CLASSIFICATION OF BRAIN TUMORS BASED ON MAGNETIC RESONANCE SPECTROSCOPY
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1 CLASSIFICATION OF BRAIN TUMORS BASED ON MAGNETIC RESONANCE SPECTROSCOPY Jan Luts, Dirk Vandermeulen, Arend Heerschap, Uwe Himmelreich, Bernardo Celda, Johan A.K. Suykens, Sabine Van Huffel Department of Electrical Engineering (ESAT), Research Division SCD Katholieke Universiteit Leuven
2 Overview Brain tumors Diagnosis Invasive vs noninvasive Problem statement Aims MRS classification (etumour) HealthAgents DSS Nosologic imaging Multi-class classification Conclusions and future work 2
3 Overview Brain tumors Diagnosis Invasive vs noninvasive Problem statement Aims MRS classification (etumour) HealthAgents DSS Nosologic imaging Multi-class classification Conclusions and future work 3
4 Brain tumors Brain tumor = the growth of abnormal cells in the tissues of the brain (National Cancer Institute, Benign (noncancerous) or malignant (cancerous) Cancer cells result from uncontrolled cell growth and can grow into adjacent tissue Benign tumors can become large and press on healthy organs and tissue, which can potentially affect their functioning Benign tumors rarely invade other tissue Primary brain tumors start from the brain itself Secondary brain tumors (i.e. metastatic tumors) originate from other parts in the body 4
5 Brain tumors: glioblastoma multiforme (grade IV) 5
6 Brain tumors: epidemiology Central Brain Tumor Registry of the United States (CBTRUS): 98,990 new primary brain/cns tumors in United States from Overall incidence rate of per 100,000 Males and females accounted for 44 % and 56 % of the cases, respectively Metastatic brain tumors occur in 10 to 20 % of all cancer patients Common primary sites are lung cancer, breast cancer, melanoma Number of Americans with brain metastases > 150,000 per year 6
7 Overview Brain tumors Diagnosis Invasive vs noninvasive Problem statement Aims MRS classification (etumour) HealthAgents DSS Nosologic imaging Multi-class classification Conclusions and future work 7
8 Diagnosis Accurate diagnosis is crucial for treatment: Personal and family medical history Physical/neurological examination: general signs of health, including signs of disease Medical imaging: computed tomography (CT), MRI Histology: definite diagnosis by studying the microscopic anatomy of cells by the pathologist 8
9 Diagnosis: biopsy = invasive death rate = % 9
10 Diagnosis: histopathology Histopathology = gold standard Glioblastoma multiforme, grade IV Astrocytoma, grade II 10
11 Diagnosis: magnetic resonance imaging (MRI) Noninvasive Good soft tissue contrast No ionizing radiation as for CT Provides morphological information Type and grade of the tumor remain difficult to address 11
12 Diagnosis: magnetic resonance spectroscopy (MRS) 12
13 Diagnosis: MRS Noninvasive Provides chemical information (= metabolite) about tissue: molecular composition Concentration of metabolite = area under the peak Each tissue type has a typical MR spectrum One spectrum from one specific brain location No spatial distribution of the chemical information 13
14 Diagnosis: magnetic resonance spectroscopic imaging (MRSI) Obtain metabolite maps in a noninvasive way Provides information of the heterogeneity of the tumor Spatial resolution of MRSI is low compared to MRI 14
15 Overview Brain tumors Diagnosis Invasive vs noninvasive Problem statement Aims MRS classification (etumour) HealthAgents DSS Nosologic imaging Multi-class classification Conclusions and future work 15
16 Problem statement and aims Manual, individual, viewing and analysis of the multiple spectral patterns from MRS or MRSI: is time-consuming requires specific spectroscopic expertise MRS and MRSI are not practical in a clinical environment Automated systems to process and classify MRS/MRSI are needed These tools should be available in decision support systems (DSSs) 16
17 Problem statement and aims Training data set? New patient 17
18 Problem statement and aims Training data set Build classifier: New patient 18
19 Problem statement and aims Training data set Run classifier: New patient 19
20 Overview Brain tumors Diagnosis Invasive vs noninvasive Problem statement Aims MRS classification (etumour) HealthAgents DSS Nosologic imaging Multi-class classification Conclusions and future work 20
21 Magnetic resonance spectroscopy classification Training data set Build classifier: New patient 21
22 Magnetic resonance spectroscopy classification Training data set Build classifier: Test data set Performance on large amount of new independent data 22
23 Magnetic resonance spectroscopy classification etumour project ( ) 23
24 Magnetic resonance spectroscopy classification Training data: INTERPRET Test data: etumour Tumor types: Glioblastoma multiforme = GBM Metastasis = MET Meningioma = MEN Low-grade glioma = LGG Garcia-Gomez J.M., Luts J., et al. MAGMA, 22(1):5 18,
25 Magnetic resonance spectroscopy classification Performance on test data 25
26 Magnetic resonance spectroscopy classification Training error vs test error 26
27 Magnetic resonance spectroscopy classification Revise test data healthy tissue feature tumor feature 27
28 HealthAgents: distributed decision support system 28
29 HealthAgents: distributed decision support system 29
30 HealthAgents: distributed decision support system Developed classifiers Saez C., Garcia-Gomez J.M., Vicente J., Tortajada S., Luts J., et al. Accepted for publication in The Knowledge Engineering Review,
31 HealthAgents: distributed decision support system 31
32 Overview Brain tumors Diagnosis Invasive vs noninvasive Problem statement Aims MRS classification (etumour) HealthAgents DSS Nosologic imaging Multi-class classification Conclusions and future work 32
33 Nosologic imaging Definition: A nosologic image summarizes the spectroscopic information in one image where each pixel is colored according to the histopathological class it belongs to (F.S. De Edelenyi et al., Nat Med 6: , 2000) Advantages: easy to interpret for clinicians visualizes the heterogeneity of the tumor provides more information than MRI segmentation Luts J., Laudadio T., et al. NMR Biomed,22(4): ,
34 Nosologic imaging Data: 1.5 T Siemens Vision Scanner: 25 patients, 4 volunteers (University Medical Center Nijmegen, Radboud University Nijmegen) MRI: T1, T2, Pd, T1 post Gd MR images MRSI: TE = 20 ms, TR = 2000/2500 ms Tissue classes: normal brain tissue (volunteers/patients), CSF, grade II glioma, grade III glioma, glioblastoma, meningioma Segmentation-classification approach: Brain tumor segmentation based on outlier detection (M. Prastawa et al. Med Image Anal 8: , 2004) Digital brain atlas Iterative algorithm: 1. Detection of abnormality 2. Detection of edema using T2 3. Reclassification with spatial and geometric constraints In new approach: only step 1 and step 3, no edema detection using MRI Subject-specific abnormal tissue prior from MRSI 34
35 Nosologic imaging: abnormal tissue prior Include prior knowledge from MRSI into the segmentation framework Classify with LDA normal tissue versus tumor tissue based on MRSI Obtain probabilistic output from LDA Extend the digital brain atlas with tumor tissue probabilities Good prototypes and priors: improved sampling in the segmentation method Less normal tissue samples are sampled from abnormal tissue regions 35
36 Nosologic imaging: abnormal tissue prior Include prior knowledge from MRSI into the segmentation framework Classify with LDA normal tissue versus tumor tissue based on MRSI Obtain probabilistic output from LDA Extend the digital brain atlas with tumor tissue probabilities: Good prototypes and priors: improved sampling in the segmentation method Less normal tissue samples are sampled from abnormal tissue regions 36
37 Nosologic imaging: multi-class classification Classify the segmented tumor region using a (semi-) supervised classifier MRSI and MRI information can now be combined Pixels can be classified independently or use canonical correlation analysis (CCA) for including spatial information in this step Probabilities are calculated with a multiclass Bayesian LS- SVM classifier or with multiclass kernel logistic regression Any type of classifier can be included in this step 37
38 Multi-class Bayesian LS-SVM classification (LS-)SVMs: binary classifiers and black/white decisions Multiclass classification: Coding schemes (1 vs all, MOC, ECOC, 1 vs 1...) 1 vs 1: Faster training and tuning More balanced groups Binary classifier = powerful Degree of uncertainty about prediction needed Pairwise coupling methods needed to obtain global class probabilities P(y=i x): Iterative procedure by Hastie and Tibshirani Direct approach by Price et al. Direct approaches by Wu et al. Luts J., Heerschap A., et al. AIM 40(2):87 102,
39 Nosologic imaging: oligodendroglioma grade II/III 41 year old female, survival unknown yellow = grade II glioma, orange = grade III glioma, green = CSF, light blue = white matter, dark blue = gray matter 39
40 Nosologic imaging: uncertainties for abnormal region 40
41 Nosologic image: glioblastoma multiforme (grade IV) 54 year old male, 1 year of survival 41
42 Conclusions Tools for processing and classifying MRS, MRSI and MRI to assist clinicians in the diagnosis of brain tumors: Machine learning approach results in accuracies of 90 % on an independent test data set Implementation in distributed decision support system Nosologic imaging by combining MRI and MRSI 42
43 Future work Automated quality control of MRSI Increase spatial resolution of MRSI Improve quantification approaches by spatial information Additional clinical features (e.g. tumor location) Integrate multi-modal data (e.g. DTI, diffusion-weighted MRI and perfusion-weighted MRI) Extensive classifier validation Therapy follow-up 43
44 The end Thank you! 44
45 References De Neuter B., Luts J., Vanhamme L., Lemmerling P., and Van Huffel S. Java-based framework for processing and displaying short-echo-time magnetic resonance spectroscopy signals. Computer Methods and Programs in Biomedicine, 85(2): , Luts J., Heerschap A., Suykens J.A.K., and Van Huffel S. A combined MRI and MRSI based multiclass system for brain tumour recognition using LSSVMs with class probabilities and feature selection. Artificial Intelligence in Medicine, 40(2):87 102, Luts J., Poullet J.-B., Garcia-Gomez J.M., Heerschap A., Robles M., Suykens J.A.K., and Van Huffel S. Effect of feature extraction for brain tumor classification based on short echo time 1H MR spectra. Magnetic Resonance in Medicine 60(2): , Garcia-Gomez J.M., Tortajada S., Vidal C., Julia-Sape M., Luts J., Van Huffel S., Arus C., and Robles M. The effect of combining two echo times in automatic brain tumor classification by MRS. NMR in Biomedicine, 21(10): , Garcia-Gomez J.M., Luts J., Julia-Sape M., Krooshof P., Tortajada S., Vicente J., Melssen W., Fuster-Garcia E., Olier I., Postma G., Monleon D., Moreno-Torres A., Pujol J., Candiota A.-P., Martinez-Bisbal M.C., Suykens J.A.K., Buydens L.M.C., Celda B., Van Huffel S., Arus C., and Robles M. Multiproject-multicenter evaluation of automatic brain tumor classification by magnetic resonance spectroscopy. Magnetic Resonance Materials in Physics, Biology and Medicine, 22(1):5 18, Luts J., Laudadio T., Idema A.J., Simonetti A.W., Heerschap A., Vandermeulen D., Suykens J.A.K., and Van Huffel S. Nosologic imaging of the brain: segmentation and classification using MRI and MRSI. NMR in Biomedicine, 22(4): , Saez C., Garcia-Gomez J.M., Vicente J., Tortajada S., Luts J., Dupplaw D., Van Huffel S., and Robles M. A generic and extensible framework for providing automatic classification functionality applied for brain tumour diagnosis in HealthAgents. Accepted for publication in The Knowledge Engineering Review,
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