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

2 Predict Tumour Growth Why? Study tumour growth patterns Improve treatment planning initial tumour tumour 6 months later

3 Outline Introduction Incremental Growth Modeling Features Models (UG, GW, CDM) Experiments

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

5 Incremental Growth Model Tumor

6 Incremental Growth Model Tumor Neighbours

7 Incremental Growth Model Tumor

8 Incremental Growth Model Tumor Neighbours

9 Incremental Growth Model Tumor

10 Incremental Growth Model Tumor

11 Which New Voxels to Add UG: Uniform Growth GW: Growth based on tissue types CDM: Classification-based diffusion

12 Tumour growth modeling – uniform diffusion (UG) Radial uniform growth (in all directions alike) Original tumour Final tumour volume

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

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

15 Classification-Based Diffusion Model (CDM) Preprocessing Noise reduction Spatial registration Intensity Standardization Tissue segmentation Tumour segmentation Feature extraction Classification Tumour growth modeling

16 Features Patient features Tumour properties Voxel features Features of neighbouring voxels A total of 75 features patient tumour voxel

17 Features: Patient Age Correlation between age and glioma grade (more aggressive tumours occur in older patients; benign tumours in children) patient

18 Features: Tumour Area-volume ratio Volume increase between 2 scans Percentage of edema

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

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) neighbours y x z

21 Classification-Based Diffusion Model (CDM) Preprocessing Noise reduction Spatial registration Intensity Standardization Tissue segmentation Tumour segmentation Feature extraction Classification Tumour growth modeling

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

23 Learning Parameters (Classifier) How to learn Θ ? Naïve Bayes Logistic Regression Linear-kernel SVM Trained on other brain images

24 Outline Introduction Incremental Growth Modeling Experiments Evaluation Measure Model Comparison Best Case Average Case Special Cases Average P/R

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

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 ++v3v

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

28 Evaluation Precision, Recall for tumour, non-tumour voxels nt = truth & pt = prediction ; Precision = Recall Correct ntPredicted pt

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 

30 Results – Model Comparison

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

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

33 Results (Special case) GBM_10: CDM beats UG by 8% and GW by 2% True positives False positives False negatives Resection & Recurrence

34 Results T-test: the probability that the means are not significantly different Paired data (same data sample; different models) CDM vs UG: p = CDM vs GW: p = (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!

35 Future work  More expressive features  Spectroscopy, DTI, genetic data  Larger dataset (treatment effect)  Brain atlas (“highways” vs. “barriers”)

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

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