Diagnosis and prognosis of breast cancer by magnetic resonance spectroscopy of fine-needle aspirates analysed using a statistical classification strategy

Background: The aim was to develop robust classifiers to analyse magnetic r esonance spectroscopy (MIRS) data of fine-needle aspirates taken from breas t tumours. The resulting data could provide computerized, classification-ba sed diagnosis and prognostic indicators. Methods: Fine-needle aspirate biopsies obtained at the time of surgery for both benign and malignant breast diseases were analysed by one-dimensional proton MRS at 8.5 Tesla. Diagnostic correlation was performed between the s pectra and standard pathology reports, including the presence of vascular i nvasion by the primary cancer and involvement of the excised axillary lymph nodes. Results: Malignant tissue was distinguished from benign lesions with an ove rall accuracy of 93 per cent. From the same spectra, lymph node involvement was predicted with an overall accuracy of 95 per cent, and tumour vascular invasion with an overall accuracy of 94 per cent. Conclusion: The pathology, nodal involvement and tumour vascular invasion w ere predicted by computerized statistical classification of the proton MRS spectrum from a fine-needle aspirate biopsy taken from the primary breast l esion.