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A Classification-based Glioma Diffusion Model Using MRI Data Marianne Morris 1,2 Russ Greiner 1,2, Jörg Sander 2, Albert Murtha 3, Mark Schmidt 1,2 1 Alberta Ingenuity Centre for Machine Learning 2 University of Alberta 3 Cross Cancer Institute, Alberta Cancer Board
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2 Predict Tumour Growth Why? Study tumour growth patterns Improve treatment planning initial tumour tumour 6 months later
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3 Outline Introduction Incremental Growth Modeling Features Models (UG, GW, CDM) Experiments
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4 Incremental Growth Model Iteratively assign each voxel around the active tumour border to tumour vs non-tumour Stops at termination condition Reaching a specified size of tumour … there’s no more voxels to add Several Approaches
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5 Incremental Growth Model Tumor
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6 Incremental Growth Model Tumor Neighbours
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7 Incremental Growth Model -++ -+ -+ -+ ++- Tumor
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8 Incremental Growth Model -++ -+ -+ -+ ++- Tumor Neighbours
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9 Incremental Growth Model -+ -+++ -++ -+- -+- -++-- ++ Tumor
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10 Incremental Growth Model -+ -+++ -++ -+- -+- -++-- ++ Tumor
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11 Which New Voxels to Add UG: Uniform Growth GW: Growth based on tissue types CDM: Classification-based diffusion
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12 Tumour growth modeling – uniform diffusion (UG) Radial uniform growth (in all directions alike) Original tumour Final tumour volume
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13 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 matterGrey matter Original tumour Final tumour volume
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14 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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15 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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16 Features Patient features Tumour properties Voxel features Features of neighbouring voxels A total of 75 features patient tumour voxel
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17 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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18 Features: Tumour Area-volume ratio Volume increase between 2 scans Percentage of edema
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19 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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20 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) 1 036 5 2 4 6 neighbours y x z
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21 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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22 CDM Classifier Voxel v becomes tumour given… q v = P Θ ( class ( v ) = tumour | e patient,e tumour,e v ) Features of the patient e patient the tumour e tumour the voxel and its neighbours e v patient tumour voxel v
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23 Learning Parameters (Classifier) How to learn Θ ? Naïve Bayes Logistic Regression Linear-kernel SVM Trained on other brain images
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24 Outline Introduction Incremental Growth Modeling Experiments Evaluation Measure Model Comparison Best Case Average Case Special Cases Average P/R
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25 Experimental Procedure Training data Sample of voxels in volume-difference between two scans including 2-voxel border around the volume at the 2 nd 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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26 Tumour growth modeling – CDM (wrt Neighbours) Voxel v becomes tumour based on… Features: e patient,e tumour,e v Compute: q v = P Θ ( class ( v ) = tumour | e patient,e tumour,e v ) Neighbours of voxel v If k tumour-voxel neighbours, probability that voxel v becomes tumour p v = 1 – ( 1 – q v ) k Decision Declare voxel v is tumour if p v 0.65 v6,v7 : k = 0 v1,v2,v5 : k = 1 v3,v4 : k = 2 v1v2v6v7v5 ++v3v4+ +++++
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27 System Performance Time 1 scan Time 2 scan CDM prediction Left to right: Slices from lower to upper brain True positives False positives False negatives
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28 Evaluation Precision, Recall for tumour, non-tumour voxels nt = truth & pt = prediction ; Precision = Recall Correct ntPredicted pt
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29 Diffusion Modeling Process We grow tumour from initial volume at 1 st time scan to size of tumour volume at 2 nd time scan Precision = Recall because predicted volume truth volume Tumour at 1 st time scan Tumour volume at 2 nd time scan
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30 Results – Model Comparison
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31 Results (Best case) GBM_7: CDM beats UG by 20% and GW by 12% True positives False positives False negatives Grew tumour along edema regions but… didn’t predict other wing of butterfly
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32 Results (Average case) GBM_1: CDM beats UG by 6% and GW by 8% True positives False positives False negatives Need a more accurate brain atlas
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33 Results (Special case) GBM_10: CDM beats UG by 8% and GW by 2% True positives False positives False negatives Resection & Recurrence
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34 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 mean Var: the variance n: the number of samples CDM performs significantly better than UG and GW!
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35 Future work More expressive features Spectroscopy, DTI, genetic data Larger dataset (treatment effect) Brain atlas (“highways” vs. “barriers”)
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36 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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37 Acknowledgements The University of Alberta; Dept of Computing Science The Alberta Ingenuity Centre for Machine Learning Cross Cancer Institute Alberta Cancer Board Brain Tumor Growth Prediction team
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