantification (Methodology)
20
𝜖𝑖: = log
𝑗,𝑘 𝐴 ([𝜙(𝑣𝑖), 𝜙(𝑣𝑗), 𝜙(𝑣𝑘)])
𝑗,𝑘 𝐴 ([𝑣𝑖, 𝑣𝑗, 𝑣𝑘])
Area Distortion Map
Spherical Mapping
Eigenfunction

21. Spiculation Quantification (Results)
21
RS 1 2 3 4 5
s1 -0.2 -0.7 -0.6 -1.1 -1.8
s2 0.1 0.2 0.4 0.6 1.6
Ns 0 1 4 8 14
Nl 2 0 3 1 1
Na 1 14 4 0 1
Spiculation quantification and attachment detection via area distortion metric. The results of spiculation
detection (red line: baseline of peak, black line: medial axis of peak, red X: spiculation, black X:
lobulation, yellow X: attached peaks). Radiologist’s spiculation score (RS), and the proposed interpretable
spiculation features ( s1 : spiculation score, s2 : spiculation score, Ns : no. of spiculation, Nl : no. of
lobulation and Na : no. of attached peaks) are shown below.

22. Spiculation Quantification (Results: Comparison)
22
Choi et al. in CMPB 2021

23. Progression-free survival Prediction
after SBRT for early-stage NSCLC
23
Thor, Choi et al. ASTRO 2020
• 412 patients treated between 2006 and 2017
• PETs and CTs within three months prior to SBRT start.
• The median prescription dose was 50Gy in 5 fractions.

24. Progression-free survival Prediction (Results)
• PET entropy, CT number of peaks,
CT major axis, and gender.
• The most frequently selected model
included PET entropy and CT
number of peaks
- The c-index in the validation subset
was 0.77
- The prediction-stratified survival
indicated a clear separation between
the observed HR and LR
- e.g. a PFS of 60% was observed at 12
months in HR vs. 22 months in LR.
24
Thor, Choi et al. ASTRO 2020

25. Local tumor morphological changes
25
Jacobian Map
- Jacobian matrix: calculates rate of displacement change in each direction.
- Determinant indicates volumetric ratio of shrinkage/expansion.
𝐷𝑒𝑡 𝐽 =
𝐷𝑒𝑡 𝐽 > 1 volume expansion
𝐷𝑒𝑡 𝐽 = 1 no volume change
𝐷𝑒𝑡 𝐽 < 1 volume shrinkage
𝐷𝑒𝑡 𝐽 = 1.2 = 20% expansion
𝐷𝑒𝑡 𝐽 = 0.8 = 20% shrinkage (-20%)
Riyahi, Choi et al., PMB 2018

26. Local tumor morphological changes (Results)
26

27. Local tumor morphological changes (Results)
Features P-value AUC Correlation to responders
Minimum Jacobian 0.009 0.98 -0.79
Median Jacobian 0.046 0.95 -0.72
The P-value, AUC and correlation to responders for all significant features in univariate analysis
27
Riyahi, Choi et al., PMB 2018 SVM-LASSO: AUC 0.91

28. Aggressive Lung ADC Subtype Prediction (Motivation)
28
CT
MIP
PET/CT
Soild
CT PET/CT
Five classifications of lung ADC Travis et
al. JTO 2011
 Solid and MIP components: poor surgery/SBRT prognosis factor
 Benefit from lobectomy rather than limited resection
 Core biopsy (Leeman et al. IJROBP 2017)
 Minimally invasive, not routinely performed, sampling error (about 60%
agreement with pathology)
 Preoperative diagnostic CT and FDG PET/CT radiomics
 Non-invasive and routinely performed

29. Aggressive Lung ADC Subtype Prediction (Method & Results)
• Retrospectively enrolled 120 patients
- Stage I lung ADC, ≤2cm
- Preoperative diagnostic CT and FDG PET/CT
• Histopathologic endpoint
- Aggressiveness (Solid : 18 cases, MIP : 5 cases)
• 206 radiomic features & 14 clinical parameters
• SVM-LASSO model
29
Performance of the SVM-LASSO model to predict aggressive lung ADC
Choi et al. Manuscript under review
Box plots of SUVmax (FDR q=0.004) and PET Mean of
Cluster Shade (q=0.002)
Feature Sensitivity Specificity PPV NPV Accuracy AUC
Conventional SUVmax 57.8±4.6% 78.5±1.4% 39.2±2.3% 88.6±1.1% 74.5±1.4% 0.64±0.01
SVM-LASSO
PET Mean of Cluster
Shade
67.4±3.1% 86.0±1.1% 53.7±2.1% 91.7±1.0% 82.4±1.0% 0.78±0.01
p-value SUVmax vs. SVM-LASSO 0.002 1e-5 7e-8 3e-5 5e-8 0.03

30. Unsupervised Learning of Deep Learned Features
from Breast Cancer Images
30
Lee, Choi et al. IEEE BIBE 2020

31. Unsupervised Learning of Deep Learned Features
31
Slide name
Silhouette
optimal
number
Cluster set Accuracy F1-score
TCGA-A7-A0DA 29
[25, 22, 6,
2, 24, 14,
0, 20, 10]
0.8829 0.8929
TCGA-A2-A0YM 20 [7, 6, 1, 5] 0.8360 0.8863
TCGA-A2-A3XT 19
[13, 10, 5,
1, 2]
0.9316 0.9514
TCGA-BH-A0BG 8 [1, 5] 0.7828 0.6857
TCGA-E2-A1LS 26 [18, 5] 0.8495 0.8680
TCGA-OL-A66I 25
[7, 18, 12,
20, 1, 0]
0.7761 0.7091
TCGA-C8-A26Y 21
[9, 18, 12,
16]
0.8594 0.8122
Lee, Choi et al. IEEE BIBE 2020

32. PathCNN: interpretable convolutional neural networks
for survival prediction and pathway analysis applied to glioblastoma
32
Oh, Choi et al. Bioinformatics 2021, joint first author Source code: https://github.com/mskspi/PathCNN.
• CNNs have achieved great success
• A lack of interpretability remains
a key barrier
• Moreover, because biological array
data are generally represented in a
non-grid structured format
• PathCNN
An interpretable CNN model on
integrated multi-omics data using
a newly defined pathway image.

33. PathCNN: interpretable CNNs (results)
33
Cancer PathCNN Logistic
regression SVM with RBF Neural network MiNet
GBM 0.755 ± 0.009 0.668 ± 0.039 0.685 ± 0.037 0.692 ± 0.030 0.690 ± 0.032
LGG 0.877 ± 0.007 0.816 ± 0.036 0.884 ± 0.017 0.791 ± 0.031 0.854 ± 0.027
LUAD 0.637 ± 0.014 0.581 ± 0.028 0.624 ± 0.034 0.573 ± 0.031 0.597 ± 0.042
KIRC 0.709 ± 0.009 0.654 ± 0.034 0.684 ± 0.027 0.702 ± 0.028 0.659 ± 0.030
Comparison of predictive performance with benchmark methods in terms of the area
under the curve (AUC: mean ± standard deviation) over 30 iterations of the 5-fold
cross validation
Note: AUCs for PathCNN were obtained with three principal components. Bold = Highest AUC for each dataset.
SVM, support vector machine; RBF, radial basis function; MiNet, Multi-omics Integrative Net; GBM, glioblastoma
multiforme; LGG, low-grade glioma; LUAD, lung adenocarcinoma; KIRC, kidney cancer.
Oh, Choi et al. Bioinformatics 2021, joint first author Source code: https://github.com/mskspi/PathCNN.

34. Outline
 Radiomics - Decision Support Tools
• Lung Cancer Screening
• Tumor Response Prediction and Evaluation
• Aggressive Lung ADC subtype prediction
• Multimodal data: Pathology, Multiomics, etc.
 Auto Delineation and Variability Analysis
• Delineation Variability Quantification
• Dosimetric Consequences of Variabilities
• OARNet, Probabilistic U-Net
34

35. Delineation Variability Quantification and Simulation
A framework for radiation therapy variability analysis
35
RT plan
Structure Set
CT Image
Dose Distribution
Structure Sets
DV simulation
ASSD
GrowCut
RW
Other delineators
SV analysis
DV analysis
Geometric
Dosimetric
Variability analysis
Human DV
Simulated
DV
Consensus SS
OARNet
Choi et al., AAPM, 2019.

36. Delineation Variability Quantification and
Simulation
• ESTRO Falcon contour workshop (EduCase)
- A HNC case, Larynx, 70 Gy and 35 fractions
- 14 independent manually delineated (MD) OAR structure sets (SS)
- BrainStem, Esophagus, OralCavity, Parotid_L, Parotid_R, SpinalCord, and Thyroid
• Consensus MD SS
- The simultaneous truth and performance level estimation (STAPLE)
36
Choi, Nourzadeh et al., AAPM, 2019.

37. Delineation Variability Quantification and
Simulation (Methods)
•Geometric analysis
- Similarity: Dice coefficient (Volumetric, Surface)
- Distance: Hausdorff distance (HD), Actual Average Surface
Distance (AASD)
- Reference: STAPLE SS
•Dosimetric analysis
- Single dose distribution planned from a human SS
- DVH confidence bands (90%tile)
- 𝐷mean, 𝐷max, 𝐷min, 𝐷50
37
Choi, Nourzadeh et al., AAPM, 2019.

38. Delineation Variability Quantification and Simulation (Results)
• DVH variability not predicted by geometric measures
• Large human variability
38
100%
50%
0%
100%
50%
0%
Human ASSD GrowCut RW
Right
Parotid
Left
Parotid
Choi, Nourzadeh et al., AAPM, 2019.

39. Human ASSD GrowCut RW
100%
50%
0%
Education
100%
50%
0%
Clinic
39

40. Dose Volume Coverage Map (DVCM)
40
Human ASSD
GrowCut RW

41. • Plan Competition Data Set: A HNC case, Nasopharynx , 70 Gy
• PV: 409 plans for IMRT, VMAT, and Tomotherapy on various TPS
Eclipse, Monaco , Pinnacle, RayStation, Tomotherapy
• SV: Setup error simulation using Radiation Therapy Robustness
Analyzer (RTRA)
• 1000 simulations: 3mm translation and 5-degree rotation
• DV: 5 manually delineated (MD) SSs
• BrainStem, Esophagus, OralCavity, Parotid_L, Parotid_R, and SpinalCord
• 3x5 Simulated DVs using Radiation Therapy Variability Analyzer (RTVA)
Variability Analysis
Plan Variability, Setup Variability, Delineation Variability
41
Choi, Nourzadeh et al., AAPM, 2020.

42. Dose Volume Coverage Map (DVCM)
42
Plan Variability Setup Variability Delineation Variability
Choi, Nourzadeh et al., AAPM, 2020.

43. Plan Competition DVCM Analysis
43
Fractional
Volume
Dose
OAR Dose Constraint
Prob Threshold
5%

44. Plan Competition DVCM Analysis
44
TV SV×PV SV×DV PV×DV SV PV DV Average
BrainStem 0.15 0.11 0.10 0.07 0.03 0.01 0.02 0.07 >0.95
Chiasm 0.68 0.00 0.65 0.56 0.00 0.00 0.49 0.34
Eye_L 0.07 0.05 0.04 0.01 0.02 0.01 0.01 0.03
Eye_R 0.02 0.01 0.01 0.01 0.00 0.00 0.01 0.01 >0.5
Lens_L 1.00 1.00 1.00 1.00 0.99 1.00 0.01 0.86
Lens_R 1.00 1.00 1.00 1.00 1.00 1.00 0.01 0.86 >0.3
Mandible 0.04 0.04 0.00 0.07 0.00 0.08 0.01 0.03 >0.2
OpticNerve_L 0.51 0.42 0.43 0.36 0.17 0.00 0.34 0.32 >0.05
OpticNerve_R 0.56 0.38 0.61 0.38 0.20 0.00 0.39 0.36 ≤0.05
SpinalCord 0.13 0.12 0.05 0.05 0.04 0.01 0.02 0.06 =0.00
Average 0.42 0.31 0.39 0.35 0.25 0.21 0.13 0.29
Fractional volume affected by different variations when constraint failing probability > 5% (worst-case scenario)
Choi, Nourzadeh et al., AAPM, 2020.

45. OARNet: auto-delineate organs-at-risk (OARs) in
head and neck (H&N) CT image
45
Soomro, Nourzadeh, Choi et al., AAPM, 2020.

46. OARNet results
46

47. A Probabilistic U-Net for Segmentation of Ambiguous Images
Kohl et al. NeurIPS 2018 47

48. Segmentations on different Variability levels (middle 5) and their occupancy map
• Implemented the model using PyTorch
• Model trained using TCIA LIDC dataset (about 2000 nodules with up to 4 radiologists delineations)
• Titan Xp, 12GB (1 week for training)
• Unstable to train, 2D segmentation, tumor
A Probabilistic U-Net (Results)
48
Variability
Low High

49. A Probabilistic U-Net for Segmentation of Ambiguous Images
Wasserstein distance?
2D → 2.5D → 3D 49

50. Summary
 Radiomics - Decision Support Tools
• Lung Cancer Screening
• Tumor Response Prediction and Evaluation
• Aggressive Lung ADC subtype prediction
• Multimodal data: Pathology, Multiomics, etc.
 Auto Delineation and Variability Analysis
• Delineation Variability Quantification
• Dosimetric Consequences of Variabilities
• OARNet, Probabilistic U-Net
50

51. Short-term Future Works
• Human-Variability aware auto-delineation
• Variability quantification and simulation using generative models
• OARNet + Probabilistic U-Net → Probabilistic OARNet
• Develop interpretable radiomic features
• Improve spiculation quantification
• Multi-institution validation
• Integrate the radiomics framework into TPS
• Eclipse (C#), MIM (Python), RayStation (Python)
51

52. Long-term Future Works
• Comprehensive Framework for Cancer Imaging
• Multi-modal imaging
• Response prediction and evaluation (Pre, Mid, and Post)
• Longitudinal analysis of tumor change
• Shape analysis (e.g. Spiculation)
• Deep learning models
• Automation of Clinical Workflow
• Big Data Analytics: EMR, PACS, ROIS, Genomics, etc.
• Provide an informatics platform for comprehensive cancer therapy
52

53. Selected Publications
1. Jung Hun Oh*, Wookjin Choi* et al., “PathCNN: interpretable convolutional neural networks for survival prediction and
pathway analysis applied to glioblastoma”, Bioinformatics, 2021, *joint first author
2. Wookjin Choi et al., “ Reproducible and Interpretable Spiculation Quantification for Lung Cancer Screening”, Computer
Methods and Programs in Biomedicine”, 2021
3. Noemi Garau, Wookjin Choi, et al., “ External validation of radiomics‐based predictive models in low‐dose CT screening
for early lung cancer diagnosis”, Medical Physics, 2020
4. Jiahui Wang, Wookjin Choi et al., “Prediction of anal cancer recurrence after chemoradiotherapy using quantitative
image features extracted from serial 18F-FDG PET/CT”, Frontiers in oncology, 2019
5. Wookjin Choi et al., “Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer”,
Medical Physics, 2018
6. Sadegh Riyahi, Wookjin Choi, et al., “Quantifying local tumor morphological changes with Jacobian map for prediction of
pathologic tumor response to chemo-radiotherapy in locally advanced esophageal cancer”, Physics in Medicine and
Biology, 2018
7. Shan Tan, Laquan Li, Wookjin Choi, et al., “Adaptive region-growing with maximum curvature strategy for tumor
segmentation in 18F-FDG PET”, Physics in Medicine and Biology, 2017
8. Wookjin Choi et al., “Individually Optimized Contrast-Enhanced 4D-CT for Radiotherapy Simulation in Pancreatic Ductal
Adenocarcinoma”, Medical Physics, 2016
9. Wookjin Choi, Tae-Sun Choi, “Automated Pulmonary Nodule Detection based on Three-dimensional Shape-based
Feature Descriptor”, Computer Methods and Programs in Biomedicine, 2014
53
Complete list of publications: https://scholar.google.com/citations?user=iHgsGLUAAAAJ

54. 54
Thank You!
Q & A
https://qradiomics.wordpress.com
E-mail: wchoi@vsu.edu

55. Spiculation Quantification: Height, Area Distortion
• Area distortion better than solid angle for non-conic spicules
• Height is medial axis length, can be perpendicular distance
• Width is measured at base, can be FWHM
𝑠1 =
𝑖 mean 𝜖𝑝 𝑖 ∗ ℎ𝑝 𝑖
𝑖 ℎ𝑝 𝑖
𝑠2 =
𝑖 min 𝜖𝑝 𝑖 ∗ ℎ𝑝 𝑖
𝑖 ℎ𝑝 𝑖
55

56. Spiculation Quantification (Model Validation)
56

57. Local tumor morphological changes
(Motivation)
57

58. Delineation Variability Simulation (Methods)
• DV Simulation using auto-delineation (AD) methods (σ=2, 5, 10 mm)
• Average surface of standard deviation (ASSD): random perturbation
• GrowCut: cellular automata region growing
• Random walker (RW): probabilistic segmentation
58
Background 0
Foreground 1
Initial Binary Mask
Gaussian-Smoothed Mask
Gaussian Noises-Added Mask
Intensity
Spatial location
Inside
Outside
σ = 2mm σ = 5mm σ = 8mm σ = 10mm
Choi et al., AAPM, 2019.

59. OARNet comparisons
59
(a) Dice similarity coefficient and (b) Hausdorff comparison for the alternative delineation
methods. The points in the graphs are mean values and bars show the 95% confidence intervals.

60. 60
A matrix of adjusted P-values.The row represents the 146 KEGG pathways ordered on pathway images.
The columns represent the first two principal components of each omics type.The red color indicates key
pathways with adjusted P-values < 0.001