Artif Intell Med - Identifying malignant transformations in recurrent low grade gliomas using high resolution magic angle spinning spectroscopy.


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JECTIVE: The objective of this study was to determine whether metabolic parameters derived from ex vivo analysis of tissue samples are predictive of biologic characteristics of recurrent low grade gliomas (LGGs). This was achieved by exploring the use of multivariate pattern recognition methods to generate statistical models of the metabolic characteristics of recurrent LGGs that correlate with aggressive biology and poor clinical outcome.METHODS: Statistical models were constructed to distinguish between patients with recurrent gliomas that had undergone malignant transformation to a higher grade and those that remained grade 2. The pattern recognition methods explored in this paper include three filter-based feature selection methods (chi-square, gain ratio, and two-way conditional probability), a genetic search wrapper-based feature subset selection algorithm, and five classification algorithms (linear discriminant analysis, logistic regression, functional trees, support vector machines, and decision stump logit boost). The accuracy of each pattern recognition framework was evaluated using leave-one-out cross-validation and bootstrapping.MATERIALS: The population studied included fifty-three patients with recurrent grade 2 gliomas. Among these patients, seven had tumors that transformed to grade 4, twenty-four had tumors that transformed to grade 3, and twenty-two had tumors that remained grade 2. Image-guided tissue samples were obtained from these patients using surgical navigation software. Part of each tissue sample was examined by a pathologist for histological features and for consistency with the tumor grade diagnosis. The other part of the tissue sample was analyzed with ex vivo nuclear magnetic resonance (NMR) spectroscopy.RESULTS: Distinguishing between recurrent low grade gliomas that transformed to a higher grade and those that remained grade 2 was achieved with 96% accuracy, using areas of the ex vivo NMR spectrum corresponding to myoinositol, 2-hydroxyglutarate, hypo-taurine, choline, glycerophosphocholine, phosphocholine, glutathione, and lipid. Logistic regression and decision stump boosting models were able to distinguish between recurrent gliomas that transformed to a higher grade and those that did not with 100% training accuracy (95% confidence interval [93-100%]), 96% leave-one-out cross-validation accuracy (95% confidence interval [87-100%]), and 96% bootstrapping accuracy (95% confidence interval [95-97%]). Linear discriminant analysis, functional trees, and support vector machines were able to achieve leave-one-out cross-validation accuracy above 90% and bootstrapping accuracy above 85%. The three feature ranking methods were comparable in performance.CONCLUSIONS: This study demonstrates the feasibility of using quantitative pattern recognition methods for the analysis of metabolic data from brain tissue obtained during the surgical resection of gliomas. All pattern recognition techniques provided good diagnostic accuracies, though logistic regression and decision stump boosting slightly outperform the other classifiers. These methods identified biomarkers that can be used to detect malignant transformations in individual low grade gliomas, and can lead to a timely change in treatment for each patient.

Resumo Limpo

jectiv object studi determin whether metabol paramet deriv ex vivo analysi tissu sampl predict biolog characterist recurr low grade glioma lggs achiev explor use multivari pattern recognit method generat statist model metabol characterist recurr lggs correl aggress biolog poor clinic outcomemethod statist model construct distinguish patient recurr glioma undergon malign transform higher grade remain grade pattern recognit method explor paper includ three filterbas featur select method chisquar gain ratio twoway condit probabl genet search wrapperbas featur subset select algorithm five classif algorithm linear discrimin analysi logist regress function tree support vector machin decis stump logit boost accuraci pattern recognit framework evalu use leaveoneout crossvalid bootstrappingmateri popul studi includ fiftythre patient recurr grade glioma among patient seven tumor transform grade twentyfour tumor transform grade twentytwo tumor remain grade imageguid tissu sampl obtain patient use surgic navig softwar part tissu sampl examin pathologist histolog featur consist tumor grade diagnosi part tissu sampl analyz ex vivo nuclear magnet reson nmr spectroscopyresult distinguish recurr low grade glioma transform higher grade remain grade achiev accuraci use area ex vivo nmr spectrum correspond myoinositol hydroxyglutar hypotaurin cholin glycerophosphocholin phosphocholin glutathion lipid logist regress decis stump boost model abl distinguish recurr glioma transform higher grade train accuraci confid interv leaveoneout crossvalid accuraci confid interv bootstrap accuraci confid interv linear discrimin analysi function tree support vector machin abl achiev leaveoneout crossvalid accuraci bootstrap accuraci three featur rank method compar performanceconclus studi demonstr feasibl use quantit pattern recognit method analysi metabol data brain tissu obtain surgic resect glioma pattern recognit techniqu provid good diagnost accuraci though logist regress decis stump boost slight outperform classifi method identifi biomark can use detect malign transform individu low grade glioma can lead time chang treatment patient

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