Automated classification of human brain tumours by neural network analysis using in vivo 1H magnetic resonance spectroscopic metabolite phenotypes

Abstract

We present a novel method to integrate in vivo nuclear magnetic resonance spectroscopy (MRS) information into the clinical diagnosis of brain tumours. Water-suppressed 1H MRS data were collected from 33 patients with brain tumours and 28 healthy controls in vivo. The data were treated in the time domain for removal of residual water and a region from the frequency domain (from 3.4 to 0.3 p.p.m.) together with the unsuppressed water signal were used as inputs for artificial neural network (ANN) analysis. The ANN distinguished tumour and normal tissue in each case and was able to classify benign and malignant gliomas as well as other brain tumours to match histology in a clinically useful manner with an accuracy of 82%. Thus the present data indicate existence of tumour tissue-specific metabolite phenotypes that can be detected by in vivo 1H MRS. We believe that a user-independent ANN analysis may provide an alternative method for tumour classification in clinical practice.