PMID- 15324770
OWN - NLM
STAT- MEDLINE
DCOM- 20041021
LR  - 20061115
IS  - 1090-7807 (Print)
IS  - 1090-7807 (Linking)
VI  - 170
IP  - 1
DP  - 2004 Sep
TI  - Classification of brain tumours using short echo time 1H MR spectra.
PG  - 164-75
AB  - The purpose was to objectively compare the application of several techniques and 
      the use of several input features for brain tumour classification using Magnetic 
      Resonance Spectroscopy (MRS). Short echo time 1H MRS signals from patients with 
      glioblastomas (n = 87), meningiomas (n = 57), metastases (n = 39), and 
      astrocytomas grade II (n = 22) were provided by six centres in the European Union 
      funded INTERPRET project. Linear discriminant analysis, least squares support 
      vector machines (LS-SVM) with a linear kernel and LS-SVM with radial basis 
      function kernel were applied and evaluated over 100 stratified random splittings 
      of the dataset into training and test sets. The area under the receiver operating 
      characteristic curve (AUC) was used to measure the performance of binary 
      classifiers, while the percentage of correct classifications was used to evaluate 
      the multiclass classifiers. The influence of several factors on the 
      classification performance has been tested: L2- vs. water normalization, 
      magnitude vs. real spectra and baseline correction. The effect of input feature 
      reduction was also investigated by using only the selected frequency regions 
      containing the most discriminatory information, and peak integrated values. Using 
      L2-normalized complete spectra the automated binary classifiers reached a mean 
      test AUC of more than 0.95, except for glioblastomas vs. metastases. Similar 
      results were obtained for all classification techniques and input features except 
      for water normalized spectra, where classification performance was lower. This 
      indicates that data acquisition and processing can be simplified for 
      classification purposes, excluding the need for separate water signal 
      acquisition, baseline correction or phasing.
FAU - Devos, A
AU  - Devos A
AD  - SCD-SISTA, Department of Electrical Engineering, Katholieke Universiteit Leuven, 
      Kasteelpark Arenberg 10, 3001 Heverlee (Leuven), Belgium. 
      adevos@esat.kuleuven.ac.be
FAU - Lukas, L
AU  - Lukas L
FAU - Suykens, J A K
AU  - Suykens JA
FAU - Vanhamme, L
AU  - Vanhamme L
FAU - Tate, A R
AU  - Tate AR
FAU - Howe, F A
AU  - Howe FA
FAU - Majos, C
AU  - Majos C
FAU - Moreno-Torres, A
AU  - Moreno-Torres A
FAU - van der Graaf, M
AU  - van der Graaf M
FAU - Arus, C
AU  - Arus C
FAU - Van Huffel, S
AU  - Van Huffel S
LA  - eng
PT  - Comparative Study
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PL  - United States
TA  - J Magn Reson
JT  - Journal of magnetic resonance (San Diego, Calif. : 1997)
JID - 9707935
SB  - IM
MH  - Brain Chemistry
MH  - Brain Neoplasms/chemistry/*diagnosis
MH  - Diagnosis, Computer-Assisted
MH  - Discriminant Analysis
MH  - Humans
MH  - Magnetic Resonance Spectroscopy/*methods
MH  - *Pattern Recognition, Automated
EDAT- 2004/08/25 05:00
MHDA- 2004/10/22 09:00
CRDT- 2004/08/25 05:00
PHST- 2003/12/01 00:00 [received]
PHST- 2004/05/26 00:00 [revised]
PHST- 2004/08/25 05:00 [pubmed]
PHST- 2004/10/22 09:00 [medline]
PHST- 2004/08/25 05:00 [entrez]
AID - S1090-7807(04)00181-8 [pii]
AID - 10.1016/j.jmr.2004.06.010 [doi]
PST - ppublish
SO  - J Magn Reson. 2004 Sep;170(1):164-75. doi: 10.1016/j.jmr.2004.06.010.