title: Classification of tumour 1H NMR spectra by pattern recognition.
abstract: 1H spectra of tumours or normal tissues, which include signals from all hydrogen-containing metabolites, are too complex for the human eye to interpret. We have studied 58 1H spectra from perchloric acid extracts of three normal tissues (liver, kidney and spleen) and five rat tumours (GH3 pituitary, fibrosarcoma, Morris Hepatomas 7777 and 9618a and Walker carcinosarcoma). Instead of editing them or quantifying individual metabolites, we have used statistical pattern recognition techniques to classify them into groups. This automatic, objective method differentiated spectra from normal and malignant rat tissue biopsies, and from different types of cancer. It seems likely that this technique can be applied to human tissues and thus used for cancer diagnosis.
authors.affiliation: CRC Biomedical Magnetic Resonance Research Group, St George's Hospital Medical School, London, UK.
authors.forename: S L
authors.initials: SL
authors.lastname: Howells
authors.forename: R J
authors.initials: RJ
authors.lastname: Maxwell
authors.forename: J R
authors.initials: JR
authors.lastname: Griffiths
mesh_terms: D000818:Animals; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D005615:Freezing; D006863:Hydrogen-Ion Concentration; D007773:Lactates / Q000032:analysis; D019344:Lactic Acid; D009682:Magnetic Resonance Spectroscopy; D009374:Neoplasms, Experimental / Q000175:diagnosis*; D010363:Pattern Recognition, Automated; D011522:Protons; D051381:Rats
chemical_list: D007773:Lactates; D011522:Protons; D019344:Lactic Acid