D e c is io n b o u n d a rie s ir
F ig u r e 13.5 C u rre n t a p p ro a ch e s in c la s sify in g MR S p e ctra are m a in ly s u p e rv ise d . T h e cla s s ifie rs are train ed to p re d ic t th e patholog y.
In all cases, histology, the ’gold standard’ for clinical diagnosis, was used as the target in training the classifiers. Although it may be accepted that supervised classifiers are easier to implement and are, arguably, more accurate, forcing the spectra from the largely heterogeneous tissue to comply with the rigid and predetermined classes may in reality be less informative. Metabolic profiles are continuous, unlike the mechanism by which tumours are classified in histopathology (chapter 1 and section 13.3.4). In addition, spectra from necrotic parts of a glioma, for example, are different from those of the viable regions of the same tumours (Rutter et ai, 1995) such that they may provide different information about the tumours (e.g. whether the tumours are growing or quiescent), while their histology remains the same. The degree of variation of these spectra is uncertain. In a supervised classifier, such differences would be obscured as the classifier is
C hapter 13: Discussion 156
constrained to mimic the targets (usually the grades -PreuI et al, 1996). By forcing the spectra to follow the histological diagnosis, the classifier cannot make use of the added information from MRS, diminishing its potential clinical role.
An alternative approach is to use unsupervised techniques (Fig. 13.6). Unsupervised classifiers group the data, without prior knowledge, on the basis of their inherent structures (Duda and Hart, 1973). Since histopathology remains the gold standard for tissue classification, it might be more beneficial to allow the spectra to reveal their inherent properties.ürst. before checking them against pathology. By using the pathology information subsequent to the unsupervised classification (Fig. 13.6), MRS information can complement the pathology and not just be interpreted by it.
M R S U n s u p e r v i s e d p r o j e c t i o n p a t h o l o g y D e c i s i o n b o u n d a r i e s
F ig u r e 1 3 .6 U n s u p e rvise d c la s sific a tio n . T h e in fo rm a tio n fro m p a th o lo g y is use d to ve rify an d in te rp re t th e c la s s ific a tio n . C la s s ific a tio n b o u n d a rie s can the n be g e n e ra te d , e.g. by le a rn in g v e c to r q u a n tis a tio n as s u g g e s te d by K oh on en (1 9 9 0 ).
The case for unsupervised learning becomes more obvious in the case of CSI, where diagnosis may be available for only one of the many spectra obtained, or may not available at all. Recently, initial results showing the advantage of using unsupervised techniques have been demonstrated (El-Deredy et al, 1997). The results revealed that the lipid profile of high grade gliomas has two distinctive clusters, which appears to be consistent with biochemical analysis (Arus, 1997)"^. On the other hand, Kaartinen et al (1998) have recently demonstrated the possibility of classifying blood plasma lipoproteins using self-organising maps.
13.4.4 Fuzzy and Neuro-fuzzy techniques
If, in spite of the difficulties outlined in section 13.3.4 and 13.4.3, supervised classification is still desirable, fuzzy logic may offer a mechanism that copes with the uncertainty about the tissue type. Fuzzy logic (Zadeh, 1997) is a superset of conventional Boolean logic that has been extended to handle the concept of partial truth (values between "completely true" and "completely false"), while neuro-fuzzy techniques (Bossley, 1997) enable combining data driven models with qualitative information from experts. As emphasised in the previous section, changes between normal and abnormal characteristics of tumours are not discrete valued but lie on a continuum and are often overlapping. The use of fuzzy
logic may prove helpful for biomedical MRS because it constructs decision boundaries that are more flexible, and may improve the accuracy of classifying marginal cases close to the decision boundaries.
13.4.5 Continuous learning
Continuous learning (Saad and Rattray, 1997) is an approach by which the pattern recognition system, while being routinely used for classification or prediction, can use newly arriving data to update its parameters in order to improve its performance continually without having to start the training afresh. W ith the help of confidence measures which assess the gaps in performance and suggest what data needs to be collected, pattern recognition systems using continuous learning will effectively improve by ’experience’ and, in turn, will increase the confidence in their results.
13.4.6 Improving data quality and signai-to-noise ratio
The ability of the pattern recognition system to use tools that can reduce the data or remove noise should not, however, undermine the importance of collecting as much ’good’ data as practically possible. The use of data sets that are too small creates ill- posed problems and will yield applications which, though seeming to operate well on the limited data available, will not be reliable or robust in the long term. Ill-posed problems could be constrained using régularisation techniques which prevent over-fitting. Other techniques like Bayesian methods do not, in theory, require test data and hence could utilise all the available data for training.
In section 3,4,7 we demonstrated the use of wavelet transforms to improve of signal-to- noise ratio (section 3.4.7). Because they operate locally, wavelets have the remarkable advantage over conventional filtering tools in that they can remove the noise without losing information by excessive smoothing. This implies that noise in MRS data could be filtered out without line broadening (as shown in Fig. 3.12). The local operation property may also be used for the selective editing of metabolites without effecting the rest of the spectrum. It may also be possible to design wavelet filters that model and correct base line distortion without losing the broad peaks from macromolecules.
13.4.7 Rejection capabilities
Ideally, any pattern recognition system should be equipped with a mechanism to reject bad data. In the majority of applications reported in chapter 3, the data were classified according to their true categories (mostly using information from pathology). However, this restricts the system, when given a new datum or pattern, to making a prediction based on those categories only. A situation might arise where the new datum is either of particularly bad quality or does not belong to any of the redefined categories. The system should therefore have the ability to reject such a datum completely, rather than just classifying it with low confidence. In chapter 11 we used confidence distance to eliminate spectra which the neural network was not confident enough to assign to any of the predefined classes.
Chapter 13: Discussion 158
13.5 Conclusion
This thesis has provided an example of the power of neuro-computing techniques as computational tools for detecting subtle or implicit relationships among data with little or no requirement for prior knowledge of the nature of these relationships. Unlike other methods of interpreting MRS obtained from cancer patients, which have focused on finding tumour diagnostic and prognostic factors in the spectra, neuro-computing techniques as used in this thesis require no such factors but observe the spectra overall as patterns and extract their own discriminating factors through the training process. W e have reviewed and presented statistical and neural pattern recognition techniques as effective and accurate tools for such analysis. New algorithms have been developed to speed up the training process, avoid sub-optimal minima in neuro-computing, and identify automatically the important metabolites in the spectra which would otherwise be difficult to detect due to noise. W e have also demonstrated that neuro-computing algorithms using autom atic feature selection of spectral components can infer relationships between the metabolic profile and other cell properties such as the response to cytotoxic drugs. These results suggest that MRS can act as a pre-treatment predictor of drug response, thereby defining a potential role for this type of analysis to operate in a clinical setting and to facilitate our understanding of the metabolic pathways involved in the response of tumours to drugs.
In conclusion, we have argued that the realisation of the practical clinical potential of MRS relies, in part, on understanding the role played by the constituent metabolites in the characterisation of and the differentiation between tissue types and in relating metabolism to other tissue properties such as response to drugs. It also relies on robust and accurate tools to analyse the complex and noisy spectra. Pattern recognition techniques, particularly those of neuro-computing, could play an important role in achieving these goals.
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There are no magic answers, no miraculous methods to overcome the problems we face, just the fàmiliar ones: honest search for understanding, education, organization, action ... and the kind of commitment that will persist despite the temptations of disillusionment, despite many failures and only limited successes, inspired by the hope of a brighter future.