Classification-based Glioma Diffusion Modeling

Marianne Morris

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Overview Introduction

Motivation Assumptions

Related Work Framework Contribution Results Conclusions

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Introduction

Task: Where to irradiate! What is a glioma? What is tumour diffusion

modeling? Brain Biology MRI Radiotherapy

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Task Goal: Effective radiotherapy of Brain Tumours

determine what region of brain to treat (irradiate) Problem:

Just targeting visible tumour cells is NOT enough… Must also kill “(radiologically) occult”

cancer cells surrounding tumour ! Current Approach:

Irradiate 2cm margin around tumour Not known if

this area contains occult cells ONLY this area contains occult cells

Treated area

?? Normal tissue+

Occult cells ??

tumour

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Better Approach

Locate brain tumours from MRI scan Predict “(radiologically) occult” cancer

cells surrounding tumour predictor learned from earlier MRI data sets

Treat tumour + predicted-occult region Meaningful as current techniques can zap

arbitrary shapes!

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Underlying assumptions Occult cells future tumour growth Probability of growth of tumour T into

adjacent voxel V is determined by properties of T: growth rate, histology properties of V: location, intensity, tissue type

Voxel properties are known throughout brain

Uniformity of brain tumour characteristics

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What is a glioma? A primary

brain tumour that originated from a cell of the nervous system

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Diffusion Model

Tumor

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Diffusion Model

Tumor

Neighbours

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Diffusion Model

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Tumor

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Diffusion Model

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Tumor

Neighbours

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Diffusion Model

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Tumor

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Diffusion Model

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Tumor

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Diffusion Model

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Brain Biology

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MRIMagnetic Resonance Imaging

Magnet

signal

Echo signaldetected

Signal reconstructedinto image

Signal intensity (on image) determined by T1, T2 relaxation times

Time line in minutes00: T2 scanning05: T1 scanning10: contrast15: T1-contrast scanning

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MRI – image views

Axial Sagittal Coronal

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MRI – image types

T1 T1-contrast T2

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Tissue differentiation on MRI scans

TissueT1-weighted

T2-weighted

Bone Dark Dark

Air Dark Dark

Fat Bright Dark

Water Dark Bright

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MRI – image types

T1 T1-contrast T2

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T1-Contrast scan (axial) Tumour is bright

white structure

Necrotic region is black structure dead cells in center

of tumour

Edema may surround tumour swelling of normal

tissue

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Radiotherapy

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Radiotherapy

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Current Treatment Region

Irradiate everything within 2 cm margin around tumour

… includes Occult cells Normal cells

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Better Treatment Region

Irradiate Tumour Occult cells Minimal number of

normal cells - minimize loss of brain function

Higher dose of radiation – smaller chance of recurrent cancer

Radiotherapy can zap arbitrary shapes!

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Overview

Introduction Related Work Framework Contribution Results Conclusions

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Related work

Modeling macroscopic glioma growth 3D cellular automata (Kansal et al., 2000)

Differential motility in grey vs. white matter (Swanson et al., 2002)

White matter tract invasion (Clatz et al., 2004)

Supervised treatment planning (Zizzari, 2004)

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Related work

3D cellular automata Describes the transition of cells within

the tumour from dividing to necrotic Does not assume uniform radial growth Does not account for biological factors Too simple to model real tumour

growthProliferating Inactive Necrotic

Kansal et al., 2000

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Related work

A 5:1 ratio in white vs. grey matter

Rate of change of tumour cell density =Diffusion of tumour cells + Growth of

tumour

Dw = 5 Dg

Swanson et al., 2000

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Related work White matter tract invasion – DTI*

Uses anatomical atlas of white fibers Initiates simulation from a tumour at time 1 Uses diffusion-reaction equation Evaluates results against tumour at time 2

Only one test patient (GBM)

*Diffusion Tensor Imaging

Clatz et al., 2004

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Related work

Modeling macroscopic GBM growth Differential equations; diffusion-reaction

Supervised treatment planning Predicts treatment volume using ANN Trains on control points in predicted

clinical volume vs. truth treatment volume Does not consider brain or patient info

Zizzari, 2004

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Overview

Introduction Related Work Framework Contribution Results Conclusions

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Framework Noise reduction Spatial registration Intensity Standardization Tissue segmentation Tumour segmentation Feature extraction Classification Tumour growth modeling

Contribution

Preprocessing

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Framework

Noise Reduction

Spatial Registration

Intensity Standardization

Tissue Segmentation

Tumour Segmentation

Preprocessing

Feature Extraction

Classification

Tumour Diffusion Modeling

Contribution

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Framework Noise reduction Spatial registration Intensity Standardization Tissue segmentation Tumour segmentation Feature extraction Classification Tumour growth modeling

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Noise reduction

Inter-slice intensity variation reduction Reduction of sudden changes in intensity

values across the slices of a scan Using Weighted Linear Regression

Intensity inhomogeneity reduction Reduction of a varying spatial field

across the scan – inherent to MR imaging Using Statistical Parametric Mapping

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Inter-slice intensity variation

Before inter-slice intensity variation reduction

After inter-slice intensity variation reduction

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Framework Noise reduction Spatial registration Intensity Standardization Tissue segmentation Tumour segmentation Feature extraction Classification Tumour growth modeling

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Spatial registration Using Statistical Parametric Mapping*

Linear template registration Registering to same coordinate system

Non-linear warping Applying deformations to lineup to template

Spatial interpolation Filling inter-slice gaps and computing

intensities

*Algorithms specifically designed for the analysis and processing of MRI brain scans

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Spatial registration Template example

Colin Holmes template Average T2 template

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Spatial registration

Before registration

After registration

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Framework Noise reduction Spatial registration Intensity Standardization Tissue segmentation Tumour segmentation Feature extraction Classification Tumour growth modeling

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Intensity Standardization

Reduction of intensity variations across scans

Using Weighted Linear Regression

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Intensity Standardization

After intensity standardization

Before intensity standardization

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Framework Noise reduction Spatial registration Intensity Standardization Tissue segmentation Tumour segmentation Feature extraction Classification Tumour growth modeling

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Tissue segmentation

White matter Grey matter Cerebrospinal fluid

Using Statistical Parametric Mapping

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Framework Noise reduction Spatial registration Intensity Standardization Tissue segmentation Tumour segmentation Feature extraction Classification Tumour growth modeling

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Tumour segmentation

Slice from patient’s scan Segmented tumour

Tumour contour drawn by human experts

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Framework Noise reduction Spatial registration Intensity Standardization Tissue segmentation Tumour segmentation Feature extraction Classification Tumour growth modeling

Contribution

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Features

Patient features Tumour properties Voxel features Neighbourhood attributes

A total of 76 features

patient

tumour

voxel

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Features

Patient attributes Age

Correlation between age and glioma grade (more aggressive tumours occur in older patients; benign tumours in children)

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Features

Tumour properties Growth rate of tumour mass Percentage of edema Area-volume ratio Volume increase between 2 scans

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Features Voxel features

Min Distance from tumour border Tissue type derived from template Tissue type derived from patient’s image Image intensities (T1, T1-contrast, T2) Template intensity Edema region Coordinates & Tissue Map Distance-Area ratio

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Features

Neighbourhood* features Edema Image intensities Tissue type derived from template Tissue type derived from patient’s

image

* A neighbourhood in 3D is the 6 voxels immediately adjacent to some voxel v

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Framework Noise reduction Spatial registration Intensity Standardization Tissue segmentation Tumour segmentation Feature extraction Classification Tumour growth modeling

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Classification

Task description Training and testing data Classifiers

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Classification – Task description

Diffusion model that iteratively assigns each voxel around the active tumour border to tumour or non-tumour class Learn a classifier from data of 17

patients Test on unlabeled brain volume Use labels predicted by classifier as

input to diffusion algorithm

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Classification Training data

Sample of voxels in volume-difference between two scans including 2-voxel border around the volume at the 2nd time scan

Volume-pairs for 17 patients Total of ½ million voxels

We evaluate voxels encountered in diffusion process Cross-validation (17 patients)

Original tumour

Additional tumour growth

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Classification

Classifiers Naïve Bayes Logistic Regression Linear-kernel SVM

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Framework Noise reduction Spatial registration Intensity Standardization Tissue segmentation Tumour segmentation Feature extraction Classification Tumour growth modeling

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Tumour growth modeling

Uniform diffusion Growth based on tissue types Classification-based diffusion CDM

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Tumour growth modeling – uniform diffusion

Radial uniform growth(in all directions alike)

Original tumour

Final tumour volume

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Tumour growth modeling –

White vs. matter

A 5:1 ratio for diffusion in white matter vs. grey matter (Sawnson et al., 2000)

White matter Grey matter

Original tumourFinal tumour volume

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Tumour growth modeling –

CDM Based on…

Features of patient, tumour and voxels around the tumour

Labels predicted by classifier Number of tumour-voxel neighbours

pi = 1 – (1 – qi)k

pi is probability that voxel i becomes tumour

Learns qi by training

qi = PΘ(l (vi) = tumour | epatient,etumour,ei) k is # tumour-voxel neighbours

Uses probability threshold pi > 0.65

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Tumour growth modeling - CDM

Tumor

Neighbours

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Tumour growth modeling - CDM

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Tumor

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Tumour growth modeling - CDM

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Tumor

Neighbours

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Tumour growth modeling - CDM

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Tumor

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Tumour growth modeling - CDM

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Tumor

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Overview

Introduction Related Work Framework Contribution Results Conclusions

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Results

Evaluation measure Best case Average case Special cases Average P/R (CDM, UG, GW)

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Results (evaluation measure)

Precision|nt ∩ pt|

|pt|

Recall |nt ∩ pt|

|nt|nt = truth & pt = prediction ; Precision = Recall

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Results (Best case)

CDM beats UG by 20% and GW by 12%

True positivesFalse positivesFalse negatives

Didn’t predict other wing of butterfly

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Results (Average case)

CDM beats UG by 6% and GW by 8%

True positivesFalse positivesFalse negatives

Didn’t predict growth in lower brain

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Results (Special case)

CDM beats UG by 8% and GW by 2%

True positivesFalse positivesFalse negatives

Resection & Recurrence

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Results

Average Recall

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CDM UG GW

Average Recall

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Results

Average Precision

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CDM UG GW

Average Precision

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Results T-test: the probability

that the means are not significantly different

Paired data (same data sample; different models) P(CDM vs. UG) = 0.001 P(CDM vs. GW) = 0.001 P(UG vs. GW) = 0.034

X is the meanVar: the variancen: the number of samples

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Overview

Introduction Related Work Framework Contribution Results Conclusions

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Conclusions

Challenging problem Still feasible Future research directions

More expressive features Spectroscopy, DTI, genetic data

Larger dataset (treatment effect) Brain atlas (“highways” vs. “barriers”)

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Acknowledgements

Dr. Russ Greiner & Dr. Jörg Sander Dr. Albert Murtha (Radiation Oncology, CCI)

BTGP team Mark Schmidt Stephen Walsh Chi Hoon Lee Alden Flatt, Luiza Antonie, Gabi Moise

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References Clatz et al., 2004, In Silico Tumour Growth: Application

to Glioblastomas, MICCAI 2004, 337--345 Kansal et al., 2000, Simulated brain tumour growth

dynamics using a three-dimensional cellular automaton, J Theor Biol., 203:367--382

SPM (online) - http://www.fil.ion.bpmf.ac.uk/spm/ Swanson et al., 2000, A quantitative model for

differential motility of gliomas in grey and white matter, Cell Prolif., 33:317--329

Zizzari 2004, Methods on Tumor Recognition and Planning Target Prediction for the Radiotherapy of Cancer, PhD Thesis, University of Magdeburg

Schmidt 2005, Automatic Brain Tumour Segmentation, MSc thesis, University of Alberta