New Statistical Algorithms for the Analysis of Mass - FU Berlin, FB MI ...

94 CHAPTER 4. (BIO-)MEDICAL APPLICATIONS
Figure 4.4.11: Classification of the training set and test sets in 10-fold-stratified
cross-validation for marker-positive seminoma patients vs. healthy controls (CM10
ProteinChip � )
Figure 4.4.12: Classification of the training set and test sets in 10-fold-stratified
cross-validation for marker-negative seminoma patients vs healthy controls, (CM10
ProteinChip � )
Assessment of beta-HCG-positive seminoma vs. control subjects yielded
comparable but less significant results. Due to the lack of peaks with an
adequate height difference, only three peaks were selected for analysis (CM10
ProteinChip � ). The highest number of significant peaks were found in a mass
range of 3800-8000 Da. Marker-positive seminomas were correctly predicted in
67.6% (decision trees, 95% confidence interval of 50.2%-81.9%), 78.4% (neural
networks, 95% confidence interval of 61.8%-90.2%), and 75.8% of the cases
(support vector machines, 95% confidence interval of 58.8%-88.2%). The
three analytical methods had adequate sensitivities ranging between 76.5% and
88.2%. Their specificities were likewise adequate at 60% (decision trees), 75%
(neural networks), and 65% (support vector machines), respectively. Three
different peaks (3.80 kDa; 4.98 kDa; 7.97 kDa) were chosen to create a fingerprint.
Figure 4.4.11 displays the corresponding sensitivities and specificities. Decision
trees achieved 80% sensitivity and 70% specificity in discriminating
between beta-HCG-negative seminoma and control subjects.
Figure 4.4.12 shows decision trees, neural networks, and support vector
machine analyses. The rates of correctly classified subjects in all groups are
71.1%-75.7% (CM10 ProteinChip � , 95% confidence interval of 60.7%-87.1%
for decision trees; 95% confidence interval of 60.7%-87.1% for neural networks;
95% confidence interval of 55.7%-83.6% for support vector machines; molecular
masses: 6.94 kDa; 7.76 kDa; 8.63 kDa; 8.69 kDa). The CM10 ProteinChip
� achieved excellent differentiation between seminoma and control subjects,
while the IMAC-Cu+ chip yielded significantly less precise results. The combination
of 6 peaks (4.19 kDa, 6.37 kDa, 7.76 kDa, 7.93 kDa, 7.97 kDa and
9.29 kDa) predicted cancer patients in 60.6%-76.6% (decision trees, neural
networks and support vector machines) with a sensitivity of 27.6% to 76.6%
and a specificity of 76.6% to 93.6% (Figure 4.4.13) in the 10-fold stratified
cross-validation (95% confidence interval of 67.9%-85.6%, decision trees; 95%
confidence interval of 64.4% -74.4%, neural networks; 95% confidence interval
of 50.0% - 60.4%, support vector machines).