GLISTRboost[11]

T1 T1-Gd T2 T2-FLAIR

EnhancingNon-enhancingEdema/Invasion

White MatterGray MatterCerebrospinal Fluid

Tumor labels

Healthy brain labels

Target Audience:

– Genera l Purpose Tools –

· Functionality ·

Primary Aim: To enable swift and efficient translation of cutting-edge academic research into clinically useful tools[1].

– Spec ia l i zed Appl icat ions –

Imaging Signature of EGFRvIII in GBM [5] Computational Study

of Brain Connectivity [6,7]

DTI-based

Resection Margin Estimation

Peritumoral Effects

of Glioblastoma

· Future Work ·

Interaction

Coordinate definition on

various tissue types Region AnnotationApproximation by a

sphere

References

[1] Davatzikos et al., Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome,

Journal of Medical Imaging, 2018

[2] Shinohara et al., Statistical normalization techniques for magnetic resonance imaging, Neuroimage Clinical, 2014

[3] Gaonkar et al., Adaptive geodesic transform for segmentation of vertebrae on CT images, Medical Imaging, 2014

[4] Yushkevich et al., ITK-SNAP: An interactive tool for semi-automatic segmentation of multi-modality biomedical images, IEEE Eng Med Biol Soc., 2016

[5] Bakas et al., In vivo detection of EGFRvIII in glioblastoma via perfusion magnetic resonance imaging signature consistent with deep peritumoral infiltration,

Clinical Cancer Research, 2017

[6] Tunc et al., Individualized Map of white matter pathways: connectivity-based paradigm for neurosurgical planning, Neurosurgery, 2016

[7] Tunc et al., Automated tract extraction via atlas based Adaptive Clustering, NeuroImage, 2014

[8] Keller et al., Estimation of breast percent density in raw and processed full field digital mammography images, Medical Physics, 2012

[9] Keller et al., Preliminary evaluation of the publicly available Laboratory for Breast Radiodensity Assessment (LIBRA) software tool, Breast Cancer Research 2015

[10] Li et al., Predicting treatment response and survival of early-stage non-small cell lung cancer patients treated with stereotactic body radiation therapy using

unsupervised two-way clustering of radiomic features, Int. Workshop on Pulmonary Imaging, 2017

[11] Bakas et al., GLISTRboost: Combining multimodal MRI segmentation, registration, and biophysical tumor growth modeling with gradient boosting machines for

glioma segmentation, Springer, LNCS, 2016

[12] Bakas et al., Advancing TCGA glioma MRI collections with expert segmentation labels and radiomic features, Nature Scientific Data, 2017.

[13] Akbari et al., Imaging Surrogates of infiltration obtained via multiparametric imaging pattern analysis predict subsequent location of recurrence of glioblastoma,

Neurosurgery, 2016

[14] Rathore et al., Radiomic signature of infiltration in peritumoral edema predicts subsequent recurrence in glioblastoma: Implications for personalized

radiotherapy planning, Journal of Medical Imaging, 2018

[15] Macyszyn et al., Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques, Neuro-Oncology, 2016

[16] Rathore et al., Imaging pattern analysis reveals three distinct phenotypic subtypes of GBM with different survival rates, Neuro-Oncology, 2016

[17] Kamnitsas et al., Efficient multi-Scale 3D CNN with fully connected CRF for accurate brain lesion segmentation, Medical Image Analysis, 2016

Breast Density Assessment [8,9]

(Cancer Risk Estimation)

Applied to more than

50,000 mammography

screening exams

GERawPD: 7.54%

GEProcessedPD: 75.6%

HologicRawPD: 22.5%

HologicProcessedPD: 22.8%

Cancer Imaging Phenomics Toolkit (CaPTk): A Radio(geno)mics Platform for Quantitative Imaging Analytics on Computational Oncology

C. Davatzikos, D. Kontos, P. Yushkevich, R. Shinohara, Y. Fan, R. VermaCenter for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania

www.cbica.upenn.edu/captk

FUNDING: ITCR U24CA189523

Specialized Segmentation

CTPET

Lung Tumor [10] LIBRA [8,9]

Segmentation [3,4]

ITK-SNAPQuantitative Feature Extraction

Textural

Voxel-based/ROI based

texture features

Gray-Level

Matrices

Local Binary

Patterns

1st Order Statistics Histogram-related

Intensity-basedVolume / Morphology

Model Training Functionality | Brain: Distinct radiographic subtypes of GBM[14] | Breast: Comprehensive parenchymal texture characterization | Deep Learning pipelines for segmentation[17]

• Racial disparities

• Strong associations with breast cancer risk

• Density changes after bariatric surgery

captk@cbica.upenn.edu

@CBICAannounce

github.com/cbica

@CBICA

Published in Nature Scientific Data[12]

enriching the TCGA-GBM & TCGA-LGG datasets with

manual tumor segmentations and radiomic features,

publicly available on the TCIA webpage.

Personalized Radiation Dose Escalation in areas of

higher likelihood of recurrence: Application of our

predictive maps to a trial funded by Abramson Cancer

Center and to an NRG trial.

GBM Recurrence Prediction [13, 14]

(Predictive Maps of Peritumoral Infiltration)

High Probabilityof Recurrence

Low Probabilityof Recurrence

Post-recurrence scan, with nodular enhancement in the predicted areas

Infiltration heatmap in pre-op T1-Gd

Cross-Platform ExtendableOpen-Source

1. Clinical experts: facilitating use of complex algorithms for clinically relevant studies through a user-friendly interface.

2. Computational experts: allowing for batch-processing and integration of new algorithms.

Easy to use

Input MRI DenoisingBias

CorrectionHistogram

Matching

T1-G

dT2-F

LA

IR

Pre-processing [2]

GBM Survival Prediction [15]

Kaplan-Meier Survival Curves

Survival (Months)

Cu

mu

lati

ve s

urv

ival ra

te (

%)

HR(low&high): 10.64 (95% CI 5.9-19.3, p<0.001)

HR(med&high): 3.88 (95% CI 2.3-606, p<0.001)

HR(low&med): 2.77 (95% CI 1.8-4.2, p<0.001)

Low SPI

Medium SPI

High SPI

Web-access ible

ipp.cbica.upenn.edu

(HPC-shared resources for computationally

demanding pipelines)

Spatial Distribution Atlases

Cla

ss 1

Cla

ss 2

100

80

60

40

20

0

0 20 40 60 80