Classification-based Glioma Diffusion Modeling Marianne Morris

2 Overview Introduction Motivation Assumptions Related Work Framework Contribution Results Conclusions

3 Introduction Task: Where to irradiate! What is a glioma? What is tumour diffusion modeling? Brain Biology MRI Radiotherapy

4 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

5 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!

6 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

7 What is a glioma? A primary brain tumour that originated from a cell of the nervous system

8 Diffusion Model Tumor

9 Diffusion Model Tumor Neighbours

10 Diffusion Model Tumor

11 Diffusion Model Tumor Neighbours

12 Diffusion Model Tumor

13 Diffusion Model Tumor

14 Diffusion Model Tumor Neighbours

15 Brain Biology

16 MRI Magnetic Resonance Imaging Magnet signal Echo signal detected Signal reconstructed into image Signal intensity (on image) determined by T1, T2 relaxation times Time line in minutes 00: T2 scanning 05: T1 scanning 10: contrast 15: T1-contrast scanning

17 MRI – image views AxialSagittalCoronal

18 MRI – image types T1 T1-contrast T2

19 Tissue differentiation on MRI scans TissueT1-weightedT2-weighted BoneDark AirDark FatBrightDark WaterDarkBright

20 MRI – image types T1 T1-contrast T2

21 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

22 Radiotherapy

23 Radiotherapy

24 Current Treatment Region Irradiate everything within 2 cm margin around tumour … includes Occult cells Normal cells

25 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!

26 Overview Introduction Related Work Framework Contribution Results Conclusions

27 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)

28 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 growth ProliferatingInactiveNecrotic Kansal et al., 2000

29 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 D w = 5 D g Swanson et al., 2000

30 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

31 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

32 Overview Introduction Related Work Framework Contribution Results Conclusions

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

34 Framework Noise Reduction Spatial Registration Intensity Standardization Tissue Segmentation Tumour Segmentation Preprocessing Feature Extraction Classification Tumour Diffusion Modeling Contribution

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

36 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

37 Inter-slice intensity variation Before inter-slice intensity variation reduction After inter-slice intensity variation reduction

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

39 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

40 Spatial registration Template example Colin Holmes template Average T2 template

41 Spatial registration Before registration After registration

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

43 Intensity Standardization Reduction of intensity variations across scans Using Weighted Linear Regression

44 Intensity Standardization After intensity standardization Before intensity standardization

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

46 Tissue segmentation White matter Grey matter Cerebrospinal fluid Using Statistical Parametric Mapping

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

48 Tumour segmentation Slice from patient’s scanSegmented tumour Tumour contour drawn by human experts

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

50 Features Patient features Tumour properties Voxel features Neighbourhood attributes A total of 76 features patient tumour voxel

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

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

53 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

54 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

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

56 Classification Task description Training and testing data Classifiers

57 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

58 Classification 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

59 Classification Classifiers Naïve Bayes Logistic Regression Linear-kernel SVM

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

61 Tumour growth modeling Uniform diffusion Growth based on tissue types Classification-based diffusion CDM

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

63 Tumour growth modeling – White vs. matter A 5:1 ratio for diffusion in white matter vs. grey matter (Sawnson et al., 2000) White matterGrey matter Original tumour Final tumour volume

64 Tumour growth modeling – CDM Based on… Features of patient, tumour and voxels around the tumour Labels predicted by classifier Number of tumour-voxel neighbours p i = 1 – (1 – q i ) k p i is probability that voxel i becomes tumour Learns q i by training q i = P Θ ( l (v i ) = tumour | e patient,e tumour,e i ) k is # tumour-voxel neighbours Uses probability threshold p i > 0.65

65 Tumour growth modeling - CDM Tumor Neighbours

66 Tumour growth modeling - CDM Tumor

67 Tumour growth modeling - CDM Tumor Neighbours

68 Tumour growth modeling - CDM Tumor

69 Tumour growth modeling - CDM Tumor

70 Overview Introduction Related Work Framework Contribution Results Conclusions

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

72 Results (evaluation measure) Precision |nt ∩ pt| |pt| Recall |nt ∩ pt| |nt| nt = truth & pt = prediction ; Precision = Recall

73 Results (Best case) CDM beats UG by 20% and GW by 12% True positives False positives False negatives Didn’t predict other wing of butterfly

74 Results (Average case) CDM beats UG by 6% and GW by 8% True positives False positives False negatives Didn’t predict growth in lower brain

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

76 Results

77 Results

78 Results T-test: the probability that the means are not significantly different Paired data (same data sample; different models) P(CDM vs. UG) = P(CDM vs. GW) = P(UG vs. GW) = X is the mean Var: the variance n: the number of samples

79 Overview Introduction Related Work Framework Contribution Results Conclusions

80 Conclusions Challenging problem Still feasible Future research directions More expressive features Spectroscopy, DTI, genetic data Larger dataset (treatment effect) Brain atlas (“highways” vs. “barriers”)

81 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

82 References Clatz et al., 2004, In Silico Tumour Growth: Application to Glioblastomas, MICCAI 2004, Kansal et al., 2000, Simulated brain tumour growth dynamics using a three-dimensional cellular automaton, J Theor Biol., 203: SPM (online) - Swanson et al., 2000, A quantitative model for differential motility of gliomas in grey and white matter, Cell Prolif., 33: 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