A Classification-based Glioma Diffusion Model Using MRI Data

Marianne Morris1,2

Russ Greiner1,2, Jörg Sander2, Albert Murtha3, Mark Schmidt1,2

1 Alberta Ingenuity Centre for Machine Learning 2 University of Alberta3 Cross Cancer Institute, Alberta Cancer Board

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Predict Tumour Growth

Why? Study tumour growth patterns Improve treatment planning

initial tumour tumour 6 months later

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Outline

Introduction Incremental Growth Modeling

Features Models (UG, GW, CDM)

Experiments

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Incremental Growth Model

Iteratively assign each voxel around the active tumour border to tumour vsnon-tumour

Stops at termination condition Reaching a specified size of tumour … there’s no more voxels to add

Several Approaches

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Incremental Growth Model

Tumor

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Incremental Growth Model

Tumor

Neighbours

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Incremental Growth Model

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Tumor

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Incremental Growth Model

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Tumor

Neighbours

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Incremental Growth Model

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+ +

Tumor

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Incremental Growth Model

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+ +

Tumor

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Which New Voxels to Add

UG: Uniform Growth GW: Growth based on tissue types CDM: Classification-based diffusion

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Tumour growth modeling – uniform diffusion (UG)

Radial uniform growth(in all directions alike)

Original tumour

Final tumour volume

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Tumour growth modeling –

White vs. Grey matter (GW)

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

Uniform growth: Yes!

GW model: If White matter: Yes! If Grey matter: 20%

CDM model: “Learn” tumour

growth pattern

Am I a tumour?

voxel

Active tumour border

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Classification-Based Diffusion Model (CDM) Preprocessing

Noise reduction Spatial registration Intensity Standardization Tissue segmentation Tumour segmentation

Feature extraction Classification Tumour growth modeling

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Features

Patient features Tumour properties Voxel features Features of neighbouring voxels

A total of 75 features

patient

tumour

voxel

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Features: Patient

Age Correlation between age and glioma grade

(more aggressive tumours occur in older patients; benign tumours in children)

patient

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Features: Tumour

Area-volume ratio Volume increase between 2

scans Percentage of edema

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Features: Voxel 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

tumour

voxel

tumour

voxel

tumour

edema

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Features: Neighbourhood

For each of 6 neighbours*

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 (not including diagonal ones)

10 36

5

2

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6 neighbours

y

x

z

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Classification-Based Diffusion Model (CDM) Preprocessing

Noise reduction Spatial registration Intensity Standardization Tissue segmentation Tumour segmentation

Feature extraction Classification Tumour growth modeling

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CDM Classifier

Voxel v becomes tumour given…qv = PΘ (class (v) = tumour | epatient,etumour,ev)

Features of the patient epatient

the tumour etumour

the voxel and its neighbours evpatient

tumour

voxel v

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Learning Parameters (Classifier)

How to learn Θ ? Naïve Bayes Logistic Regression Linear-kernel SVM

Trained on other brain images

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Outline

Introduction Incremental Growth Modeling Experiments

Evaluation Measure Model Comparison

Best Case Average Case Special Cases Average P/R

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Experimental Procedure

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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Tumour growth modeling –

CDM (wrt Neighbours) Voxel v becomes tumour based on…

Features: epatient,etumour,ev Compute:

qv = PΘ (class (v) = tumour | epatient,etumour,ev)

Neighbours of voxel v If k tumour-voxel neighbours,

probability that voxel v becomes tumourpv = 1 – (1 – qv)k

Decision Declare voxel v is tumour if pv 0.65

v6,v7 : k = 0v1,v2,v5 : k = 1v3,v4 : k = 2

v1

v2

v6

v7

v5

+ + v3

v4

+

+ + + + +

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System Performance

Time 1 scan

Time 2 scan

CDMprediction

Left to right: Slices from lower to upper brain

True positivesFalse positivesFalse negatives

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Evaluation

Precision, Recall for tumour, non-tumour voxels

nt = truth & pt = prediction ; Precision = Recall

Correct ntPredicted pt

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Diffusion Modeling Process

We grow tumour from initial volume at 1st time scan to size of tumour volume at 2nd time scan Precision = Recallbecause predicted volume truth

volume

Tumour at 1st time scan

Tumour volume at2nd time scan

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Results – Model Comparison

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Results (Best case)

GBM_7: CDM beats UG by 20% and GW by 12%

True positivesFalse positivesFalse negatives

Grew tumour along edema regions but…didn’t predict other wing of butterfly

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Results (Average case)

GBM_1: CDM beats UG by 6% and GW by 8%

True positivesFalse positivesFalse negatives

Need a more accurate brain atlas

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Results (Special case)

GBM_10: CDM beats UG by 8% and GW by 2%

True positivesFalse positivesFalse negatives

Resection & Recurrence

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Results T-test: the probability

that the means are not significantly different

Paired data (same data sample; different models) CDM vs UG: p = 0.001 CDM vs GW: p = 0.001

(UG vs GW: p = 0.034)

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

CDM performs significantlybetter than UG and GW!

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Future work More expressive

features Spectroscopy, DTI,

genetic data Larger dataset

(treatment effect) Brain atlas

(“highways” vs. “barriers”)

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Conclusion

Challenge: Predicting how brain tumours will grow

Answer: Learned model CDM performs significantly better than other existing models!

… can improve with additional data

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Acknowledgements

The University of Alberta;Dept of Computing Science

The Alberta Ingenuity Centre for Machine Learning

Cross Cancer InstituteAlberta Cancer Board

Brain Tumor Growth Prediction team