A Classification-based Glioma Diffusion Model Using MRI Data Marianne Morris 1,2 Russ Greiner 1,2,...

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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

Predict Tumour Growth

Why? Study tumour growth patterns Improve treatment planning

initial tumour tumour 6 months later

Outline

Introduction Incremental Growth Modeling

Features Models (UG, GW, CDM)

Experiments

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

Neighbours

- + + +

- + + - -

- + + +

- + + - -

Which New Voxels to Add

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

Tumour growth modeling – uniform diffusion (UG)

Radial uniform growth(in all directions alike)

Original tumour

Final tumour volume

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

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?

Active tumour border

Classification-Based Diffusion Model (CDM) Preprocessing

Noise reduction Spatial registration Intensity Standardization Tissue segmentation Tumour segmentation

Feature extraction Classification Tumour growth modeling

Features

Patient features Tumour properties Voxel features Features of neighbouring voxels

A total of 75 features

patient

tumour

Features: Patient

Age Correlation between age and glioma grade

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

patient

Features: Tumour

Area-volume ratio Volume increase between 2

scans Percentage of edema

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

Features: Neighbourhood

For each of 6 neighbours*

Edema Image intensities Tissue type derived from template Tissue type derived from patient’s

A neighbourhood in 3D is the 6 voxels immediately adjacent to some voxel v (not including diagonal ones)

6 neighbours

Classification-Based Diffusion Model (CDM) Preprocessing

Noise reduction Spatial registration Intensity Standardization Tissue segmentation Tumour segmentation

Feature extraction Classification Tumour growth modeling

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

Learning Parameters (Classifier)

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

Trained on other brain images

Outline

Introduction Incremental Growth Modeling Experiments

Evaluation Measure Model Comparison

Best Case Average Case Special Cases Average P/R

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

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

+ + v3

+ + + + +

System Performance

Time 1 scan

Time 2 scan

CDMprediction

Left to right: Slices from lower to upper brain

True positivesFalse positivesFalse negatives

Evaluation

Precision, Recall for tumour, non-tumour voxels

nt = truth & pt = prediction ; Precision = Recall

Correct ntPredicted pt

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

Results – Model Comparison

Results (Best case)

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

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

Results (Average case)

Need a more accurate brain atlas

Results (Special case)

Resection & Recurrence

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!

Future work More expressive

features Spectroscopy, DTI,

genetic data Larger dataset

(treatment effect) Brain atlas

(“highways” vs. “barriers”)

Conclusion

Challenge: Predicting how brain tumours will grow

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

… can improve with additional data

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

A Classification-based Glioma Diffusion Model Using MRI Data Marianne Morris 1,2 Russ Greiner 1,2,...

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