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**MAGIC5 meeting - Genova 2011/5/4-6**

A CAD system for cerebral glioma and therapy follow-up in DTI and FLAIR: status report Giorgio De Nunzio1, Marina Donativi1, Gabriella Pastore2, Matteo Rucco3 , Antonella Castellano4, Andrea Falini4 Dept of Materials Science, Univ. of Salento, and INFN (National Institute of Nuclear Physics) (Lecce, Italy) PO 'Vito Fazzi' - UOC Fisica Sanitaria, and Dept of Materials Science, University of Salento (Lecce, Italy) School of Science and Technologies, University of Camerino (Italy) Neuroradiology Unit and CERMAC, San Raffaele Scientific Institute (Milano, Italy)

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**Diffusion Tensor Imaging (DTI); Isotropic and Anisotropic maps**

Glioma Common primary brain tumors. Typical infiltrative growth pattern glioma cells preferentially infiltrate along white matter fibers. Conventional MRI cannot accurately localize microscopic glioma infiltrations, therefore it does not always permit precise delineation of tumor margins or tumor differentiation from edema and/or treatment effects. Diffusion Tensor Imaging (DTI); Isotropic and Anisotropic maps Diffusion of water molecules

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**Materials and methods Glioma in MR-DTI**

CAD System for glioma segmentation (texture analysis) Glioma structure during follow-up

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**Training step CAD Once upon a time… svoi ANN (learn) csvoi rsvoi IMAGE**

ACQUISITION ROI CREATION SLIDING WINDOW MAZDA SCATTER PLOT PCA FEATURE SELECTION CAD IMAGE ACQUISITION GUI SLIDING WINDOW MAZDA PCA ANN (classification) ROI CREATION

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**CAD Training step NOW!!! ALL IN MATLAB svoi ANN (learn) csvoi rsvoi**

IMAGE ACQUISITION ROI CREATION SLIDING WINDOW 69 FEATURES PCA LDA APPLY FILTER FEATURE SELECTION ALL IN MATLAB CAD IMAGE ACQUISITION GUI ENTIRE BRAIN SLIDING WINDOW 69 FEATURE PCA LDA ANN (classification) FEATURE SELECTION ROI CREATION

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**Fisher’s score vs feature**

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Work in progress… PCA or LDA?

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**Why Principal Component Analysis?**

Maximize variance by axis transformation

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**Dimensionality Reduction**

Can ignore the components of lower significance. You do lose some information, but if the eigenvalues are small, you don’t lose much n dimensions in original data calculate n eigenvectors and eigenvalues choose only the first p eigenvectors, based on their eigenvalues final data set has only p dimensions Variance Dimensionality 9

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Limitations of PCA Are the maximum-variance variables the relevant features for discrimination preservation? 10

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**Linear Discriminant Analysis**

What is the goal of LDA? Perform dimensionality reduction “while preserving as much of the class discriminatory information as possible”. Seeks to find directions along which the classes are best separated. Takes into consideration the within-class scatter but also the between-class scatter.

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**Feature dimensionality reduction methods**

PCA given an s-dimensional vector representation (features) of each sample in a training set, Principal Component Analysis (PCA) tries to find a s-dimensional space whose basis vectors correspond to the maximum-variance directions in the original feature space. The dimensionality of this new space is then normally decreased to a lower one (t << s) by neglecting directions with low eigenvalues. If x is the feature array, it is possible to diagonalise the covariance matrix: and obtain the eigenvalues of the linear transformation Matrix T, that is Starting from that, it is possible to calculate the PC’s LDA Linear Discriminant Analysis finds the vectors in the space that best discriminate among classes. For two classes, the solution proposed by Fisher is to maximize a function that represents the difference between the means, normalized by a measure of the within-class scatter Between-class scatter matrix Within-class scatter matrix

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PCA LDA

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PCA LDA

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**… Classifier:Artificial Neural Networks (ANN)**

FLAIR 5 patients for training and 4 for test Back-propagation feed-forward ANN: 1 hidden layer, with 3 neurons 1 output neuron LDA PCA AUC=0.97 AUC=0.94

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**… some results of map creation and segmentation**

Probability maps in p, q or FLAIR images: the dots mark the positions of the sliding window (svoi centers). Color scale: darker colors for low probability values, lighter colors for high values. Red line: shows the segmentation produced by the CAD system (“arbitrary” threshold)

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**… some results of map segmentation**

P MAP 6 patients for training and 6 for test P MAP - PCA P MAP – MED ROI P MAP - LDA AUC=0.88 AUC=0.95

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**… some results of map segmentation**

Q MAP 6 patients for training and 6 for test Q MAP - PCA Q MAP – MED ROI Q MAP - LDA AUC=0.77 AUC=0.90

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**… some results of map segmentation**

FLAIR 5 patients for training and 4 for test Fluid Attenuated Inversion Recovery FLAIR - PCA FLAIR – MED ROI FLAIR - LDA AUC=0.94 AUC=0.97

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**Prospects Feature reduction or selection? PCA Fisher score LDA ICA**

Both selection and reduction?? Fisher score (with a threshold according to the AUC plateau [as a function of the FS]) Jaccard Coefficient to set the ‘best’ ANN threshold for segmentation FLAIR segmentation is promising!

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**Changes in glioma water diffusion values after chemotherapy: work in progress!!**

LGG (low-grade glioma) cells grow and diffuse typically along the white matter tracts Diffusion Tensor Imaging in glial tumors allows to depict white matter alterations not visible by conventional MRI Price et al., Clin Radiol 2003; Wang et al., AJNR 2009 Starting from Diffusion Tensor it is possible to obtain two maps: isotropic (p) and anisotropic (q) Pena et al., BJR 2006; Price et al., Eur Radiol 2004 Isotropic (p) and Anisotropic (q) Maps allow a better characterization of the diffusion features of tumoral and peritumoral areas Price et al., AJNR 2006; Price et al., Eur Radiol 2007; Wang et al., AJNR 2009

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**Changes in glioma water diffusion values after chemotherapy: work in progress!!**

Changes in tumor water diffusion occur after successful treatment and can be attributed to changes in cell density. p q decrease of tissue infiltration p q increase of tissue infiltration Moffat et al., PNAS 2005 Hamstra et al., JCO 2008 Galban et al., TransOnc 2009 Aim of Study: to investigate whether changes in the Brownian motion of water within tumor tissue as quantified by using diffusion MRI could be used in the follow up of treated gliomas.

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**Patients & methods 9 patients with low grade glioma similar Histology**

ID Age Site Previous surgery Histology 1p-19q MGMT Seizures TMZ (dose-dense) 1 34 Frontal L Oct-05 O II codel N/A No 6 cycles 2 28 Jul-07 OA II met 3 33 Fronto-tempo-insular R Jul-04 4 25 Fronto-tempo-insular L Sep-07 no codel Yes 5 36 Apr-07 A II 6 56 7 45 Nov-04 8 37 Sep-09 unmet 9 32 Fronto-temporal R Mar-08 9 patients with low grade glioma similar Histology same duration of treatment similar Clinical History same scheme of neuroradiological follow-up

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Patients & methods 3T Scanner Intera Philips Medical System (gradients 80 mT/m) MR morphological study: axial T2 TSE (TR/TE 3000/85, NSA 2), axial FLAIR (TR/TE/TI /120/2800) axial FFE MP-RAGE (TR/TE 8/3.9) voxel size and positioning as for DTI, acquired following i.v. injection of paramagnetic contrast DTI scans: axial Single-Shot Spin Echo EPI (TR/TE 8986/80, b-value 1000 mm2/sec, 32 directions, SENSE 2.5, FOV 240, mm, repeated twice) Diffusion maps: diffusion-tensor elements calculated and diagonalized at each voxel, obtaining three eigenvalues, fractional anisotropy (FA), and trace (Tr) maps; from the elaboration of these datasets in MATLAB pure isotropic (p) and pure anisotropic (q) diffusion maps are obtained Segmentation of tumor areas in the various maps of tensor decomposition metrics (p, q) obtained from first MR examination and after five cycles IMAGE ACQUISITION q and p MAP IMAGE COREGISTRATION TUMOR AREA SEGMENTATION SEARCH OF THRESHOLD PIXEL COLOR MAP

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**Patients & methods y>x y<x x y x y RED BLUE p or q**

p value before chemoterapy p value after chemoterapy x y p value before chemoterapy p value after chemoterapy x y y>x y<x Let’s see to an example with respect to isotropic map: if we plot on the x axis the value of isotropic diffusion at baseline of a point inside the tumor and on y axis the value of the same point after therapy, when the value on y is greater than the value on x the point will be represented in red both on the graph and on the map, so red areas inside the tumor are characterized by an increase of isotropic diffusion and tissue infiltration; when the value on y is smaller than the value on x, the point will be represented in blue, and this indicate a reduction of isotropic diffusion and tissue infiltration. Points on green bisecting line indicate no change. RED BLUE p or q p or q 25

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Results ID Seizure response Radiological response (FLAIR) DTI DTI response % blue/red voxels on isotropy map Second surgery extent of resection (%) Peritumoral IDH1 1 N/A SD + of isotropy 7.3 blue; 6.1 red 79,72 subtotal 2 mR -36.4% 2.7 blue; 2.2 red 83,48 subtotal 3 mR -26% - of isotropy 26 blue; 53.5 red 82,67 subtotal 4 Stable SD -11.6% of isotropy 27 blue;13 red 82,16 subtotal 5 PD of isotropy 1 blue; 30 red 97,5 subtotal 6 >50% SD -9,7% 5.8 blue; 3.7 red 97,7 subtotal 7 mR -35% 4.4 blue; 3.1 red 100 total Neg 8 of isotropy 7 blue; 4.8 red 9 67 blue; 17 red 79 subtotal Almost all patients have seizures (convulsioni) as a unique symptom, difficult to control with antiepileptic drugs; most patients with seizures have a clinical benefit even in the absence of a radiological change on MRI. 26

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**stabilità radiologica di malattia miglioramento clinico**

M.G., astrocitoma WHO II: fDM su mappa p (isotropia) dopo 6 cicli TMZ voxel blu: 7% voxel rossi: 4.8% I esame II esame riduzione di oltre 50% frequenza delle crisi epilettiche a fronte di una stabilità dimensionale alla FLAIR stabilità radiologica di malattia miglioramento clinico 27

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**progressione di malattia! progressione radiologica di malattia?**

R.G., oligodendroglioma WHO II: fDM su mappa p (isotropia) dopo 6 cicli TMZ voxel blu: 1% voxel rossi: 30% I esame II esame The last patient I will show you is a low-grade astrocytoma located in left frontal lobe, who had a partial resection at 1st surgery; this is an example of failure of chemotherapy, as the patients progressed clinically and diffusion maps showed a marked increase of isotropy after temozolomide. All the points are located above the bisection line. progressione di malattia! progressione radiologica di malattia? stabilità clinica 28

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**stabilità radiologica di malattia miglioramento clinico**

B.L., oligodendroglioma WHO II: fDM su mappa p (isotropia) dopo 6 cicli TMZ voxel blu: 7.3% voxel rossi: 6.1% I esame II esame Now I will show you some illustrative cases: this is a left posterior frontal and cingulate oligodendroglioma, you can see the diffusion isotropy maps and the corresponding FLAIR images. After five cycles of temozolomide, there is a reduction of isotropic diffusion in an area lining the anterior and lateral border of the tumor. The graph on the left show that inside the tumor there are some points in which isotropy is increased, and some others in which isotropy decreased; however, if we look at the graph on the right, that is the same of the left one in which point are classified according to the quantity or density of points in an area of the graph, we see that most of the points in which isotropy is changed are clustered under the bisection line, so most of points inside the tumor show a reduction of isotropic diffusion and tissue infiltration after therapy. stabilità radiologica di malattia miglioramento clinico 29

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**B.L., ODG WHO II: confronto con neurofisiologia intraoperatoria**

sonda monopolare per la stimolazione intraoperatoria infiltrazione fibre CST infiltrazione fibre CST sonda bipolare per la stimolazione intraoperatoria Intraoperative subcortical stimulation of anterior and lateral areas evokes activation of leg motor fibers, and this activation was achieved by the use of bipolar probe and a lower current for stimulation, that indicates a reduced infiltration of fibers in the blue area depicted on the map. In the same patients, subcortical stimulation of a posterior and mesial frontal area depicted in red on the map required the use of monopolar probe for stimulation with a higher current and evokes activation of leg motor fibers. This region was characterized by an increase of isotropy over time and so a high infiltration of fibers. 30

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Some conclusions: Tissue analysis with DTI could help the physicians in evaluating the chemotherapy responses. The p or q value variation could suggest a tumor progression or regression also for cases in which the tumor volume does not change. These preliminary results are in accordance with neurophysiological results and with intraoperative bioptic samples. 31

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**Prospects: we are working to…**

unify in the scatter plot both the p and the q value variations study also the local maximum variation

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Conferences: A. CASTELLANO, M. DONATIVI, L. BELLO, G. DE NUNZIO, M. RIVA, G. PASTORE, G. CASACELI, R. RUDÀ, R. SOFFIETTI, AND A. FALINI(2011). Evaluation of changes in gliomas structural features after chemotherapy using DTI-based Functional Diffusion Maps (fDMs): a preliminary study with intraoperative correlation. In:2011 Joint Annual Meeting ISMRM-ESMRMB. Montréal, May 7-13, 2011 G. DE NUNZIO, M. DONATIVI, G. PASTORE, A. CASTELLANO, A. FALINI, L. BELLO, R. SOFFIETTI, (2010). A CAD system for cerebral glioma and therapy follow-up in Diffusion-Tensor Images. In II Workshop Plasmi Sorgenti Biofisica e Applicazioni, Lecce (Italy) 26 Ottobre 2010 A. CASTELLANO, L. BELLO, E. FAVA, G. CASACELI, M. RIVA, M. DONATIVI, G. PASTORE, G. DE NUNZIO, R. RUDA', L. BERTERO, R. SOFFIETTI, A. FALINI (2010) DTI-MR 3D Texture Analysis per la valutazione delle modificazioni delle caratteristiche strutturali dei gliomi cerebrali dopo trattamento con Temodal: studio preliminare. In XV Congresso Nazionale della Associazione Italiana di Neuro-Oncologia (AINO), Fiuggi (FR, Italy) 3-6 Ottobre 2010 G. DE NUNZIO, G. PASTORE, M. DONATIVI, A. FALINI, A. CASTELLANO, L . BELLO, R. SOFFIETTI (2010). DT-MR images: A CAD System for Cerebral Glioma and Therapy Follow-up. In IVth European Conference of Medical Physics - Advances in High Field Magnetic Resonance Imaging, Udine (Italy) September (2010) G. DE NUNZIO, M. DONATIVI, G. PASTORE, A. CASTELLANO, G. SCOTTI, L. BELLO, A. FALINI (2010). Automatic Segmentation and Therapy Follow-up of Cerebral Glioma in Diffusion-Tensor Images. In 2010 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA 2010). Taranto (Italy) September 6-8, 2010 CASTELLANO, L. BELLO, E. FAVA, M. RIVA, G. CASACELI, G. DE NUNZIO, M. DONATIVI, G. PASTORE, R. RUDA', R. SOFFIETTI, A. FALINI (2010) Changes in gliomas structural features after Temodal treatment evaluated by DTI-MR texture analysis: a preliminary study. In 9th International Meeting UPDATES IN NEURO-ONCOLOGY, Brain Tumor Symposium, Cortona (AR, Italy), July 2-4, 2010 G. DE NUNZIO, G. PASTORE, M. DONATIVI, A. CASTELLANO, A. FALINI (2010). A CAD system for cerebral glioma based on texture features in DT-MR images. In International Conference on Imaging Techniques in Subatomic Physics, Astrophysics, Medicine and Biology (Imaging 2010), Stockholm (Sweden) 8-11 June 2010 G. DE NUNZIO, A. CASTELLANO, G. PASTORE, M. DONATIVI, G. SCOTTI, L. BELLO, A. FALINI (2010). Semi-automated evaluation of structural characteristics and extension of cerebral gliomas using DTI-MR 3D Texture Analysis. In: 2010 Joint Annual Meeting ISMRM-ESMRMB. Stockholm, May 1-7, 2010 G. DE NUNZIO, A. CASTELLANO, M. DONATIVI, G. PASTORE, A. FALINI. (2010). A semi-automated DTI-based approach to evaluate structural characteristics and extension of cerebral gliomas (poster No C-2926). In: European Congress of Radiology (ECR2010). Vienna, March 4-8, 2010 G. DE NUNZIO, G. PASTORE, A. CASTELLANO, M. DONATIVI, A. FALINI (2010). Automatic Segmentation of Cerebral Glioma in DT-MR Images by 3D Texture Analysis. In: Risonanza magnetica in medicina: dalla ricerca tecnologica avanzata alla pratica clinica (Italian Chapter of the International Society of Magnetic Resonance in Medicine). Milano, 4-5 febbraio 2010

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**ICAdecompone il dataset nelle sue sottoparti indipendenti**

Metodi per la riduzione dello spazio delle feature ai fini della classificazione ICAdecompone il dataset nelle sue sottoparti indipendenti Dato il vettore x, mistura dei segnali originali s tramite una matrice di mixing A scopo della ICA è identificare una matrice di de-mixing W tale che le componenti del vettore in uscita siano quanto più statisticamente indipendenti

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**ICA Per stimare una delle IC**

La combinazione lineare delle sorgenti indipendenti è più “gaussiana” delle componenti originarie e lo diventa “al minimo” quando z ha solo l’i-imo elemento non nullo: questo porta a scegliere W in modo da massimizzare la non-gaussianità di WTx

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Features

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**Linear Discriminant Analysis**

Within-class scatter matrix Between-class scatter matrix projection matrix LDA computes a transformation that maximizes the between-class scatter while minimizing the within-class scatter: products of eigenvalues ! : scatter matrices of the y data after projection

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**Linear Discriminant Analysis**

Does Sw-1 always exist? If Sw is non-singular, we can obtain a conventional eigenvalue problem by writing: In practice, Sw is often singular since the data are image vectors with large dimensionality while the size of the data set is much smaller (M << N ) Since Sb has at most rank C-1, the max number of eigenvectors with non-zero eigenvalues is C-1 (i.e., max dimensionality of sub-space is C-1)

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**Features in MaZda VS Features in Matlab: an example**

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