  A method has been developed for the analysis of images of sentinel lymph
nodes generated by a spectral scanning device. The aim is to classify the
nodes, excised during surgery for breast cancer, as normal or metastatic. The
data from one node constitute spectra at 86 wavelengths for each pixel of a
20*20 grid. For the analysis, the spectra are reduced to scores on two factors,
one derived externally from a linear discriminant analysis using spectra taken
manually from known normal and metastatic tissue, and one derived from the node
under investigation to capture variability orthogonal to the external factor.
Then a three-group mixture model (normal, metastatic, non-nodal background)
using multivariate t distributions is fitted to the scores, with external data
being used to specify informative prior distributions for the parameters of the
three distributions. A Markov random field prior imposes smoothness on the
image generated by the model. Finally, the node is classified as metastatic if
any one pixel in this smoothed image is classified as metastatic. The model
parameters were tuned on a training set of nodes, and then the tuned model was
tested on a separate validation set of nodes, achieving satisfactory
sensitivity and specificity. The aim in developing the analysis was to allow
flexibility in the way each node is modelled whilst still using external
information. The Bayesian framework employed is ideal for this.
