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Classification-Based Diffusion Model for Glioma Growth Prediction Using MRI Data

This study presents a classification-based diffusion model (CDM) that predicts glioma tumor growth using MRI data from patients. The model learns key features from patient data and classifies voxel changes, determining which new voxels to categorize as tumor based on tissue types. It utilizes an incremental growth approach, assessing features from the tumor, surrounding tissue, and neighboring voxels. With a training set of over half a million voxels from 17 patients, the CDM demonstrates significant improvements in prediction accuracy over existing models, aiding in treatment planning.

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Classification-Based Diffusion Model for Glioma Growth Prediction Using MRI Data

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  1. A Classification-based Glioma Diffusion Model Using MRI Data tumour voxel patient Original tumour Final tumour volume Tumor Uniform Growth GW Model Time 1 Time 2 Results True positives,False positives,False negatives Marianne Morris, Russ Greiner, Jörg Sander, Albert Murtha, Mark Schmidt Incremental Growth • Which new voxels to add? • UG: Uniform Growth • Grow equally in all directions • GW: Growth based on tissue types • Diffuse faster in white matter than grey matter • CDM: Classification-based diffusion • Learn key features from patient data and build a classifier to iteratively label voxels bordering the volume labelled “tumour” Problem Predict Tumour Growth Initial Tumour 6 Months Later • Why? • Improve Treatment Planning • CDM Features • Patient features • Tumour properties • Voxel features • Features of • neighbouring voxels • 75 in total • CDM Classifier • Voxel v becomes tumour given • qv = PΘ (class (v) = tumour | ep,et,ev) • Features of the patient ep • the tumour et • the voxel and its neighbours ev • Decision Threshold • If k tumour-voxel neighbours, then v becomes tumour with probability • pv = 1 – (1 – qv)k • Decision • Declare v is tumour if • pv0.65 • Stopping Criteria • Iterate around active tumour border until • tumour grown from initial volume at 1st scan equals size of tumour volume at 2nd scan • Precision = Recall • predicted volumetruth volume • Experiments • Training data • Volume-pairs for 17 patients • Total of ½ million voxels • We evaluate voxels encountered in diffusion process • Cross-validation (17 patients) CDM Performance Model Comparison • Conclusion • Learned model CDM performs significantly better than other existing models! • Can be improved with additional data • DTI, Spectroscopy, genetic data, brain atlas • For More Project Information, see • www.cs.ualberta.ca/~btgp Additional tumour growth Original tumour

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