Algorithms 2009, 2, ; doi: /a OPEN ACCESS

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1 Algorthms 009,, ; do:0.3390/a04350 OPEN ACCESS algorthms ISSN Artcle CADrx for GBM Bran Tumors: Predctng Treatment Response from Changes n Dffuson-Weghted MRI Jng Huo, *, Kazunor Okada, Hyun J. Km, Whtney B. Pope, Jonathan G. Goldn, Jeffrey R. Alger and Matthew S. Brown UCLA Department of Radologcal Scences, 94 Westwood Blvd., Sute 650, Los Angeles, CA 9004, USA; E-Mal: mbrown@mednet.ucla.edu (M.S.B.) San Francsco State Unversty Computer Scence Department, Thornton Hall 9, 600 Holloway Avenue, San Francsco, CA , USA; E-Mal: kazokada@sfsu.edu (K.O.) * Author to whom correspondence should be addressed; E-Mal: jhuo@mednet.ucla.edu. Receved: August 009; n revsed form: September 009 / Accepted: 3 November 009 / Publshed: 6 November 009 Abstract: The goal of ths study was to develop a computer-aded therapeutc response (CADrx) system for early predcton of drug treatment response for globlastoma multforme (GBM) bran tumors wth dffuson weghted (DW) MR mages. In conventonal Macdonald assessment, tumor response s assessed nne weeks or more post-treatment. However, we wll nvestgate the ablty of DW-MRI to assess response earler, at fve weeks post treatment. The apparent dffuson coeffcent (ADC) map, calculated from DW mages, has been shown to reveal changes n the tumor s mcroenvronment precedng morphologc tumor changes. ADC values n treated bran tumors could theoretcally both ncrease due to the cell kll (and thus reduced cell densty) and decrease due to nhbton of edema. In ths study, we nvestgated the effectveness of features that quantfy changes from pre- and post-treatment tumor ADC hstograms to detect treatment response. There are three parts to ths study: frst, tumor regons were segmented on Tw contrast enhanced mages by Otsu s thresholdng method, and mapped from Tw mages onto ADC mages by a 3D regon of nterest (ROI) mappng tool usng DICOM header nformaton; second, ADC hstograms of the tumor regon were extracted from both pre- and fve weeks post-treatment scans, and ftted by a two-component Gaussan mxture model (GMM). The GMM features as well as standard hstogram-based features were extracted. Fnally, supervsed machne learnng technques were appled for classfcaton of responders or non-responders. The approach was evaluated wth a dataset of 85 patents wth GBM under chemotherapy, n whch 39 responded and 46 dd not,

2 Algorthms 009, 35 based on tumor volume reducton. We compared adaboost, random forest and support vector machne classfcaton algorthms, usng ten-fold cross valdaton, resultng n the best accuracy of 69.4% and the correspondng area under the curve (Az) of Keywords: CADx; DW-MRI; bomarker; adaboost; random forest; support vector machne. Introducton Computer aded dagnoss (CADx) can be defned as a dagnoss that s made by a radologst who uses the output from a computerzed analyss of medcal mages as a second opnon n both detectng lesons and makng dagnostc decsons []. One am of the typcal CADx system s to extract and analyze the characterstcs of bengn and malgnant lesons n an objectve manner to ad the radologst. Here, the dagnostc decson relates to treatment response and early classfcaton of drug responders versus non-responders, and we name our proposed system as computer-aded therapeutc response (CADrx) system. Globlastoma multforme (GBM) s the most aggressve and lethal prmary bran tumor n human. Ant-angogeness drugs are ncreasngly beng explored n clncal trals as therapeutc optons. In a phase II n vvo clncal tral, the conventonal way to assess treatment response s the tumor sze change after chemotherapy or radotherapy based on Macdonald crtera and evaluated on T-weghted contrast enhanced (TwCE) MR mages. However, effcacy can only be evaluated at least 8 0 weeks after treatment. Dffuson weghted magnetc resonance magng (DW-MRI) has the potental to work as a surrogate bomarker to reveal changes n the tumor mcroenvronment that precede morphologc tumor changes []. DW-MRI depends on the mcroscopc moblty of water. Ths moblty, classcally called Brownan moton, s due to thermal agtaton and s hghly nfluenced by the cellular envronment of water. Because water dffuson s strongly affected by molecular vscosty and membrane permeablty between ntra- and extracellular compartments, DW-MRI can be used to characterze hghly cellular regons of tumors versus acellular regons. Treatment response detecton can be manfested as a change n tumor cellularty, whch may precede tumor sze changes. Thus, fndngs on DW-MRI could be an early sgn of bologc changes. [3] The purpose of ths study s to use apparent dffuson coeffcent (ADC), derved from DW-MR mages, for early predcton of the tumor volume change on a later scan. There are two man parts to ths computer-aded treatment response evaluaton system. Frst, a sem-automated segmentaton algorthm s appled to segment the GBM bran tumors on TwCE mages. Then, the tumor ROI s mapped onto derved ADC maps and the hstogram of tumor ADC values wll be extracted for automatc treatment response predcton. Computer-aded detecton and segmentaton of GBM bran tumors s a challengng problem and n Table we present a concse revew of the pror art n automatc tumor segmentaton. Fuzzy clusterng and knowledge-based analyss are popular methods explored by the early poneers [4-6]. Voxel-based classfcaton method usng statstcal pattern classfcaton technques are explored by others [7-5].

3 Algorthms 009, 35 Most of the studes above use multple MRI sequences (Tw, Tw, proton densty weghted, and Flar) for the automatc tumor and edema detecton and segmentaton. Lu et al. [6] developed an nteractve system adaptng the fuzzy connectedness usng multple MRI sequences. Dube et al. [7,8] used texture features and segmentaton by the weghted aggregaton (SWA) method for the GBM tumor segmentaton on TwCE mages whch s smlar to part of our study. In our study, we developed sem-automated method to segment tumors on TwCE mages; n addton, we mapped the tumor contours onto ADC maps. Table. Summary of related methods n bran tumor segmentaton. The type abbrevatons are NC: Nasopharyngeal carcnoma; MNG: Menngomas; MG - malgnant glomas; MS multple scleross. Authors Technque Type Image sequences # of tumors Lu et al. [6] Sem-automated fuzzy GBM Tw, Tw+c, Flar 5 clusterng Phlps et al. [4] Fuzzy clusterng GBM PD,Tw, Tw+c Clark et al. [5] Fuzzy clusterng and GBM PD,Tw, Tw+c 7 knowledge-based analyss Fletcher-Heath Fuzzy clusterng and Bran PD,Tw, Tw wth 4 et al. [6] knowledge-based analyss tumor no contrast Prastawa et al. [7] Learn dstrbuton of normal Bran Tw, Tw (wth or 3 tssues/outler detecton as tumors tumors wthout contrast) Kaus et al. [8] Adaptve template -moderate LGG/M Tw+c, sagttal 0 technque wth atlas pror G vew Lee et al. [] Condtonal random feld and Bran Tw, Tw+c, Tw 7 support vector machne tumors Ho et al. [9] 3D level set GBM Tw+c, Tw, Tw 3 Vntsk et al. [0] k-nearest neghbor MS and MG PD, Tw, Tw, magnetzaton transfer 9 Zhu & Yan et al. [] Hopfeld neural network Bran tumors NA Zhang et al. [3] Support vector machne NC Tw, Tw+c 9 Corso et al. [5] SWA-segmentaton by GBM Tw, Tw, Tw+c, 0 weghted aggregaton. Flar Dube et al. [7] SWA wth texture features GBM Tw+c NA Ne et al. [4] Spatal accuracy-weghted hdden Markov feld and EM to solve the problem of hgh and low resoluton problem Glomas Hgh:Tw, Tw+c Low:Tw, Flar 5 Computer-aded dagnoss (CADx) n GBM bran tumor s an actve research area, and many promsng MR methods have been developed for detectng and characterzng cancer, ts treatments and adverse effects, e.g. T-weghted MR, T-weghted MR, MR spectroscopy, perfuson-weghted MR, and dffuson-weghted MR. In our study, we focused on T-weghted and DW-MRI. Tumor sze

4 Algorthms 009, 353 change on Tw mages s the only magng bomarker that s accepted by the FDA as a surrogate endpont of clncal outcome after chemotherapy and radotherapy for phase III trals [9]. Dffuson MRI has been explored as early detecton of human GBM bran tumor treatment response early therapeutc responses before the tumor sze changes. Table presents a revew of the recent studes that used DWI for GBM early predcton of treatment response. Ross et al reported ADC value ncrease sgnfcantly n effectve therapeutc nterventon n pre-clncal studes and presented two patents to support ths hypothess n a prelmnary clncal study [,0]. Mardor et al. [] appled both low and hgh b-value and used mean ADC and dffuson ndex for treatment response evaluaton. Moffat et al calculated voxel-by-voxel tumor ADC value changes over tme and dsplayed t as a functonal dffuson map for correlaton wth clncal response [,3]. They reported that the number of voxels wth ncreased ADC s related to treatment effcacy. Our prevous work [4] showed promsng results for usng ADC hstogram analyss, and we explored a more sophstcated classfer and desgned experments to show the advantages of the two-component hstogram modelng. Table. Summary of related methods n GBM tumor treatment response usng DWI. Authors # Of Patents Chenevert et al. [0] Ross et al. [] Mardor et al. [] 0 Moffat et al. [] 0 Hamstra et al. [3] 34 Machne learnng and statstcal pattern recognton have great contrbutons to the bomedcal communty because they can mprove the senstvty and/or specfcty of detecton and dagnoss of dsease, whle at the same tme ncreasng objectvty of the decson-makng process [6]. The need for machne learnng s perhaps greater than ever gven the dramatc ncrease n medcal data beng collected, new detecton, and dagnostc modaltes beng developed as well as the complexty of the data types and mportance of multmodal analyss. In all of these cases, machne learnng can provde new tools for nterpretng the hgh-dmensonal and complex datasets wth whch the clncan s confronted [6]. In our study, we explored three dfferent classfcaton methods: AdaBoost, random forest, and support vector machne. The AdaBoost algorthm, ntroduced by Freund and Schapre [7], s an teratve algorthm that can boost weak classfers nto a strong classfer and mprove the fnal accuracy. In each teraton, a feature s workng as a weak classfer and the best feature s selected to mnmze the average tranng error. Afterwards, the weghts on tranng samples are redstrbuted n such a way that the weght of accurately classfed samples wll be reduced whle the weght of ll classfed samples s rased. Therefore, AdaBoost focuses on the most dffcult ones [8]. The fnal classfer aggregates the selected weak classfer from each teraton, and the weght for each weak classfer depends on ts error rate. However, AdaBoost can be senstve to nose and may ntroduce the overfttng problem. Random forests (RF) s a classfer that combnes many decson trees [9]. Each tree depends on values of a random vector sampled ndependently and wth equal dstrbuton. Each tree casts a unt vote for the most popular case at nput, and random forests outputs the class that s the mode of the

5 Algorthms 009, 354 classes output by ndvdual trees. Breman suggests the generalzaton error for forests converges to a lmt as the number of trees n the forest becomes large [30]. The error of a forest of tree classfers depends on the strength of the ndvdual trees n the forest and the correlaton between them. Usng a random selecton of features to splt each node yelds error rates that compare favorably to Adaboost but are more robust wth respect to nose. Support vector machnes (SVMs) are a set of related supervsed learnng methods used for classfcaton and regresson [3,3]. Vewng nput data as two sets of vectors n an n-dmensonal space, an SVM wll construct a separatng hyperplane n that space, one whch maxmzes the margn between the two data sets. To calculate the margn, two parallel hyperplanes are constructed, one on each sde of the separatng hyperplane, whch are "pushed up aganst" the two data sets. Intutvely, a good separaton s acheved by the hyperplane that has the largest dstance to the neghborng data ponts of both classes, snce n general the larger the margn the lower the generalzaton error of the classfer [33]. SVM have been reported to work well for pharmaceutcal data analyss [34]. There are two man challenges n ths work. One challenge s the two competng effects n ADC changes after treatment. In general, water movement nsde cells s more restrcted than outsde. Thus, ncreased cell densty tends to lower ADC values, whereas ncreased edema (more ntersttal water) results n hgher ADC values. Therefore, theoretcally, ADC values n treated bran tumors could not only ncrease due to the cell kll (and thus reduced cell densty), but also decrease due to nhbton of edema. None of the lsted studes above have specfed the separate effects. Thus, we appled a twocomponent model to ft the tumor ADC hstogram [5]. The other challenge s that t s dffcult to drectly dentfy GBM bran tumors on ADC maps. We developed a sem-automated framework to acheve that goal. There are several contrbutons n ths work. Frst, we developed a computer-aded method to semautomatcally dentfy tumors on ADC maps. Second, we explored the changes of dfferent statstcal features of the whole tumor ADC hstogram. Moreover, we appled a two-component Gaussan mxture modelng to ft the tumor ADC hstogram to overcome the two competng effects. Next, we used earth mover s dstance (EMD) to drectly measure the dstance between the pre- and posttreatment tumor ADC hstograms. Fnally, we ntroduced machne learnng technque to do feature selecton and classfcaton to classfy responders and non-responders. Ths paper s organzed as follows: Secton descrbes the mage acquston and patent group, Secton 3 descrbes the sem-automated dentfcaton of GBM tumors on ADC maps, and Secton 4 descrbes the hstogram feature extracton and classfcaton. The Result Secton reports the performance of the tumor mappng on ADC maps, and the results of our comparatve study for three dfferent classfers. The fnal secton offers a dscusson of the expermental results as well as the future work.. Image Acquston.. Patent cohort A total of 85 patents wth GBM treated by ant-angogeness drugs were ncluded n our prelmnary study from our research database. Images n ths database were acqured as part of

6 Algorthms 009, 355 multcenter GBM treatment tral. Tumors were dagnosed by board-certfed radologsts as responders or non-responders to drugs based on Macdonald crtera from follow-up scans (8 0 weeks after baselne). The Macdonald crtera defne tumor response by use of tumor sze change, sterods, and neurologcal functons. There are four response categores: complete response (CR): dsappearance of enhancng tumors, off sterods, and neurologcally stable or mproved. Partal response (PR): >50% reducton n sze of enhancng tumor, sterods stable or reduced, neurologcally stable or mproved. Progressve dsease (PD): >5% ncrease n sze of enhancng tumor or any new tumor, or neurologcally worse, and sterods stable or ncreased. Stable dsease (SD): all other stuatons. In our study, we used tumor volume to evaluate tumor szes. More than 50% ncrease n volume s consdered to be the PD based on the neuro-radologst s suggeston [44]. Snce GBM s a rapdly progressng dsease, we classfed PD as non-responders and CR, PR and SD as responders. As a result, 39 were responders and 46 were non-responders. The DW-MRI scans were performed 5 7 weeks apart between baselne and follow-up scans. The patents n ths study were pooled from sx medcal stes scanned on 9 dfferent scanner models (GE/Semens) ncludng both.5 T and 3 T scanners. The magng protocol for TwCE s 3D volume n the axal plane wth flp angle-spoled gradent echo sequence (FSPGR) or magnetzaton-prepared rapd gradent-echo (MP-RAGE) sequence, 5 mm slce thckness, mm by mm pxel sze, and n-plane resoluton. The magng protocol for the DW mages s ether DWI or dfferent tensor magng (DTI), 700,000 s/mm for b-value, 3 30 for the number of dffuson senstzaton probng drectons, 5 7 mm slce thckness,.797mm by.797 mm pxel sze, and or 8 8 n-plane resoluton. We developed a qualty assurance technque to evaluate the consstency of ADC measurements from multple scanners and multple vsts by use of ROI analyss wth normal appearng whte matter [35]. Our study [35] showed that there s no sgnfcant dfference n ADC measurement among the dfferent scanner models used. For the between-vst reproducblty, ADC measurement was found to be reproducble wth consstent mage protocols... ADC map dervaton All ADC maps were calculated from DW-MR mages wth the same n-house software usng a twopont method as shown n the followng equaton: ADC = -ln[s(b)/s(0)]/b () wth b beng the dffuson senstvty factor rangng between 700 and,000 s/mm, S(0) and S(b) beng the mage ntensty when b = 0 and b = 700,000 s/mm. For DWI mages, we calculated ADC maps from DW mages by equaton (). For DTI, we calculated ADC for each orentaton and averaged them as the fnal ADC map. Fgure (b) shows an example of a derved bran ADC map.

7 Algorthms 009, 356 Fgure. (a) An example of the tumor segmented on a TwCE mage; (b) An example of the tumor ROI mapped from TwCE to ADC map; (c) An example of the tumor ADC hstogram ftted by two-component Gaussan mxtures. (a) (b) (c) 3. Sem-Automated Image Analyss on ADC Maps All patents were scanned by both TwCE MR mages and DW-MR mages. Snce t s dffcult to segment tumors accurately on derved ADC maps, we segmented tumors on TwCE mages frst, and then mapped the tumor contours onto the correspondng ADC maps. 3.. Tumor segmentaton on TwCE MR mages All tumors were segmented on TwCE mages va a sem-automated method usng the Otsu s thresholdng algorthm [36] and seeded regon growng [37]. Frst, radologsts drew a lne from nsde of the tumor to the outsde of the tumor on the approxmate center slce of the tumor. Then ntensty values along the lne were collected to form a bmodal hstogram, and the Ostu s thresholdng method was used to fnd the optmal thresholdng value. Afterwards, a 3D seeded regon growng was appled to obtan the segmentaton results n the whole volume. Threshold-based segmentaton methods are a standard approach to calculaton of tumor volume. The concept behnd the Otsu s thresholdng method [36] s to fnd the threshold that mnmzes the weghted wthn-class varaton σ as n equaton (), consderng the two-class segmentaton nto object and background: w t t) = P( ) = σ ( t) = q ( t) σ ( t) + q q ( σ ( t) = [ µ ( t)] w t = ( t) σ ( t) P( ) q ( t) µ ( t) = t P( ) q ( t) = wth q( t) as the class probablty, µ ( t ) as class mean, σ ( t ) as class varance, c =, as two dfferent c c () classes, t s varable for the ntensty value, and P(.) s the probablty densty functon. Gven an ntal class mean and varance, the algorthm wll do an exhaustve search by alterng the thresholdng value to fnd the optmal thresholdng value. Afterwards, seeded regon growng [37] usng the optmal thresholdng value was appled to get the tumor contours n the 3D volume. Radologsts revewed the results and made manual correctons when necessary. Fgure (A) shows an example of a segmented tumor on a TwCE mage.

8 Algorthms 009, Tumor mappng from TwCE mages to ADC maps It s dffcult for radologsts to drectly delneate the tumor contours on ADC maps, and the scanner-provded Tw mages and the derved ADC maps are not nherently co-regstered, because they have dfferent slce thckness, dfferent feld of vew (FOV), and dfferent mage resolutons. Therefore, a 3D ROI mappng tool was developed to map the tumor ROIs from TwCE mages onto ADC maps based on the scanner geometry. Compared to the co-regstraton technque, the mappng tool only transformed voxels wthn the tumor ROI rather than the whole mage volume; thus t was more computatonally effcent. However, the mappng tool could not correct for patent moton; thus a board-certfed radologst was requred to vsually check the mapped results and perform manual correctons when necessary. The mappng tool used an affne transformaton wth the parameters extracted from the DICOM header based on physcal locatons. Equaton 3 shows the way to calculate the 3D physcal locaton voxelwse.,j.k s the physcal voxel sze read from the tag pxel spacng and calculated from slce locaton ; X x,y,z, Y x,y,z s mage orentaton read from the tag mage orentaton whch specfes the orentaton of the mage frame rows and columns, Z x,y,z s the z-drecton orentaton calculated from X x,y,z, Y x,y,z, S x,y,z s read from the tag patent poston whch specfes the physcal locaton of the patent s anteror-left-upper corner;, j, k are voxel ndex; and P x,y,z are the calculated physcal locaton of the voxel n mllmeters. The transformaton matrces are calculated for both source and target ROI respectvely. For each voxel n the source ROI, the physcal locaton s frst calculated, and then the nverse operaton s performed to calculate the correspondng voxel coordnates of the target ROI. Fnally, radologsts vsually check the contours on ADC maps and manually correct the tumor contours on ADC when necessary. Fgure (B) shows an example of the mapped tumor ROI on the ADC map from the TwCE mage. Equaton 3. The physcal locaton calculaton of a voxel (,j,k). Px Py Pz = X x X y X z 0 Y x Y y Y y 0 j j j Z Z Z 0 y z x k k k S x S y j S k z 4. Feature Extracton and Classfcaton The dfferences between the features extracted from pre- and post-treatment tumor ADC hstograms are used as the nput to a tumor response classfer. 4.. Observatons Fgure shows examples of tumor ADC hstograms for both pre-and post-treatment wth responders and non-responders. The upper hstogram shows the ADC value dstrbuton before the drug treatment, whle the lower one shows the ADC value dstrbuton after the drug treatment. On the

9 Algorthms 009, 358 left s an example of a volumetrcally respondng tumor, whle on the rght s an example of a nonrespondng tumor. From the fgure, we observe that not only the locaton but also the shape of the responder s hstogram changes after treatment. The two Gaussan mxture components change as well. 4.. General hstogram features Dfferent statstcal features from tumor ADC hstograms were extracted. Accordng to clncal studes, the ADC value should change after treatment. In our data set, we observed that the hstograms exhbt change not only n locaton, but also n shape. Therefore, we ntroduced the extracton of dfferent ADC hstograms features and explored changes n ther pattern. The features are: mean, standard devaton, skewness, kurtoss, medan, IQR (nterquartle range), 5% percentle, and 75% percentle. Fgure. Examples of hstograms from two tumors and two tme ponts: (a), (c): example of a respondng tumor for pre- and post-treatment respectvely; (b), (d): example of a nonrespondng tumor for pre- and post-treatment respectvely Features from GMM Two-component Gaussan mxture modelng was appled to each tumor ADC hstogram and the two-component features were extracted. Due to the competng effects of tumor cell densty and edema, we made the assumpton that the obtaned tumor ADC hstogram was composed of two components relatng to tumor cellularty and edema. We assumed that the component wth lower peak s nfluenced by tumor cellularty, and the component wth hgher peak by edema effects. We used a two component

10 Algorthms 009, 359 GMM as shown n Equaton 3 to ft the ADC hstogram for both baselne and follow-up scans and appled EM algorthm to estmate GMM parameters, wth x as the ntensty values, a as the weght of the components, µ and σ as the Gaussan parameters. f ( x) = = αg ( x), wth G ( x µ ) σ ( x) = e σ π (4) The EM algorthm can be used to estmate the parameters of a parametrc mxture model dstrbuton: the weght of the components a, the Gaussan parameters µ, and σ. It s an teratve algorthm wth two steps: an expectaton step (E-step) and a maxmzaton step (M-step). In the E-step, wth the current parameter estmates of the mxture components, the algorthm calculates the expectaton values for the membershp varables of all data ponts. In the (m+) teraton, the expectaton s: p ( m+ ) n = α = ( m) α G ( m) ( m) G ( x) ( m) ( x) (5) In M-step, the algorthm maxmzes the expectaton value and updates the correspondng parameters. The followng solutons can be developed: µ = N ( m+ ) n= N n= x p n p ( m+ ) n ( m+ ) n ( σ ) = N ( m+ ) n= ( x µ ) ( m+ ) ( m+ ) n n N n= p ( m+ ) n p α N ( m+ ) = p n n n= (6) The features we obtaned from the GMM-EM were named as lower peak mean (LPM), lower peak varance (LPV), lower peak proporton (LPP), hgher peak mean (HPM), hgher peak varance (HPV) and hgher peak proporton (HPP). Fgure shows examples of tumor ADC hstograms ftted by GMM wth low ADC and hgh ADC curves overlad. Combnng GMM features wth the statstcal features, we obtaned 4-dmensonal feature vectors for both pre- and post-treatment tumor hstograms. Afterwards, we calculated the rate of change between the pre- and the post-treatment tumor hstogram. Therefore, we had a 4-dmensonal vector as the dfference feature vector Earth Mover s Dstance Fnally, we appled the earth mover's dstance (EMD) [38,39] as a metrc to drectly evaluate the dstance between the pre- and post-treatment tumor ADC hstograms. Informally, f the hstograms are nterpreted as two dfferent ways of plng up a certan amount of drt over the regon D, the EMD s the mnmum cost of turnng one ple nto the other; where the cost s assumed to be amount of drt moved tmes the dstance by whch s moved. The calculated EMD value was appended as the 5th element n the dfference feature vector. The calculated 5-dmensonal vector was the nput feature vector for classfcaton.

11 Algorthms 009, Classfcaton In ths study for classfcaton, we nvestgated three classfcaton technques wth dfferent characterstcs: AdaBoost, random forests (RF) and support vector machne (SVM). We employed three classfers to avod basng the results by selecton of a sngle classfcaton method. The reason we choose them s that the frst two classfers both nclude a feature selecton mechansm. By applyng these two classfcaton technques, we are seekng the best features that would separate responders from non-responders. SVM s reported to outperform several of the most frequently used machne learnng technques n structure actvty relatonshp (SAR) analyss. [34] In ths study, all classfers were mplemented n the open source data mnng software Weka [4]. Ther performance was evaluated usng 0-fold cross valdaton method. Three experments were performed. Frst, the conventonal method of usng mean ADC for treatment response classfcaton was appled []. Second, the AdaBoost, RF classfer, and SVM were appled to the dfference feature vectors of general statstcal hstogram features wthout GMM features, and results from the three classfers were compared. Fnally, the three classfers were appled usng all statstcal features ncludng the GMM features, and the results were compared, and the results of accuraces from dfferent classfcaton technques were compared wth conventonal method of ADC mean changes by the test of proporton. 5. Results 5.. Segmentaton Performance Fgure 3 shows four examples of segmentaton on TwCE mages and the mapped results on the derved ADC maps. For quanttatve evaluaton of the tumor segmentaton mappng results, we randomly selected 3 subjects baselne data. The 3 tumors are from an ADC mappng database, 0 of whch have dfferent mage resolutons between the TwCE and ADC mages n all three dmensons and of whch have exactly the same 3D mage resoluton n both modaltes. We calculated the overlap rato between the mapped ROI generated automatcally by the tool and an ROI corrected by a neuro-radologst. The overlap rato (OR) s defned by Equaton 6, where A and B are two tumor ROIs and sze(.) s the number of voxels n that ROI. * sze ( A B) ( sze( A) + sze( B)) The results are shown n Table 3 wth 0 out of 3 ROIs (64.5%) have an overlap rato over 90%. Table 3. Dstrbuton of overlap ratos. Overlap rato 00% 95~00% 90~95% 80~90% 60~80% 0~60% Number of patents (7)

12 Algorthms 009, 36 Fgure 3. (a)-(d) and ()-(l) show four examples of tumor segmentatons on TwCE mages; (e)-(h) and (m)-(p) show the correspondng mapped tumor contours on ADC maps. (a) (b) (c) (d) (e) (f) (g) (h) () (j) (k) (l) (m) (o) (p) (q)

13 Algorthms 009, Classfcaton Performance Usng the conventonal method of mean ADC change (subjects wth a mean ADC ncrease classfed as responders and those wth an ADC decrease as non-responders) [,0], the accuracy s 9.4% (5/85), wth a senstvty of 7.95% and a specfcty of 60.87% (see Table 4). Table 4. Performance of the conventonal mean ADC classfcaton method. Classfer Senstvty Specfcty Accuracy Az Mean ADC change 7.95% 60.87% 9.4% 0.33 The experment wth AdaBoost nvolved 0 learnng teratons. The RF classfer was composed of 0 trees, each of whch s constructed consderng fve random features. The SVM classfer used non-lnear polynomal kernels and normalzed all features. The results for the experment usng only the general hstogram features wthout GMM are shown n Table 5 wth senstvty, specfcty, accuracy and area under the ROC curve (Az). The ROC curves are shown n Fgure 4. The curve usng conventonal mean ADC was plotted by varyng the threshold of the mean ADC change used for the classfcaton, whle the curve usng the three ML technques were plotted by Weka. Weka plots the ROC curves by varyng the threshold on the probablty assgned to the postve class. Table 5. Performance comparson among three classfers wthout GMM features. Classfer Senstvty Specfcty Accuracy Az AdaBoost 45.45% 75% 63.53% * 0.6 Random forest 54.55% 73% 65.88% * 0.66 SVM 7.7% 9.3% 67.06% * 0.60 (*: All p-values <0.000 comparng wth accuracy of Table 4) Fgure 4. ROC curve for three classfers wthout GMM features. Wth GMM features added, the three classfers wth the same parameter setups were appled to the data. The results are shown n Table 6 wth senstvty, specfcty, accuracy and area under the curve (Az) of the ROC curve. The ROC curves are shown n Fgure 5.

14 Algorthms 009, 363 Table 6. Performance comparson among three classfers wth GMM features. Classfer Senstvty Specfcty Accuracy Az AdaBoost 39.39% 80.77% 64.7% * 0.60 Random forest 5.5% 80.77% 69.4% * 0.70 SVM 7.7% 9.3% 67.06% * 0.60 (*: All p-values <0.000 comparng wth accuracy of Table 4) Fgure 5. ROC curve for three classfers wth GMM features. 6. Dscusson Compared to usng only the mean ADC value, the quanttatve statstcal hstogram features and the proposed classfcaton system tremendously mproved the accuracy from 9.4% to 69.4% (Az ncreased from 0.33 to 0.70). The statstcal analyss ndcates that all three classfers are sgnfcantly dfferent from the conventonal mean ADC method wth our dataset. Compared to general statstcal hstogram features, the classfcaton wth GMM features usng random forest technque slghtly mproved the accuracy from 65.88% to 69.4%, whle adaboost and RF classfers generated the same accuracy no matter whether GMM features were ncluded. There s no sgnfcant dfference between the three machne-learned classfers. The conventonal mean ADC method performs worse than a random classfer (Az < 0.5). The reason s that conventonally researchers hypotheszed that mean ADC ncreases because the tumor cell densty decrease after an effectve treatment. Ths assumpton may not be vald for our dataset, because t nvolves n an ant-angogeness drug, whch suppresses the cancer cell growth wthout necessary kllng tumor cells (decreasng ther densty) at an early stage (5-7 weeks). Another possble reason s that n our dataset many of the GBM tumors are recurrent GBM tumors that are usually necrotc. The treatment tends to reduce necross and edema, whch wll dmnsh ADC. Essentally there are two competng processes at work: cell densty, edema and necross [5]. Another state-of-art study ncluded features that capture spatal nformaton n tumor heterogenety features. Functonal dffuson map (fdm) [,3] s a popular technque studyng the ADC value ncrease or decrease voxel-by-voxel. Moffat et al. appled fdm to 0 patents, classfed patents nto

15 Algorthms 009, 364 the three categores: PR, SD and PD, and reported 00% accuracy []. However, the threshold they used for classfcaton was determned from a sngle dataset of 0 patents used for both tranng and testng, whle n our experments, a cross valdaton analyss was performed. In Moffat et al s study, they explored the assessment of fractonated radaton therapy for dfferent types of bran tumors wth 0 patents scanned on the same scanner []. However, n our study, we focused on the GBM bran tumors treated by ant-angogeness drugs, whch suppress the blood supply for the tumor cells and may not drectly decrease the tumor cellularty. The dfference n accuracy may come from the dfferent mechansm of treatment. Addtonally, our dataset s from GBM drug trals across multple stes, thus our prelmnary study s an mportant contrbuton for explorng DWI as an early magng bomarker n a real pharmaceutcal drug tral. In future work, we wll extract texture feature to nclude spatal nformaton, and shape features wll be extracted as well. By ntroducng a new rcher feature set ndcatng more useful tumor nformaton, we am to nclude more nformaton about tumors and further mprove the performance of the classfcaton system. One lmtaton of ths study s that we classfed CR, PR and SD as responders for the ground truth to acheve a bnary classfcaton. Snce SD and PR may have dfferent patterns n terms of ther ADC hstogram change, a mult-category classfcaton system wll be explored n future work. Another lmtaton of the study s that we used the Macdonald crtera at the eghth or tenth week after treatment for determnng treatment response. In future work, tme-to-progresson and survval tme wll be a better endpont to classfy treatment response. Another lmtaton comes from the 3D ROI mappng tool. Ths tool s more computatonally effcent compared to the co-regstraton technques, but t cannot correct for patent moton. Therefore, n our study, a board-certfed radologst s vsually checked and edted all segmentaton results as needed. In the future, a more sophstcated regstraton method wth an mage smlarty measure may mprove the accuracy of the tumor contours on ADC maps, and consequently mprove the accuracy of the extracted features and the classfer performance. ADC values obtaned on pre-operatve MRI scans are reported to be of prognostc value n patents wth globlastoma [5,4]. The term "prognoss" refers to predctng the lkely outcome of treatment. ADC, reported to be nversely proportonal to tumor cellularty, s ganng nterest n predctng GBM tumor prognoss. Our proposed framework now uses changes n DW-MRI for early predcton of treatment response; however, the framework wth feature extracton and machne learnng technque could be generalzed to pre-treatment DW-MRI for prognoss predcton. In ths study, we developed a CADrx framework wth machne learnng technques to automatcally predct tumor treatment response before the sze change usng DW-MRI. In our prelmnary study, our major contrbutons are extractng statstcal ADC hstogram features, applyng GMM to model the ADC hstogram to nterpret the competng effects of cellular densty and edema, and applyng machne learnng technques usng all the extracted features. Cell densty and edema may be reflected n ADC values before sze changes are apparent on standard MRI sequences. Therefore, ADC holds promse as a bomarker, n determnng both whch tumors are more lkely to respond to treatment and whch tumors are actually respondng. In concluson, ths work shows that a CADrx system usng quanttatve ADC hstogram features and a machne-learned classfer has better performance n treatment response assessment over conventonal analyss usng only a mean ADC value. Ths wll have major mplcatons for clncal trals. Ths work has potental clncal sgnfcance for early treatment response assessment n GBM.

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