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Center for Biomedical Image Computing and Analytics
Computational Breast Imaging Group
Penn Image Computing and Science Lab
Penn Statistical Imaging and Visualization Endeavor
Section for Biomedical Image Analysis
The Cancer imaging Phenomics Toolkit
(CaPTk)
Christos Davatzikos, on behalf of the team
www.med.upenn.edu/cbica/captk
Participating PIs
ITCR U24/NCI: Cancer Imaging
Phenomics
Computational Breast Cancer Imaging R01 (NCI; Kontos, Conant, Schnall, Weinstein)
Computational Neuro-oncology R01 (NINDS;
Davatzikos and O’Rourke)
Image Segmentation and Analysis R01 (NBIB;
Yushkevich)
Big-Data Imaging Biostatistics R01 (NINDS; Shinohara)
Kinetic Modeling P01 (NCI; Mankoff, Doot, Pryma, Fan)
Brain Connectomics R01 (NINDS; Verma and Brem)
www.med.upenn.edu/cbica/captk
Feature Synthesis and Integrationvia Machine Learning
Second Level
Segmentation:
Regions of Interest (ROIs)
Registration:- Measure change with time:
- Population atlases:
A B B→A
Common
vs.
Rare
Image Operations:
• DICOM access
• Format conversion
• Intensity normalization
• Co-registration
• Noise Reduction
• ROI annotation
• Seed-point initialization
Feature Extraction:
Texture, histogram,
dynamics, spatial patternWavelet-DP1
Image
Harmonization:
Image Analysis Algorithms
CaPTk Radiomic Panel
Open-CV
Output Modules and Outcomes
Personalized Treatment:
Connectomics Infiltration
Predictive models:
Before After
RadiogenomicsImaging signatures of molecular characteristics:
Breast MRI Phenotypes
vs. Oncotype DXImaging Signatures of
GBM mutations
First Level
Precision Diagnosis,
Risk Estimation:
Kaplan-Meier EstimatorBreast Density Factor
vIII+
vIII-
EGFRvIII+ EGFRvIII-
ITK
MOVIE CLIPAvailable in: www.youtube.com/channel/UC69N7TN5bH2onj4dHcPLxxA
www.med.upenn.edu/cbica/captk
Feature Extraction on Command Line
FeatureExtraction.exe
-n AAAC
-i C:/test/t1.nii.gz,C:/test/t2.nii.gz -t T1,T2
-m C:/test/mask.nii.gz -r 1,3 -l ED,NC
-o C:/test/output.csv
-p C:/test/parameters.csv
www.med.upenn.edu/cbica/captk
Feature Extraction on Command Line
FeatureExtraction.exe
-n AAAC
-i C:/test/t1.nii.gz,C:/test/t2.nii.gz -t T1,T2
-m C:/test/mask.nii.gz -r 1,3 -l ED,NC
-o C:/test/output.csv
-p C:/test/parameters.csv
Subject ID
www.med.upenn.edu/cbica/captk
Feature Extraction on Command Line
FeatureExtraction.exe
-n AAAC
-i C:/test/t1.nii.gz,C:/test/t2.nii.gz -t T1,T2
-m C:/test/mask.nii.gz -r 1,3 -l ED,NC
-o C:/test/output.csv
-p C:/test/parameters.csv
Input Co-registered images
and respective modalities to
write to CSV output
www.med.upenn.edu/cbica/captk
Feature Extraction on Command Line
FeatureExtraction.exe
-n AAAC
-i C:/test/t1.nii.gz,C:/test/t2.nii.gz -t T1,T2
-m C:/test/mask.nii.gz -r 1,3 -l ED,NC
-o C:/test/output.csv
-p C:/test/parameters.csv
Input mask, the labels on
which extraction needs to
happen and their respective
labels to write to CSV output
www.med.upenn.edu/cbica/captk
Feature Extraction on Command Line
FeatureExtraction.exe
-n AAAC
-i C:/test/t1.nii.gz,C:/test/t2.nii.gz -t T1,T2
-m C:/test/mask.nii.gz -r 1,3 -l ED,NC
-o C:/test/output.csv
-p C:/test/parameters.csv
Output File
www.med.upenn.edu/cbica/captk
Feature Extraction on Command Line
FeatureExtraction.exe
-n AAAC
-i C:/test/t1.nii.gz,C:/test/t2.nii.gz -t T1,T2
-m C:/test/mask.nii.gz -r 1,3 -l ED,NC
-o C:/test/output.csv
-p C:/test/parameters.csvParameters for Extraction
www.med.upenn.edu/cbica/captk
Feature Extraction on Command Line
FeatureExtraction.exe
-n AAAC
-i C:/test/t1.nii.gz,C:/test/t2.nii.gz -t T1,T2
-m C:/test/mask.nii.gz -r 1,3 -l ED,NC
-o C:/test/output.csv
-p C:/test/parameters.csvExample for GLCM
www.med.upenn.edu/cbica/captk
5 Representative Examples
• Precision Diagnostics using Quantitative Imaging Signatures
• Risk Assessment for Breast Cancer
• Treatment Guidance in Neurosurgery and Radiation Therapy
• Imaging Signatures of Mutations
• Predicting Response to Treatment and Patient Survival
www.med.upenn.edu/cbica/captk
5 Representative Examples
• Precision Diagnostics using Quantitative Imaging Signatures
• Risk Assessment for Breast Cancer
• Treatment Guidance in Neurosurgery and Radiation Therapy
• Imaging Signatures of Mutations
• Predicting Response to Treatment and Patient Survival
www.med.upenn.edu/cbica/captk
Imaging Subtypes of Glioblastoma
Rathore, et al., Scientific Reports, 2018
Added Value of Imaging Subtypes Beyond IDH1 status
Survival 45 months
WHO 2016
RNAseq-based Classifications
Rathore, et al., Scientific Reports, 2018
5 Representative Examples
• Precision Diagnostics using Quantitative Imaging Signatures
• Risk Assessment for Breast Cancer
• Treatment Guidance in Neurosurgery and Radiation Therapy
• Imaging Signatures of Mutations
• Predicting Response to Treatment and Patient Survival
www.med.upenn.edu/cbica/captk
Breast Tissue Characterization
Density Pattern Fibroglandular Tissue Volume
Breast Complexity Index (BCI)
Keller, et al., MICCAI, 2011; Keller, et al., Medical Physics, 2012Wu, et al., Medical Physics, 2013; Wu, et al., BCR, 2015
Risk Prediction via PD% and Parenchymal Texture Descriptors
• Retrospective case-control study
• 106 Women with Unilateral Cancer
• 318 Age-matched Controls
• Contra-lateral FFDM Images (MLO)
• Categorical Density (PD%) and texture: 25 Imaging Features
• From screening Questionnaires
• Parity, Age at Menarche, Ethnicity,
Family Hx, #Biopsies
• 10 Significant Predictors (p<0.05): Age at menarche, ethnicity, PD%, and 7 texture features
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1 - Specificity
Sen
sit
ivit
y
Texture + PD% + Standard Risk Factors; AUC: 0.84
Standard Risk Factors + PD%; AUC: 0.64
Reference Line; AUC: 0.5
Model AUC Delong
Standard Risk Factors + PD% 0.64 p<0.001
Texture + PD% + Standard Risk Factors
0.84
www.med.upenn.edu/cbica/captkKeller, et al., RSNA, 2013
MOVIE CLIPAvailable in: www.youtube.com/channel/UC69N7TN5bH2onj4dHcPLxxA
www.med.upenn.edu/cbica/captk
5 Representative Examples
• Precision Diagnostics using Quantitative Imaging Signatures
• Risk Assessment for Breast Cancer
• Treatment Guidance in Neurosurgery and Radiation Therapy
• Imaging Signatures of Mutations
• Predicting Response to Treatment and Patient Survival
www.med.upenn.edu/cbica/captk
Peri-Tumoral Dose Escalation Guided by CaPTkPredictive mapof recurrence
Actual recurrence
Dose escalation clinical trial at Penn guided by predictive maps (20 patients)
10 times more likely to recur
Akbari, et al., Neurosurgery, 2015; Rathore, et al., J. Med. Imag., 2018
MOVIE CLIPAvailable in: www.youtube.com/channel/UC69N7TN5bH2onj4dHcPLxxA
www.med.upenn.edu/cbica/captk
5 Representative Examples
• Precision Diagnostics using Quantitative Imaging Signatures
• Risk Assessment for Breast Cancer
• Treatment Guidance in Neurosurgery and Radiation Therapy
• Imaging Signatures of Mutations
• Predicting Response to Treatment and Patient Survival
www.med.upenn.edu/cbica/captk
The Φ index: Imaging signature of EGFRvIII
Bakas et al. Clinical Cancer Research, 2017
142 de novo GBM patients
Bakas, et al. Clinical Cancer Research, 2017; Binder, et al. Cancer Cell, 2018Akbari, Bakas, et al. Neuro-Oncology, 2018
EGFRVIII-EGFRVIII +
Zooming into the imaging signature: Average perfusion curves
www.med.upenn.edu/cbica/captk
Bakas, et al. Clinical Cancer Research, 2017
MOVIE CLIPAvailable in: www.youtube.com/channel/UC69N7TN5bH2onj4dHcPLxxA
www.med.upenn.edu/cbica/captk
5 Representative Examples
• Precision Diagnostics using Quantitative Imaging Signatures
• Risk Assessment for Breast Cancer
• Treatment Guidance in Neurosurgery and Radiation Therapy
• Imaging Signatures of Mutations
• Predicting Response to Treatment and Patient Survival
www.med.upenn.edu/cbica/captk
Imaging features predict response to treatment
Ashraf et al., IEEE TMI 2013; Mahrooghy et al. IEEE TBME 2015
Breast Cancer Phenotyping via Imaging:
• Segmentation and multi-parametric feature extraction
• Identification of intrinsic phenotype patterns
• Prognostication and treatment response prediction
www.med.upenn.edu/cbica/captk
MOVIE CLIPAvailable in: www.youtube.com/channel/UC69N7TN5bH2onj4dHcPLxxA
www.med.upenn.edu/cbica/captk
www.med.upenn.edu/cbica/captk