title: Diagnostic assessment of brain tumours and non-neoplastic brain disorders in vivo using proton nuclear magnetic resonance spectroscopy and artificial neural networks.
abstract: OBJECTIVE
  Experiments were carried out to assess the potential of artificial neural network (ANN) analysis in the differential diagnosis of brain tumours (low- and high-grade gliomas) from non-neoplastic focal brain lesions (tuberculomas and abscesses), using proton magnetic resonance spectroscopy (1H MRS) as input data.
  
  
  METHODS
  Single-voxel stimulated echo acquisition mode (STEAM) (echo time of 20 ms) spectra were acquired from 138 subjects including 15 with low-grade gliomas, 47 with high-grade gliomas, 18 with tuberculomas, 18 with abscesses and 40 healthy controls. Two neural networks were constructed using the spectral points from 0.6 to 3.4 parts per million. In the first network construction, the ANN had to differentiate between tumours from infections, while the second network had to differentiate between all five histological classes.
  
  
  RESULTS
  ANN analysis gave a histologically correct diagnosis for low- and high-grade gliomas with an accuracy of 73% and 98% respectively. None of the 62 tumours was diagnosed as an infectious lesion. Among the non-neoplastic lesions, ANN classification was correct in 89% of tuberculomas and in 83% of brain abscesses. The specificity of ANN diagnosis was 98%, 92%, 99%, and 100% for low-grade gliomas, high-grade gliomas, tuberculomas and abscesses respectively.
  
  
  CONCLUSIONS
  The present data show the clinical utility of non-invasive 1H MRS by automated ANN analysis in the diagnosis of tumour and non-tumour cerebral disorders.
authors.affiliation: NMR Research Group, A.I. Virtanen Institute, University of Kuopio, Finland.
authors.forename: H
authors.initials: H
authors.lastname: Poptani
authors.forename: J
authors.initials: J
authors.lastname: Kaartinen
authors.forename: R K
authors.initials: RK
authors.lastname: Gupta
authors.forename: M
authors.initials: M
authors.lastname: Niemitz
authors.forename: Y
authors.initials: Y
authors.lastname: Hiltunen
authors.forename: R A
authors.initials: RA
authors.lastname: Kauppinen
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D001922:Brain Abscess / Q000175:diagnosis* / Q000378:metabolism; D001932:Brain Neoplasms / Q000175:diagnosis* / Q000378:metabolism; D016022:Case-Control Studies; D002648:Child; D003937:Diagnosis, Differential; D005069:Evaluation Studies as Topic; D005910:Glioma / Q000175:diagnosis* / Q000378:metabolism; D006801:Humans; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D008875:Middle Aged; D016571:Neural Networks, Computer; D011522:Protons; D016862:Tuberculoma, Intracranial / Q000175:diagnosis* / Q000378:metabolism
chemical_list: D011522:Protons