Big Data Clinical Image Analysis

Xiao Da, MSc

Functional Neuroimaging Laboratory

Brigham and Women's Hospital

Jul. 15. 2016

From Pipeline Design to Translational Applications

Outline• Big Data in Biomedical Science why big data clinical images?

• Image Analysis Pipeline how is it possible to analyze clinical images?

• Translational Applications opens up many opportunities.

Early onset of Alzheimer's Disease & Dementia prediction

Survival and molecular subtype prediction in glioblastoma

and image guided neurosurgery

Prediction of X-linked Adrenoleukodystrophy progression

Many many more

Glioblastoma

ALD

EHR

Millions of pixels for each image

3D

4D2D

Genetic Data

Neonatal Brain Injury – why big data clinical images?

Hypoxic Ischemic Encephalopathy (HIE)- affects 2-6/1000 newborns- occurs in the first 1-2 weeks of life, short of blood/oxygen- life-threatening, often long-term neuro-cognitive deficits

Timely Diagnosis is Crucial:- ADC maps are gold-standard: HIE->low ADC values- but 30-50% inter-reader variability, because- how low is too low; how low is within normal range?

Figure from Howlett et al, Ped Res, 2013

A fundamental question:How to Quantify Normal Variations?

Research-oriented Images

- Recruit normal volunteers- invest:

$$$$$$, years,

- outcome: 10s – 100s of normal images

Clinically-acquired Images

- No recruitment needed- invest:

$, weeks (vetting normal images),

- outcome: hundreds of normal images

normative abnormal

Motivating Example #1: Neuro-development

How to get and vet clinical images?

Mi2b2 engine: https://www.nmr.mgh.harvard.edu/lab/mi2b2Lead: Profs. Shawn Murphy, Randy Gollub (MGH) [Murphy et al, 2015]

N = 2,871- Scanned 2006-2013 with ADC maps

in Siemens 3T scanner- 0-6 years old at the time of scan- Radiological reports suggesting free

of abnormality

N=~100,000- Brain MRI in MGH

N = 1,648- ADC maps found and not corrupted

N = 705- ADC maps re-examined & confirmed to

be normal by a neuro-radiologist (Dr. Grant) and a neonatologist (Dr. Bates)

N = 201- Duplicates removed

- Still normal 3 years after the initial visit

To get To vet

* Ou et al, NeuroImage, 2015

Motivating Example #1: Neuro-development

Offering similar statistics to research data, and more…Motivating Example #1: Neuro-development

* Ou et al, NeuroImage, 2015

a) Whole-brain volume and ADC values, and changes

Statistically, at each age,measures from 1 atlas == measures from multiple individuals

Age-specific Normal ADC Atlases

Offering similar statistics to research data, and more…Motivating Example #1: Neuro-development

* Ou et al, NeuroImage, 2015

b) Regional and voxel-wise ADC values, and changes

measures from 1 atlas ~= measures from multiple individuals

Baseline Follow-up Jacobian (B->F)

0

1

2Case 1: Volume expanding

Case 2: Volume shrinking

*Ou, Da et al, MRM, 2014

c) Voxel-wise Spatially and Temporal Heterogeneous Changes

measures from 1 atlas > measures from multiple individuals ?

How much proportion of clinical data can we use?

Motivating Example #2: Neuro-oncology

normative abnormal

Research data Clinical data

Q: can we use abnormal images to quantify normal?

T1 with Gadolinium enhancement (T1c)

Tumor, or Normal Vessel?

A Neuro-Oncology Example

…

Where does a normal brain enhance?

- Normal people rarely undergo T1c- Tumor patients undergo T1c

Clinical Data from abnormal patients to quantify normalConstructing normal-appearing atlases from an abnormality-bearing cohort?

T1 with Gadolinium enhancement (T1c)

Tumor, or Normal Vessel?

…

Where does a normal brain enhance?

?

•Ou, Da et al, manuscript in preparation, 2016•* Atlases: https://www.nitrc.org/projects/stamp_atlases

Solution: Using the normative regions only for atlas construction

3D view(projection)

T1 with Gadolinium enhancement (T1c)

Tumor, or Normal Vessel?

tumor

Normalvessel

- Normal people rarely undergo T1c- Tumor patients undergo T1c

Motivating Example #2: Neuro-oncology

As long as we can analyze clinical images…

Analyzing Clinical Images: an Unmet Need

• Research-Oriented Images

- single site/scanner/sequence

- hundreds

- homogeneous FOV/contrast

- homogeneous age/disease

Pipelines: FSL, SPM, FreeSurfer, …

• Clinically-Acquired Images

- multi-site/scanner/sequence

- tens of thousands ++ (big data)

- diverse FOV/contrast

- diverse age/diseases

Pipeline: ?

Outline• Big Data in Biomedicine why big data clinical images?

• Image Analysis Pipeline how is it possible to analyze clinical images?

• Translational Applications opens up many opportunities.

Early onset of Alzheimer's Disease & Dementia prediction

Survival and molecular subtype prediction in glioblastoma

and image guided neurosurgery

Prediction of X-linked Adrenoleukodystrophy progression

Many many more

Glioblastoma

ALD

Technically, where do we start?

• Recall, In Medicine

Residents learn from existing patients Doctors see new patients

- how do structures look like in MRI? - structural annotation

- how do lesions look like in MRI? - disease diagnosis

- how early changes imply prognosis? - prognosis prediction

• So, in image analysis

Knowledge Translation

Training Guidance

Pipeline Design• Dicom Unpacking

• Field of View Normalization

• Skull Stripping

• Automatic Structural Annotation

• Multi-modal/channel Fusion

• Spatial Normalization, Density and Morphometry

• Atlas Construction (atlas-based detection)

• Longitudinal Change Quantification

• Disease Pattern Classification/Early Onset Prediction

• Quality Check

• ROI Statistical Analysis

• More

Ocean Water

Fresh Water

Multi-Atlas Skull Stripping

STAMP (STandardized Abnormality-free Multi-Parametric) atlases

*Ou, Da et al, manuscript to be submitted, 2016

Our Pipeline

*Ou, Da et al, Major Revision, 2016

FOV Normalization

3 tissue types2 hemispheres4 lobes151 structures

*Doshi et al, 2013, 2016

Structural Annotation

Quality Check

*Da et al, NeuroImage:Clinical, 2014

Disease Pattern Classification

Dicom Unpacking

*Ou, Da et al, 2014

Longitudinal Change Quantification

*Akbari, Da et al, 2016

Neurosurgery Navigation

Multi-ModalFusion

ROI Stat Analysis

*Ou et al, 2014, Davatzikos 2001

Spatial Normalization, Density and Morphometry

Pipeline’s Features:

• MGH/BCH Neuro-Development Data: 0-6 yo neuro-development from clinical big data, HIE characterization

• MGH Neuro-Oncology Datasets: TIV/CED/NHX clinical trials for brain tumor patients

• MGH Neurology Datasets: ALD and Stroke

• NiCK: Neurocognitive assessment and MRI analysis of children and young adults with chronic kidney disease

• PNC: Philadelphia Neurological Cohort study on healthy children and adolescents (Satterthwaite et al, 2013)

• ADNI: Alzheimer’s Disease Neuroimaging Initiative (Mueller et al, 2005)

• HANDLS: Healthy Aging in Neighborhoods of Diversity across the Life Span

• ACCORD: Action to Control Cardiovascular Risk in Diabetes (Buse et al, 2007)

• CARDIA: Coronary Artery Risk Development in young Adults (Hughes et al, 1987)

• SPRINT: Systolic Blood Pressure Intervention Trial

• WHIMS: Women's Health Initiative Memory Study (Shumaker et al, 1998)

• …… ……

Successful Applications (8000+ images in 10+ NIH studies, trials):

- Publicly-available;- Generally applicable: multi-site/vendor/scanner,

across age, health conditions,research and clinical images;

- Accurate and robust

Examples: people used our pipeline as basis for

Computational Psychiatry & Psychology

Neuro-Development and Neuro-Cognition

Oncology (Brain, Breast, Prostate)

Human brain

Other animals

* R01EB014947 * U01CA154601 * R01EB009234 * R01NS042645 * R01AG014971 * R01MH070365 * P41RR013642 * R01CA104976 * U24CA189523 * R01CA197000 * K23MH098130 * R00HD061485 ……

Outline• Big Data in Biomedicine why big data clinical images?

• Image Analysis Pipeline how is it possible to analyze clinical images?

• Translational Applications opens up many opportunities.

Early onset of Alzheimer's Disease & Dementia prediction

Survival and molecular subtype prediction in glioblastoma

and image guided neurosurgery

Prediction of X-linked Adrenoleukodystrophy progression

Many many more

Glioblastoma

ALD

Outline• Big Data in Biomedicine why big data clinical images?

• Image Analysis Pipeline how is it possible to analyze clinical images?

• Translational Applications opens up many opportunities.

Early onset of Alzheimer's Disease & Dementia prediction

Survival and molecular subtype prediction in glioblastoma

and image guided neurosurgery

Prediction of X-linked Adrenoleukodystrophy progression

Many many more

Glioblastoma

ALD

Alzheimer's Disease

200 subjects

Mild Cognitive Impairment 381 subjects

Cognitive Normal 232 subjects

Cognitive Scores(ADAS-Cog)

APOE ε4

CSF Biomarkers 192 MCITau Beta Amyloid

Prediction of early onset of AD

Imaging + Genetics, Predicting Conversion to Alzheimer

Normal Aging

Mild Cognitive Impairment (MCI)

Alzheimer’s Disease (AD)

stable or even reversible

Challenge: Convert or not?

Approach: all 4 sets of biomarkers

Multi-site/scanner ADNI data (N=813)AD: 200; CN: 232; MCI: 381

Findings:

Imaging marker ~= cognition test scores < combined

CSF markers improve imaging marker? No.

Imaging + cognitive tests good enough. ApoE improves? No.

Da et al, NeuroImage: Clinical, 2014

The only editorial highlighted article since the debut of NeuroImage: Clinical

Oral Presentation, RSNA 2013

How is the Relationship between Plasma Analytes and Brain Atrophy?

FDR Corrected p < 0.05

Outline• Big Data in Biomedicine why big data clinical images?

• Image Analysis Pipeline how is it possible to analyze clinical images?

• Translational Applications opens up many opportunities.

Early onset of Alzheimer's Disease & Dementia prediction

Survival and molecular subtype prediction in glioblastoma

and image guided neurosurgery

Prediction of X-linked Adrenoleukodystrophy progression

Many many more

Glioblastoma

ALD

Brain Tumor, Specifically Glioblastoma

• Glioma: 80% of brain cancer cases

• Glioblastoma Multiforme: Grade IV, 54% of Glioma

• GBM: 50(90)% die < 1(3) yr, median ~15 months

Challenges

• Spatial heterogeneity of tumors

• Genetically diverse

• Rely on relatively simple MRI measures

• Need accurate and reproducible biomarkers to enter routine clinical practice to predict the overall survival and molecular subtypes based on gene expression

Advanced Medical Image Analysis

Imaging: multi-modal, structural and mechanisticsMRI T1, T1c, T2, FLAIRDTI Diffusion: cellularity (cell density)DSC Perfusion: blood volume, blood flowDCE Permeability: vascular integrityMRS MR Spectroscopy: metabolism

Fundamentals:- Tumor is angiogenesis dependent- Imaging tumor microvasculature- Imaging tumor heterogeneity

Molecular Subtyping

Feature Extraction

Feature Selection

Machine Learning

Images Predicting Survival + Molecular Subtypes

• Imaging patterns predict patient survival by machine learning techniques

• Distinctive imaging phenotypes associated with GBM subtypes for tumor physiology and gene expression

• Integration of imaging markers (infiltration, cell density, microvascularity, and blood–brain barrier compromise) forms very accurate predictive biomarkers

• Informatics-derived imaging biomarkers of molecular composition will improve the diagnosis and evaluate treatment response over time and in response to targeted agents???

Conclusion

Leading Journal of Neuro Oncology and Cancer Research

Integra Foundation Award, CNS 2015

Multiparametric Images Guiding Neurosurgery

Editor’s Choice

Outline• Big Data in Biomedicine why big data clinical images?

• Image Analysis Pipeline how is it possible to analyze clinical images?

• Translational Applications opens up many opportunities.

Early onset of Alzheimer's Disease & Dementia prediction

Survival and molecular subtype prediction in glioblastoma

and image guided neurosurgery

Prediction of X-linked Adrenoleukodystrophy progression

Many many more

Glioblastoma

ALD

• Q: MR Perfusion microvascular abnormalities predictive of progression?

Neurology: X-linked Adrenoleukodystrophy

Multi-modal analysis: role of perfusion MRIi) Able to detect cerebral diseases 6 months before structural MRI;ii) Increased CTH predictive of lesion progression (conversion or not);

Population analysis: Spatial distributions of ALD lesions

Collaboration with Drs. Arne Lauer, Patricia Musolino, Florin Eichler (Neurology@MGH)

Lauer et al, AAN, 2016Lauer at al, in preparation, 2016

Capillary transit time

Outline• Big Data in Biomedicine why big data clinical images?

• Image Analysis Pipeline how is it possible to analyze clinical images?

• Translational Applications opens up many opportunities.

Early onset of Alzheimer's Disease & Dementia prediction

Survival and molecular subtype prediction in glioblastoma

and image guided neurosurgery

Prediction of X-linked Adrenoleukodystrophy progression

Many many more

Glioblastoma

ALD

Pattern Recognition for disease classification

Many many more

MGH GBM atlas VS Penn GBM atlas

Longitudinal change of tumor

OCT VS 13T

7T MRI

Functional near-infrared spectroscopyBrain Imaging

DCE MRI evaluating the treatment of Breast MetsPET enables visualization of drug delivery in GBM

Perfusion MRI guides therapy

Multiparametric MRI predictspseudo progression and recurrence

Many many moreNeurodegeneration Neuro-oncology

Neuropsychiatry

Metabolic DiseaseBrain Development

Far far away from solving the problem,but, a good step

Research-oriented images Clinically-acquired images

Reality HopeOpportunity

40

Thank you!MGH & BWH & McLean

Supervisors

Jayashree Kalpathy-Cramer

Elizabeth Gerstner

Yangming Ou

Bruce Rosen

Colleagues (Neurology)

Arne Lauer

Patricia Musolino

Florin Eichler

Colleagues (Radiology)

Otto Rapalino

Teammates

Yi-fen Yen

Xuezhu Cai

Kourosh Jafari Khouzani

Vyashak Chandra

John Evans

Artem Mamonov

Jonathan Cardona

Andrew Beers

University of Pennsylvania

Supervisors

Christos Davatzikos

Kilian Pohl

Colleagues (Breast Cancer)

Mitch Schnall

Susan Weinstein

Emily Conant

Sarah Englander

Jia Wu

Despina Kontos

Colleagues (Alzheimer's)

John Q. Trojanowski

Jon B. Toledo

David A. Wolk

Colleagues (Brain Tumor)

M. Sean Grady

Donald M. O'Rourke

Michel Bilello

Luke Macyszyn

Ragini Verma

Ali Gooya

Teammates

Yangming Ou

Andrea Schuh

Aris Sotiras

Guray Erus

Jimit Doshi

Hamed Akbari

Harsha Battapady

Vanessa Clark

Harini Eavani

Bilwaj Gaonkar

Meng-Kang Hsieh

Madhura Ingalhalikar

Dongjin Kwon

Erdem Varol

Dong Hye Ye

Aoyan Dong

Ke Zeng

Mohamad Habes

Vanessa Wallace

Colleagues (BWH)

Andrey Fedorov

Steve Pieper

Colleagues (McLean)

Yunjie Tong

Tufts

Advisors

Sergio Fantini

Angelo Sassaroli

University of Maryland

Shari Waldstein

Stanford

Kilian PohlDongjin Kwon

UCLA

Luke MacyszynBilwaj Gaonkar