the cancer imaging phenomics toolkit (captk) · participating pis itcr u24/nci: cancer imaging...

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)


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


https://www.youtube.com/channel/UC69N7TN5bH2onj4dHcPLxxA

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




-n AAAC





Subject ID




-n AAAC





Input Co-registered images

and respective modalities to

write to CSV output




-n AAAC





Input mask, the labels on

which extraction needs to

happen and their respective

labels to write to CSV output




-n AAAC





Output File




-n AAAC




-p C:/test/parameters.csvParameters for Extraction




-n AAAC




-p C:/test/parameters.csvExample for GLCM


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


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

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

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

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


Bakas, et al. Clinical Cancer Research, 2017

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


the cancer imaging phenomics toolkit (captk) · participating pis itcr u24/nci: cancer imaging...

Documents