NDLI: Gliomas classification by multivariate analysis of in vivo MRI/MRSI data based on recursive partitioning tree and discriminant analysis

Content Provider	IEEE Xplore Digital Library
Author	Xiaojuan Li Ying Lu Nelson, S.J.
Copyright Year	2002
Description	Author affiliation: Dept. of Radiol., California Univ., San Francisco, CA, USA (Xiaojuan Li; Ying Lu; Nelson, S.J.)
Abstract	Accurate diagnosis is critical for the treatment planning of brain tumors. At present, classification of tumor is based on histological examination of tissue samples. This is invasive and may be subject to sampling errors. Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive technique that provides functional information and has been proposed as a tool for non-invasive tumor grading. The goal of this work is to find a classification method that (1) explicitly combines information from MRSI and MR imaging (MRI); (2) considers the MRSI characteristics of the entire lesion instead of a pre-selected region from within the anatomic lesion. Forty-nine newly-diagnosed glioma patients were studied with multivariate analysis based on recursive partitioning analysis (RPA) and linear discriminant analysis (LDA). The cross-validation classification error was 5 out of 49 patients. This suggested that characterizing the lesion by integrating the MRI/MRSI properties has the potential for improving the diagnosis and management of brain tumors.
Sponsorship	IEEE Signal Processing Soc
Starting Page	209
Ending Page	212
File Size	290695
Page Count	4
File Format	PDF
ISBN	078037584X
DOI	10.1109/ISBI.2002.1029230
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-07-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Classification tree analysis In vivo Magnetic resonance imaging Neoplasms Lesions Magnetic analysis Linear discriminant analysis Sampling methods Magnetic resonance Spectroscopy
Content Type	Text
Resource Type	Article

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