Pattern recognition of 31P magnetic resonance spectroscopy tumour spectra obtained in vivo.
Pattern recognition has been applied to the analysis of in vivo 31P NMR spectra. Using four different classes of tumour and three types of normal tissue, cluster analysis and artificial neural networks were successful in separating and classifying the majority of samples analysed. Although the phosphomonoester and P(i) regions appeared to be the most important spectral features, data representing the entire 31P spectrum were required for best separation of the tumour and tissue classes.
Howells
S L
SL
Division of Biochemistry, St George's Hospital Medical School, London, UK.
Maxwell
R J
RJ
Howe
F A
FA
Peet
A C
AC
Stubbs
M
M
Rodrigues
L M
LM
Robinson
S P
SP
Baluch
S
S
Griffiths
J R
JR
D000818:Animals; D001921:Brain / Q000378:metabolism; D005260:Female; D005354:Fibrosarcoma / Q000175:diagnosis / Q000378:metabolism / Q000473:pathology; D008099:Liver / Q000378:metabolism; D009682:Magnetic Resonance Spectroscopy / Q000379:methods; D051379:Mice; D008809:Mice, Inbred C3H; D009132:Muscles / Q000378:metabolism; D009368:Neoplasm Transplantation; D009374:Neoplasms, Experimental / Q000175:diagnosis* / Q000378:metabolism / Q000473:pathology; D010363:Pattern Recognition, Automated; D010758:Phosphorus; D051381:Rats; D011915:Rats, Inbred BUF; D017208:Rats, Wistar; D012189:Retrospective Studies
D010758:Phosphorus