Abstract:
Early cancer detection drastically improves the chances of cure and therefore methods
are required, which allow early detection and screening in a fast, reliable and
inexpensive manner. A prospective method, featuring all these characteristics, is
vibrational spectroscopy. In order to take the next step towards the development of
this technology into a clinical diagnostic tool, classification and imaging methods for
an automated diagnosis based on spectral data are required.
For this study, Raman spectra, derived from axillary lymph node tissue from breast
cancer patients, were used to develop a diagnostic model. For this purpose different
classification methods were investigated. A support vector machine (SVM) proved to
be the best choice of classification method since it classified 100% of the unseen test
set correctly. The resulting diagnostic models were thoroughly tested for their
robustness to the spectral corruptions that would be expected to occur during routine
clinical analysis. It showed that sufficient robustness is provided for a future
diagnostic routine application.
SVMs demonstrated to be a powerful classifier for Raman data and due to that they
were also investigated for infrared spectroscopic data. Since it was found that a single
SVM was not capable of reliably predicting breast cancer pathology based on tissue
calcifications measured by infrared micro-spectroscopy a SVM ensemble system was
implemented. The resulting multi-class SVM ensemble predicted the pathology of the
unseen test set with an accuracy of 88.9%, in comparison a single SVM assessed with
the same unseen test set achieved 66.7% accuracy. In addition, the ensemble system
was extended for analysing complete infrared maps obtained from breast tissue
specimens. The resulting imaging method successfully detected and staged
calcification in infrared maps. Furthermore, this imaging approach revealed new
insights into the calcification process in malignant development, which was not
previously well understood.
