Spiculations are spikes on the surface of pulmonary nodule and are important predictors of malignancy in lung cancer. In this work, we introduced an interpretable, parameter-free technique for quantifying this critical feature using the area distortion metric from the spherical conformal (angle-preserving) parameterization. The conformal factor in the spherical mapping formulation provides a direct measure of spiculation which can be used to detect spikes and compute spike heights for geometrically-complex spiculations. The use of the area distortion metric from conformal mapping has never been exploited before in this context. Based on the area distortion metric and the spiculation height, we introduced a novel spiculation score. A combination of our spiculation measures was found to be highly correlated (Spearman's rank correlation coefficient ρ = 0.48) with the radiologist's spiculation score. These measures were also used in the radiomics framework to achieve state-of-the-art malignancy prediction accuracy of 88.9% on a publicly available dataset.

1. Interpretable Spiculation Quantification
for Lung Cancer Screening
Wookjin Choi, PhD et al.
Department of Medical Physics
choiw@mskcc.org
UKC 2018, Aug 41

2. Acknowledgements
Memorial Sloan Kettering Cancer Center
– Wei Lu PhD
– Sadegh Riyahi, PhD
– Jung Hun Oh, PhD
– Saad Nadeem, PhD
– James G. Mechalakos, PhD
– Joseph O. Deasy, PhD
– Andreas Rimner, MD
– Prasad Adusumilli, MD
– Chia-ju Liu, MD
– Wolfgang Weber, MD
University of Maryland School of Medicine
– Feng Jiang, MD, PhD
– Wengen Chen, MD, PhD
– Charles White, MD
– Seth Kligerman, MD
Stony Brook University
– Allen Tannenbaum, PhD
2
NIH/NCI Grant R01 CA172638 and NIH/NCI Cancer Center Support Grant P30 CA0
08748

3. Outline
Lung Cancer Screening
1. Deep Learning
2. Radiomics
3. Spiculation Quantification
4. Summary
5. Future Works
3

4. Lung Cancer Screening
4
 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%

5. 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
5
Summary of Lung-RADS categorization for baseline screening
ACR: American College of Radiology
Lung-RADS: Lung CT Screening Reporting and Data System

6. Outline
Lung Cancer Screening
1.Deep Learning
2. Radiomics
3. Spiculation Quantification
6

7. Deep Learning (Motivation)
7
• Data Science Bowl 2017 presented by
Booz|Allen|Hamilton & Kaggle
• Many deep learning methods were
proposed
– Top 10 teams: log loss 0.39~0.44

8. Deep Learning
(Methodology: 3D Fully Convolutional Neural Network)
• Nodule Detection network: Trained using TCIA LIDC-IRDI database, 883
cases
• Nodule Classification network: Transfer learning applied using the Nodule
Detection model and trained using 2/3 of Kaggle dataset, 1063 cases
8
Nodule Detection network Nodule Classification network

9. Deep Learning (Results)
9
• 3-fold cross-validation
– Nodule Detection: Sensitivity 95.1% with 5 false positives per scan
– Nodule Classification: Accuracy 67.4%
• Deep Learning: Feasible but not interpretable
Ranked 99th out of 1972 teams (Top 6%, Bronze medal)Loss

10. 3D Deep Leaky Noisy-or Network
Winner of Data Science Bowl 2017
Liao et al., arXiv:1711.08324 [cs.CV], 2017
AUC: 0.90 AUC: 0.87
10

11. Outline
Lung Cancer Screening
1. Deep Learning - feasible but not interpretable
2. Radiomics
3. Spiculation Quantification
11

12. Radiomics (Motivation)
12
 Controllable Feature Analysis
 More Interpretable
Lambin, et al. Eur J Cancer 2012
Aerts et al., Nature Communications, 2014

13. Radiomics Framework
• Automated Workflow (Python)
– Integrate all the radiomics components
– 3D Slicer, ITK (C++), Matlab, R, and Python
– Scalable: support multicore & grid computing
13
Source codes: https://github.com/taznux/radiomics-tools
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
Predicting
Response
• ROC analyses
• Prediction models
• Validation
• Tumor response
• Recurrence
• Survival

14. Radiomics (Methodology)
• TCIA LIDC-IDRI public data set (n=1,010)
– Multi-institutional data
– 72 cases evaluated (31 benign and 41 malignant cases)
• Consensus contour
GLCM GLRM
Texture features Intensity features
2D
Shape features
3D
14

15. Radiomics - SVM-LASSO Model
15
SVM classification
Distinctive feature identification
Malignant?
Predicted malignancy
Feature extraction
Yes
10x10-foldCV
10-foldCV
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)

16. 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
Radiomics (Results: Comparison)
DL: Deep Learning, SH: Spherical Harmonics
16Choi et al., Medical Physics, 2018.

17. Outline
Lung Cancer Screening
1. Deep Learning - feasible but not interpretable
2. Radiomics - concise model
3. Spiculation Quantification
17

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
Summary of Lung-RADS categorization for baseline screening
ACR: American College of Radiology
Lung-RADS: Lung CT Screening Reporting and Data System

19. Spiculation Quantification (Motivation)
19
• Blind Radiomics
• Semantic Features
Radiologist's spiculation score 𝑠 𝑟 for different pulmonary nodules

20. Spiculation Quantification (Methodology)
20
𝜖𝑖: = log
𝑗,𝑘 𝐴 ([𝜙(𝑣𝑖), 𝜙(𝑣𝑗), 𝜙(𝑣 𝑘)])
𝑗,𝑘 𝐴 ([𝑣𝑖, 𝑣𝑗, 𝑣 𝑘])
Area Distortion MapSpherical Mapping
𝑠1 =
𝑖 𝑚𝑒𝑎𝑛(𝜖 𝑝(𝑖)) ∗ ℎ 𝑝(𝑖)
𝑖 ℎ 𝑝(𝑖)
Area Distortion Spiculation Score

21. Angle Preserving Spherical Parameterization
21
Euclidean
Ricci Flow
Euclidean
Ricci Flow
Conformal
Welding
Stereographic
Projection
Nadeem et al., IEEE TVCG, 2017

22. Synthetic Spiculated Nodules (Results)
(a) 2mm spikes (b) 5mm spikes
Height of Spikes # Spikes # Isolated Spikes #Detected Spikes Measured Height
(a) 2mm 8 4 4 3.1±1.5mm
(b) 5mm 8 4 8 4.3±2.5mm
22

23. Spiculation Quantification (Results)
Higher correlation with the radiologist’s score (𝑠1: 0.67 AUC, P=4.29E-08, 𝜌=-0.33)
𝑠 𝑟:2, 𝑁𝑝:5, 𝑠1:-0.6 𝑠 𝑟:3 , 𝑁𝑝:6, 𝑠1:-0.9 𝑠 𝑟:4 , 𝑁𝑝:30, 𝑠1:-1.2 𝑠 𝑟:5 , 𝑁𝑝:15, 𝑠1:-1.6
𝑠 𝑟: radiologist's spiculation score, 𝑠1: proposed spiculation score
23

24. Spiculation Quantification (Results: Comparison)
Proposed Methods Mean Curvature
Mean curvature method (𝑠 𝑎: 0.59 AUC, P=0.068, 𝜌=0.22 and 𝑠 𝑏: 0.66 AUC, P=1.47E-07, 𝜌=0.29)
24

25. Sensitivity Specificity Accuracy AUC
Size+Texture (previous model) 87.20% 81.20% 84.60% 0.89
Size+Texture+Radiologist’s score 87.80% 87.10% 87.50% 0.91
Size+Texture+Our measures 92.68% 83.87% 88.89% 0.92
Spiculation Quantification (Results: Comparison)
25
Performance of Malignancy Prediction
• TCIA LIDC-IDRI public dataset
– 811 cases: only radiologist's review annotations at baseline screening
– 72 cases: with malignancy information
Choi et al. Accepted in SHAPEMI @ MICCAI 2018

26. Summary
Lung Cancer Screening
1. Deep Learning - feasible but not interpretable
2. Radiomics - concise model
3. Spiculation Quantification - interpretable feature
26

27. Future Works
• Independent validation of our radiomics model
– MSK lung screening data
– NLST data
• Radiomics Framework for Radiation Therapy
– Multi-modal imaging
– Response prediction (Pre, Post)
– Longitudinal analysis of tumor change
27

28. Thank You!
Q & A
https://qradiomics.wordpress.com
E-mail: choiw@mskcc.org
28