Towards Prediction of Radiation Pneumonitis Arising from Lung Cancer Patients Using Machine Learning Approaches

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1 Towards Predcton of Radaton Pneumonts Arsng from Lung Cancer Patents Usng Machne Learnng Approaches Jung Hun Oh, Adtya Apte, Rawan Al-Loz, Jeffrey Bradley, Issam El Naqa * Dvson of Bonformatcs and Outcomes Research, Department of Radaton Oncology, Washngton Unversty School of Medcne, MO 63110, USA ABSTRACT Purpose: Radaton pneumonts (RP) s a potentally fatal sde effect arsng n lung cancer patents who receve radotherapy as part of ther treatment. For the modelng of RP outcomes data, several predctve models based on tradtonal statstcal methods and machne learnng technques have been reported. However, no gudance to varaton n performance has been provded to date. Materals and methods: In ths study, we explore several machne learnng algorthms for classfcaton of RP data. The performance of these classfcaton algorthms s nvestgated n conjuncton wth several feature selecton strateges and the mpact of the feature selecton strategy on performance s further evaluated. The extracted features nclude patent s demographc, clncal and pathologcal varables, treatment technques, and dose-volume metrcs. In conjuncton, we have been developng an n-house Matlab-based open source software tool, called dose-response explorer system (DREES), customzed for modelng and explorng dose response n radaton oncology. Ths software has been upgraded wth a popular classfcaton algorthm called support vector machne (SVM), whch seems to provde mproved performance n our exploraton analyss and has strong potental to strengthen the ablty of radotherapy modelers n analyzng radotherapy outcomes data. These tools are demonstrated on an nsttutonal non-small cell lung carcnoma (NSCLC) dataset of patents who receved radotherapy. Results: Our methods were appled to an NSCLC dataset that conssts of 09 patents nformaton, each havng 160 varables. Usng several feature selecton methods, relevant features were searched. Subsequently, wth the selected features, varous classfcaton algorthms were tested. Through these experments, we showed the usefulness of machne learnng methods n the analyss of radaton oncology dataset. Conclusons: We have presented an open-source software tool and several machne learnng algorthms for analyzng radotherapy outcomes. We demonstrated the tool on a lung cancer patent dataset. We beleve that the mproved tool wll provde radaton oncology modelers wth new means to analyze radaton response data. Keywords: radaton pneumonts, machne learnng, nformatcs, DREES Dsclosure: The authors declare no conflcts of nterest. 1. INTRODUCTION Lung cancer s a leadng cause of cancer-related death n both men and women n the world wth a low fve-year survval rate of 15% (Amercan Cancer Socety 008). Two man types of lung cancer are small cell lung carcnoma (SCLC) and non-small cell lung carcnoma (NSCLC). Approxmately, 80% of lung cancer cases are classfed as NSCLC. About 50% of lung cancer patents receve radotherapy n addton to or nstead of surgery and t s the man treatment for patents wth advanced and noperable stages (Amercan Cancer Socety 008). One of the potentally fatal sde effects of radotherapy n lung cancer s radaton-nduced lung njury known as radaton pneumonts (RP) that results from over-dosage of surroundng normal tssues (Deasy et al. 00; El Naqa et al. 006a, 006b; Spencer et al. 009). Thus, the optmzaton of treatment plannng dose dstrbutons s crucal for provdng tumor tssues wth suffcent doses whle sparng normal tssues from excessve radaton effects. Recent advances n radotherapy and botechnology such as hghly advanced 3D treatment plannng systems provde new opportuntes to precsely estmate tumor local control probablty and complcaton rsk to surroundng normal tssues, whch allows for not only mprovements of tumor localzaton and dose dstrbuton but also ndvdualzed and patent-specfc treatment plannng decsons (Hope et al. 006). Nevertheless, the lack of dedcated nformatcs tools for extractng and analyzng metrcs that could be related to *elnaqa@wustl.edu; phone ; fax ; radonc.wustl.edu Ths artcle s lcensed under a Creatve Commons Attrbuton 4.0 Internatonal Lcense. do: /jro ISSN: X, J Radat Oncol Inform 009; 1:1:30-43

2 Hun Oh et al.: Predcton of Rad. Pneumonts Arsng from Lung Ca. Pts Usng Mach. Learnng Approaches radotherapy outcomes such as RP poses challenges for predcton of such effects and customzaton of treatment plan desgn based on expected rsk. We demonstrate the development of such tools n ths study by comparng dfferent machne learnng strateges for dentfcaton of factors that could be assocated wth RP. Ths process s composed of three steps: (1) selectng relevant varables, () buldng approprate classfers based on supervsed learnng, and (3) presentng robust tools to the radaton oncology communty through our open-source software. Proper feature selecton s a major challenge n machne learnng and s posed as the ablty to select a subset of features that wll represent the dataset or dstngush one patents group from another group. The objectves of feature selecton are manfolds: to mprove the learner (.e., classfer) performance such as ts accuracy or speed and to understand the underlyng process that generates the data. Feature selecton strateges desgned wth dfferent evaluaton crtera are manly dvded nto two categores: the flter approach and the wrapper approach. The crtera used by these approaches nclude dstance measures (Bns & Draper 001; Sebban & Nock 00), dependency measures (Yu & Lu 004), consstency measures (Dash & Lu 003; Lashka & Anthony 004), and nformaton measures (Battt 1994; Kwak & Cho 00). The flter method selects relevant feature subsets based upon characterstcs of the data wthout nvolvng any classfcaton algorthm. In contrast, the wrapper method employs a predetermned classfcaton algorthm to evaluate the qualty of features. It tends to requre ntensve computatons whle t outperforms the flter method n general. In order to use advantages of both the flter and wrapper methods, hybrd approaches have been also proposed. These methods not only mprove the performance but speed up the feature selecton task. In a varety of bonformatcs areas, the feature selecton methods have been used, ncludng sequence analyss (Salzberg et al. 1998; Delcher et al. 1999), mcroarray analyss (Alon et al. 1999; Ben-Dor et al. 000; Golub et al. 1999), mass spectra analyss (Petrcon & Lotta 003; Oh et al. 009), sngle nucleotde polymorphsm (SNP) analyss (Daly et al. 001), and text mnng (Cohen & Hersch 005; Jensen, Sarc & Bork 006; Saeys, Inza & Larrañaga 007). Classfcaton s a problem of assgnng a sample to a predefned class based on condtonal features. Many common classfcaton technques, ncludng lnear dscrmnate analyss (LDA), decson tree, neural networks, SVM, k-nearest neghbor (knn), and Bayesan classfers, have been proposed n a varety of applcatons. LDA and SVM are two man knds of lnear classfers. That s, they seek to fnd a hyperplane for whch one group can be correctly separated from another group as much as possble (Lotte et al. 007). SVM proposed by Vapnk and hs colleagues s a novel approach for solvng classfcaton problems. It s based on the structural rsk mnmzaton prncple to mnmze an upper bound of the generalzaton error (Vapnk 1995; Jeng 006). A major part of our nformatcs efforts s focused towards provdng better tools to the radotherapy outcome analyst to gan a more nsghtful understandng of complex varable nteractons that affect outcome and support treatment plannng systems wth mproved predctve models of response. Therefore, we have upgraded our n-house software tool DREES (dose-response explorer system) wth several statstcal and graphcal tools; n partcular, we have added a new machne learnng module based on SVM as dscussed further below. The remander of ths paper s organzed as follows. In Sectons and 3, we ntroduce feature selecton and classfcaton algorthms nvestgated n ths study. In Secton 4, we present a new verson of DREES that s equpped wth SVM. Expermental results wth dose-volume data n lung cancer are shown n Secton 5. Fnally, we summarze our conclusons n Secton 6.. FEATURE SELECTION TECHNIQUES.1 SVM-Recursve Feature Elmnaton (SVM-RFE) SVM-RFE, proposed by Guyon et al., s a sequental backward feature elmnaton method based on SVM (Guyon et al. 00). In SVM-RFE, features are ranked n a way that the least mportant feature s removed after teratvely tranng a SVM classfer wth exstng features. To determne a feature to be elmnated at each teraton, the weghts (w ) are estmated (see below) and a feature wth the smallest w value n the weght vector s removed.. Correlaton based Feature Selecton A correlaton based feature selecton method measures correlatons between features and tres to fnd the best feature subset by usng a heurstc search strategy n a manner of the forward best frst search (Hall & Smth 1999). The fundamental dea behnd the method s that good features are hghly correlated wth the class, but uncorrelated wth each other. The evaluaton functon of a subset of features s: J Radat Oncol Inform 009; 1:1:30-43

3 Hun Oh et al.: Predcton of Rad. Pneumonts Arsng from Lung Ca. Pts Usng Mach. Learnng Approaches 3 EV S hr cf # (1) h " h( h!1) r ff where EV S represents the heurstc evaluaton of a feature subset S contanng h features; r cf and feature-class correlaton and the mean feature-feature ntercorrelaton, respectvely..3 Ch-square Feature Selecton r ff are the mean A Ch-square feature selecton method s a smple algorthm based on the! statstc to dscretze features repeatedly untl some nconsstences are found n the data (Lu & Setono 1995). As a result of dscretzaton, the feature selecton s completed. The measure of the Ch-square s defned to be: where, k s the number of classes, k ( Aj! Ej ) % # $$ () E # 1 j # 1 A j s the number of patterns n the th nterval, jth class, R s the number of patterns n the th nterval = $ j # C j s the number of patterns n the jth class = $ # A N s the total number of patterns = $ # R 1, E j s the expected frequency of A j = R C j /N. k 1 j, A 1 j, j.4 Informaton Gan based Feature Selecton An nformaton gan based feature selecton s an algorthm based on nformaton theory for feature selecton n multclass problems. Let S be the set of nstances from k classes,.e., c 1, c,, c k. The entropy of the class dstrbuton n S s defned as follows: k c c I ( S) #! $ log. (3) # 1 S S Then, the nformaton gan of nstance set S based on attrbute F s calculated as Gan ( F ) # I( S)! I( S F ), (4) t S j # I ( S)! $ & I( S j ) S j # 1 where t s the set of all the possble values of feature F. The nformaton gan reflects the reducton n uncertanty about the overall class entropy when a certan feature F s gven. In other words, features wth zero nformaton gan ndcate the nablty to reduce such uncertanty and should be removed (Oh et al. 008). 3. CLASSIFICATION METHODS J Radat Oncol Inform 009; 1:1:30-43

4 Hun Oh et al.: Predcton of Rad. Pneumonts Arsng from Lung Ca. Pts Usng Mach. Learnng Approaches Support Vector Machne SVM s a supervsed learnng algorthm, orgnally desgned to solve two-class classfcaton problems (Burges 1998; El Naqa et al. 00; El Naqa et al. 009; Oh et al. 006). The basc dea behnd SVM s to fnd an optmal hyperplane for whch a gven tranng data are well separated. It s acheved by maxmzng the margn between the two classes after mappng the tranng data x nto a hgher dmensonal space va a mappng functon "(x). As a result, a decson functon s as follows: f ( x) #) w, ((x) ' " b, (5) where w s a weght vector and b s a scalar. Suppose that there are n tranng samples {(x, y ), 1 # # n} where x s the th tranng sample consstng of an m- dmensonal feature vector and y!{ 1, 1} s the class label of x. The problem of fndng the optmal hyperplane can be formulated as the followng optmzaton problem subject to n 1 mn w " C$ *, (6) # 1 y ( x ) 1! *, * + 0, f + where! s a slack varable and C s a user defned soft-margn constant whch regularzes the trade-off between tranng error and margn maxmzaton. Ths optmzaton problem can be solved n ts Wolfe dual form wth respect to Lagrange multplers and can be reduced to a quadratc programmng problem: subject to 1 mn n n $$ # 1 j # 1 n $,, y y K(x,x )!,, (7) j j j # 1 Here, we can compute the weght vector as w: $ 0 -, - C,, y n # 1 # 0. l $ w #, ((x ), (8) # 1 y where " s Lagrange multplers and l s the number of support vectors. In Eq. (7), "(x ) T "(x j ) s substtuted wth a kernel functon K(x,x j ) by the kernel trck. Note that for the lnear case K(x,x j ) = x x j. Two typcal kernels are polynomal: K(x,x j ) = (e + x x j ) d and radal bass functon (RBF): K(x,x j ) = exp( 1/(# ) x x j ) where e, d, and # are adjustable kernel functon parameters (El Naqa, Bradley & Deasy 008). 3. Decson Trees A decson tree classfer has a herarchcal structure n whch the data set s recursvely parttoned untl each partton conssts entrely or almost entrely of samples from one class. In the tree, leaf nodes represent classes and non-leaf nodes ndcate selected decson rules. Startng at the root node, one sample s evaluated by the decson rule. It keeps movng down the tree branch untl t reaches a leaf node. We used J48 that s mplemented as a decson tree classfer n WEKA (Wtten & Frank 005). 3.3 Random Forest A random forest classfer s an ensemble of classfcaton trees grown on bootstrap samples of the tranng data n conjuncton wth a random feature selecton n the tree nducton process. Gven a new nput, each tree casts a vote and the class havng the most votes s chosen (Rodrguez, Kuncheva & Alonso 006). J Radat Oncol Inform 009; 1:1:30-43

5 Hun Oh et al.: Predcton of Rad. Pneumonts Arsng from Lung Ca. Pts Usng Mach. Learnng Approaches Nave Bayes In a nave Bayes classfer, t s assumed that all features are mutually ndependent gven a class label, that s, each feature has the class varable as ts parent (Fredman, Geger & Goldszmdt 1997). In practce, despte ts smplfed assumpton the nave Bayes classfer has often shown good performance compared to sophstcated classfcaton methods n a varety of applcatons. In the nave Bayes classfer, the most probable class s obtaned by usng the Bayes theorem: c * n. # arg max p( c) # p(x c). (9) c # 1 (a) Parameters for SVM (b) The results of SVM classfcaton J Radat Oncol Inform 009; 1:1:30-43

6 Fgure 1. SVM classfcaton n DREES 4. DREES SOFTWARE Towards fulfllng our objectve to provde clncans and scentsts wth an accurate, flexble and user-frendly tool to explore radotherapy outcomes data and model the statstcal tumor control or normal tssue complcaton, we have developed an open-source software called DREES that enables clncal researchers to customze the functon for radotherapy outcome modelng (El Naqa et al. 006b). DREES s avalable from Recently, we ncorporated a popular SVM code called LbSVM ( nto DREES so that t can provde more powerful ablty for the analyss of radotherapy data (Chang & Ln 001). Fgure 1 llustrates a screenshot of the nterface for usng SVM n DREES. The results of SVM classfcaton are shown n Command Wndow of Matlab. In addton, new vsual representaton usng SVM was developed as shown n Fgure 1. The fgure shows the contour plot of SVM for two features, that s, the contour plot represents the kernel-based pneumonts nonlnear predcton model. The gray lne ndcates the hyperplane of the SVM classfer. The software s shared based on the GNU General Publc Lcense (GPL) v3. *elnaqa@wustl.edu; phone ; fax ; radonc.wustl.edu Copyrght 009 Journal of Radaton Oncology Informatcs ISSN: X, J Radat Oncol Inform 009; 1:1:30-43

7 Hun Oh et al.: Predcton of Rad. Pneumonts Arsng from Lung Ca. Pts Usng Mach. Learnng Approaches 7 Table 1. Top ranked 10 features for each feature selecton strategy. For CFS, only 5 features were found by ts crteron. Rankng IG Ch-square SVM-RFE CFS 1 Followup Followup D10_heartMC COMSI COMSI MOH10_heartMC V40_heartMC PerformanceStatus 3 V55_heartMC MOH5_heartMC V5_heartMC Followup 4 MOH5_heartMC V55_heartMC DCOMSI_heart D0_lungMC 5 MOH10_heartMC D5_heartMC D55_lungMC MOH10_heartMC 6 D10_heartMC COMSI maxdose 7 D35_lungMC D10_heartMC PerformanceStatus 8 D5_heartMC MOH0_heartMC V30_heartMC 9 MOH0_heartMC D35_lungMC TmeAxs 10 MOH15_heartMC V65_heartMC D15_heartMC Table. The performance when the correlaton based feature selecton s used. Methods MCC Accuracy Senstvty Specfcty AUC Naïve Bayes Random Forest Decson Tree RBF-SVM P-SVM L-SVM EXPERIMENTAL RESULTS 5.1 The Data Set In ths study, we analyzed an NSCLC dataset that conssts of nformaton obtaned from 09 patents at Washngton Unversty School of Medcne, who had receved radotherapy wth medan doses around 70 Gy as part of ther treatment. The dose dstrbuton was recalculated usng Monte Carlo methods (MC). The number of patents dagnosed wth RP was 48 patents called the dsease group. The remanng 161 patents belong to the control group. The data obtaned from each patent s composed of clncal features (age, gender, race, chemo, stage, smoke, treatment, etc.), relatve locaton of the tumor wthn the lung or nearby heart, and dosmetrc features, ncludng mean dose, maxmum dose, mnmum dose, V x (volume gettng at least x Gy), D x (mnmum dose to the hottest x% volume), MOH x (mean dose to the hottest x% volume), MOC x (mean dose to the coldest x% volume) and GEUD (generalzed equvalent unform dose). In ths study, we ncluded heart related varables due to the fact that n the radotherapy of lung cancer, a porton of the heart s typcally exposed to a relatvely hgh dose of radaton that causes heart njury (Shafman et al. 004). Recently, Deasy et al. have reported that heart dose-volume metrcs may play a role n RP (Deasy et al. 008). J Radat Oncol Inform 009; 1:1:30-43

8 Hun Oh et al.: Predcton of Rad. Pneumonts Arsng from Lung Ca. Pts Usng Mach. Learnng Approaches 8 Table 3. The performance when the Ch-square feature selecton s used. 5. Machne Learnng Methods For analyss of the dataset, a varety of machne learnng methods for feature selecton and classfcaton were tested. For feature selecton, nformaton gan (IG) based feature selecton, ch-square feature selecton, correlaton based feature selecton (CFS), and SVM-RFE were used. For classfcaton, random forest (RF), nave Bayes (NB), decson tree (DT), and SVM were employed. In SVM, the experments were carred out changng parameters. The parameter values used n ths study are as follows: # n radal bass functon SVM (RBF-SVM) vares n {0.5, 1,, 3, 4, 5}; degree d and coeffcent e n polynomal SVM (P-SVM) vary n {1,, 3, 4} and {0, 1}, respectvely; for C, {1, 10, 100} are set. By combnng these parameters, 18 RBF-SVMs, 4 P-SVMs, and 3 lnear SVMs (L-SVMs) are formed. Snce the dataset s mbalanced n sze, n SVMs weghtng values of 3 and 1 were placed nto the dsease group and control group, respectvely. J Radat Oncol Inform 009; 1:1:30-43

9 Hun Oh et al.: Predcton of Rad. Pneumonts Arsng from Lung Ca. Pts Usng Mach. Learnng Approaches 9 Table 4. The performance when SVM-RFE s used. 5.3 Performance Metrc Our comparatve experments were performed usng the WEKA software package ( For the unbased performance estmate, all measurements were averaged after 30 teratons of 10-fold cross-valdaton (CV) for each classfcaton algorthm. In the analyss of mbalanced data set, Matthew's correlaton coeffcent (MCC) s wdely used as a performance evaluaton metrc. MCC s calculated as follows: TP& TN! FP& FN r # (10) (TP " FP)(TP " FN)(TN " FP)(TN " FN) where TP and TN are the number of patents correctly classfed n the dsease and control group, and FN and FP are the number of patents falsely classfed n the dsease and control group, respectvely. r takes a real value n [ 1.0, 1.0]. A coeffcent of +1 means a perfect classfcaton. In contrast, 1 represents a perfect nverse predcton. A coeffcent of zero ndcates an average random predcton. In addton, we measured accuracy, senstvty, specfcty, and AUC (area under the ROC curve) as performance evaluaton metrcs. The accuracy, senstvty and specfcty are defned as follows: J Radat Oncol Inform 009; 1:1:30-43

10 Hun Oh et al.: Predcton of Rad. Pneumonts Arsng from Lung Ca. Pts Usng Mach. Learnng Approaches 10 TP " TN Acc #, TP " FN " TN " FP TP Sen #, TP " FN TN Spe #. (11) TN " FP Table 5. The performance when the nformaton gan based feature selecton s used. Table 6. The performance when all features wthout feature selecton are used. Methods MCC Accuracy Senstvty Specfcty AUC Naïve Bayes Random Forest Decson Tree RBF-SVM P-SVM L-SVM J Radat Oncol Inform 009; 1:1:30-43

11 Hun Oh et al.: Predcton of Rad. Pneumonts Arsng from Lung Ca. Pts Usng Mach. Learnng Approaches Feature Selecton and Classfcaton Table 1 dsplays the top ten features selected by the three feature selecton algorthms. For CFS, only fve features were chosen by ts crteron. It s worthy to note that some features were commonly found n dfferent feature selecton methods. For example, 'Followup', 'COMSI' (center-of-mass of tumor locaton n the superor nferor drecton), and 'MOH10_heartMC' were selected n IG, Ch-square, and CFS. 'D10_heartMC' was found n IG, Ch-square, and SVM- RFE. It suggests that the features are mportant for dstngushng the dsease (RP) group from the control (no RP) group. Table through Table 5 show the performance n classfcaton algorthms for four dfferent feature selecton strateges wth the top one, then the top two, and so forth up to the top ten features. Note that Table llustrates the results obtaned usng all fve features that were searched by CFS. Interestngly, n all cases kernel SVMs (RBF-SVM and P-SVM) acheved the best MCC on ths dataset. In partcular, wth features found by SVM-RFE, the performance of RBF-SVM and P-SVM outperformed consderably other methods. As shown n Table, the best MCC was obtaned when CFS was exploted wth RBF-SVM, resultng n Also, P-SVM ganed comparable MCC (0.4118). Table 6 shows the MCC values when all features were used wthout feature selecton. As can be seen n the table, n all cases MCC values were much lower than those ganed when only a few mportant features were utlzed. It justfes the mportance of feature selecton n classfcaton algorthms. Fgure dsplays the maxmum MCC values across all classfcaton algorthms for each feature selecton method. The frst bar n the fgure represents the MCC value when all features were used. The hghest MCC value (0.4131) was acheved when RBF-SVM wth C = 100, # =, and the fve features n conjuncton wth CFS were employed. Also, accuracy, senstvty, specfcty, and AUC obtaned wth these parameters were 74.74%, 69.57%, 76.7%, and 0.79, respectvely. (a) The maxmum MCC for each feature selecton method Feature Selecton Max-MCC Method No. of features C # d e w/o FS RBF-SVM CFS RBF-SVM Ch-square P-SVM IG RBF-SVM 10 SVM-RFE P-SVM (b) Parameter values used for achevng the maxmum MCC Fgure. The comparson of the maxmum MCC across all classfcaton algorthms for each feature selecton method. Note that w/o FS means wthout feature selecton. J Radat Oncol Inform 009; 1:1:30-43

12 Hun Oh et al.: Predcton of Rad. Pneumonts Arsng from Lung Ca. Pts Usng Mach. Learnng Approaches 1 6. CONCLUSION We have compared the performance of dfferent machne learnng algorthms n dentfyng sgnfcant features that are related to RP and could be used n buldng patents classfcaton rsk models of ths dsease. In our classfcaton experments wth the selected features, the kernel SVMs showed a hgher MCC than not only lnear SVM but also other competng classfcaton algorthms after correcton for mbalance effect. It s our expectaton that the applcaton of machne learnng methods to the analyss of post-radotherapy data wll shed more lght on a better understandng of underlyng mechansms n normal tssue toxctes and advance the clncal translatonal goal of ndvdualzng radotherapy n NSCLC patents. To support ths goal, we have developed a graphcal user nterface (GUI) tool to explore radotherapy outcomes data and buld data-drven statstcal tumor control or normal tssue complcatons. We wll contnue to develop DREES based on user s feedback, as an nformatcs tool to ad medcal physcsts and clncal researchers to buld more predctve radotherapy outcome models. 7. ACKNOWLEDGEMENTS We thank Dr. Joseph Deasy for valuable suggestons. Ths work was partally supported by NIH grant 1K5CA REFERENCES [1] Alon, U, Barka, N, Notterman, DA, Gsh, K, Ybarra, S, Mack, D & Levne, AJ 1999, 'Broad patterns of gene expresson revealed by clusterng analyss of tumor and normal colon tssues probed by olgonucleotde arrays', Proc. Nat. Acad. Sc., vol. 96, [] Amercan Cancer Socety 008, Cancer Facts and Fgures, Atlanta, GA: Amercan Cancer Socety. [3] Battt, R 1994, 'Usng mutual nformaton for selectng features n supervsed neural net learnng', IEEE Trans. Neural Networks, vol. 5, no. 4, pp [4] Ben-Dor, A, Bruhn, L, Fredman, N, Nachman, I, Schummer, M & Yakhn, Z 000, 'Tssue classfcaton wth gene expresson profles', J. Comput. Bol., vol. 7, [5] Bns, J & Draper, B 001, 'Feature selecton from huge feature sets', n Proc. Int. Conference Computer Vson, pp [6] Burges, C 1998, 'A tutoral on support vector machnes for pattern recognton', Data Mnng and Knowledge Dscovery, vol., pp [7] Chang, C & Ln, C 001, LIBSVM: A Lbrary for Support Vector Machnes, software avalable at [8] Cohen, A & Hersch, W 005, 'A survey of current work n bomedcal text mnng', Bref. Bonformatcs, vol. 6, [9] Daly, MJ, Roux, JD, Schaffner, SF, Hudson, TJ & Lander, ES 001, 'Hgh-resoluton haplotype structure n the human genome', Nat. Genet., vol. 9, 9 3. [10] Dash, M & Lu, H 003, 'Consstency-based search n feature selecton', Artfcal Intellgence, vol. 151, pp [11] Deasy, JO, Nemerko, A, Herbert, D, Yan, D, Jackson, A, Haken, RT, Langer, M, Sapareto, S & AAPM/NIH 00, 'Methodologcal ssues n radaton dose-volume outcome analyses: summary of a jont AAPM/NIH workshop', Medcal Physcs, vol. 9, no. 9, pp [1] Deasy, JO, Trovo, M, Huang, EX, Mu, Y, El Naqa, I, Bradley, JD 008, 'Hgh-dose heart rradaton s a statstcally sgnfcant rsk factor for radaton pneumonts wthn logstc-multvarate modelng', ASTRO 008. [13] Delcher, A, Delcher, AL, Harmon, D, Kasf, S, Whte, O & Salzberg, SL 1999, 'Improved mcrobal gene dentfcaton wth GLIMMER', Nuclec Acds Res., vol. 7, [14] El Naqa, I, Yang, Y, Wernck, M, Galatsanos, N & Nshkawa, R 00, 'A support vector machne approach for detecton of mcrocalcfcatons', IEEE Trans. Medcal Imagng, vol. 1, no. 1, pp [15] El Naqa, I, Bradley, J, Blanco, A, Lndsay, P, Vcc, M, Hope, A & Deasy, J 006, 'Multvarable modelng of radotherapy outcomes, ncludng dose-volume and clncal factors', Int. J. Radat. Oncol. Bol. Phys., vol. 64, no. 4, pp [16] El Naqa, I, Suneja, G, Lndsay, P, Hope, A, Alaly, J, Vcc, M, Bradley, J, Apte, A & Deasy, J 006, 'Dose response explorer: an ntegrated open-source tool for explorng and modellng radotherapy dose-volume outcome relatonshps', Physcs n Medcne and Bology, vol. 51, no., pp [17] El Naqa, I, Bradley, J & Deasy, J 008, 'Nonlnear kernel-based approaches for predctng normal tssue toxctes', n Proc. of 7th Int. Conference on Machne Learnng and Applcatons, pp J Radat Oncol Inform 009; 1:1:30-43

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