The differentiation of malignant and benign human breast tissue at surgical margins and biopsy using x-ray interaction data and Bayesian classification

Abstract

Worldwide, about 1.3 million women are diagnosed with breast cancer annually with an estimated 465,000 deaths. Accordingly, there is a need for high accuracy and speed in diagnosis of lesions suspected of being cancerous. This study assesses the interaction data collected from low energy x-rays within breast tissue samples. Trace element concentrations are assessed using x-ray fluorescence, as well as electron density, and molecular structure which are examined using incoherent and coherent scatter, respectively. Our work to date has shown that such data can provide a quantitative measure of certain tissue characterising parameters and hence, through appropriate modelling, could be used to classify samples for uses such as surgical margin detection and biopsy examination. The parameters used in this study for comparing the normal and tumour tissue sample populations are: levels of elements Ca, Cu, Fe, Br, Zn, Rb, K; the area, FWHM and amplitude from peaks fitted to the coherent scatter profile that are associated with fat, fibre and water content; the ratio of the Compton and coherent scatter peak area, FWHM and amplitude from the incoherent scatter profile. The novelty of the approach to this work lies in the fact that the classification process does not rely on one source of data but combines several measurements, the data from which in this application are modelled using a method based on Bayesian classification. The reliability of the classifications was assessed by its application to diagnostically known data that was not itself included in the thresholds determination. The results of the classification of over 70 breast tissue samples will be presented in this study. Bayesian modelling was carried out using selected significant parameters for classification resulting in 71% of normal tissue samples (n=35) and 66% of tumour tissue samples (n=35) being correctly classified when using all the samples. Bayesian classification using the same variables on all normal samples (n=35) and tumour samples with ≥50% tumour content (n=16), resulted in 89% of normal tissue samples and, 76% tumour tissue samples being correctly classified.