Automated classification of short echo time in in vivo 1H brain tumor spectra: a multicenter study.
Automated pattern recognition techniques are needed to help radiologists categorize MRS data of brain tumors according to histological type and grade. A major question is whether a computer program "trained" on spectra from one hospital will be able to classify those from another, particularly if the acquisition protocol is different. A subset of 144 histopathologically validated brain tumor spectra in the INTERPRET database, obtained from three of the collaborating centers, was grouped into meningiomas, low-grade astrocytomas, and "aggressive tumors" (glioblastomas and metastases). Spectra from two centers formed the training set (94 spectra) while the third acted as the test set (50 spectra). Linear discriminant analysis successfully classified 48/50 in the test set; the remaining two were atypical cases. When the training and test sets were combined, 133 of the 144 spectra were correctly classified using the leave-one-out procedure. These spectra had been obtained using different sequences (STEAM and PRESS), different echo times (20, 30, 31, and 32 ms), different repetition times (1600 and 2000 ms), and different manufacturers' instruments (GE and Philips). Pattern recognition algorithms are less sensitive to acquisition parameters than had been expected.
Tate
A Rosemary
AR
CRC Biomedical MR Research Group, St George's Hospital Medical School, University of London, London, UK. rosemary@cogs.susx.ac.uk
Majós
Carles
C
Moreno
Angel
A
Howe
Franklyn A
FA
Griffiths
John R
JR
Arús
Carles
C
D001254:Astrocytoma / Q000175:diagnosis; D001923:Brain Chemistry; D001932:Brain Neoplasms / Q000737:chemistry / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D009682:Magnetic Resonance Spectroscopy; D008577:Meningeal Neoplasms / Q000175:diagnosis; D008579:Meningioma / Q000175:diagnosis; D010363:Pattern Recognition, Automated