Abstract
The paper reports results of an in vitro study on autofluorescence spectroscopy of fresh and formalin-fixed human breast tissue samples to investigate the effect of formalin fixation on the measured autofluorescence spectra. It also explores the applicability of the approach in discriminating cancerous from the uninvolved sites of the formalin-fixed breast tissues based on their autofluorescence spectra. A probability-based diagnostic algorithm, making use of the theory of relevance vector machine (RVM), a powerful recent approach for statistical pattern recognition, was developed for that purpose. The algorithm provided sensitivity values of up to 97% and specificity values of up to 100% towards cancer for both the independent validation data set as well as for the training data set based on leave-one-out cross-validation. These results suggest that autofluorescence spectroscopy may prove to be a valuable additional in vitro diagnostic modality in clinical pathology setting for discriminating cancerous tissue sites from normal sites.
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