Comparison of the chromosomal pattern of primary testicular nonseminomas and residual mature teratomas after chemotherapy

About 70 to 75% of patients with nonseminomatous testicular germ cell tumors (NSs) present with metastases. When these metastases are treated with chemotherapy, often residual mature teratoma (RMT) is left. RMT is composed of fully differentiated somatic tissue. Untreated metastases of NSs rarely consist exclusively of mature somatic tissue. Apparently, after chemotherapy treatment there is a shift towards higher degrees of differentiation. Investigating tumor progression and the mechanism(s) involved in therapy-related differentiation, we compared the cytogenetically abnormal karyotypes of a series of 70 NSs with those of 31 RMTs. In NSs and RMTs, the modal total chromosome number does not differ and is in the triploid range. Both the frequency and the average copy number of i(12p) are the same, and the pattern of chromosomal over-and underrepresentation and distribution of breakpoints do not differ significantly in these series. So, we found the chromosomal pattern of RMTs as abnormal as those of primary NSs. Based on cytogenetics, we found no indication that specific chromosomal alterations parallel metastasis and therapy-related differentiation of the metastases. The cytogenetic data suggest that both induction of differentiation of (selected) cells or selection of cells with capacity to differentiate are possible mechanisms for the therapy-related differentiation of RMTs. (C) Elsevier Science Inc., 1997

An application of generalized matrix learning vector quantization in neuroimaging

Background and objective: Neurodegenerative diseases like Parkinson’s disease often take several years before they can be diagnosed reliably based on clinical grounds. Imaging techniques such as MRI are used to detect anatomical (structural) pathological changes. However, these kinds of changes are usually seen only late in the development. The measurement of functional brain activity by means of [18F]fluorodeoxyglucose positron emission tomography (FDG-PET) can provide useful information, but its interpretation is more difficult. The scaled sub-profile model principal component analysis (SSM/PCA) was shown to provide more useful information than other statistical techniques. Our objective is to improve the performance further by combining SSM/PCA and prototype-based generalized matrix learning vector quantization (GMLVQ). Methods: We apply a combination of SSM/PCA and GMLVQ as a classifier. In order to demonstrate the combination’s validity, we analyze FDG-PET data of Parkinson’s disease (PD) patients collected at three different neuroimaging centers in Europe. We determine the diagnostic performance by performing a ten times repeated ten fold cross validation. Additionally, discriminant visualizations of the data are included. The prototypes and relevance of GMLVQ are transformed back to the original voxel space by exploiting the linearity of SSM/PCA. The resulting prototypes and relevance profiles have then been assessed by three neurologists. Results: One important finding is that discriminative visualization can help to identify disease-related properties as well as differences which are due to center-specific factors. Secondly, the neurologist assessed the interpretability of the method and confirmed that prototypes are similar to known activity profiles of PD patients. Conclusion: We have shown that the presented combination of SSM/PCA and GMLVQ can provide useful means to assess and better understand characteristic differences in FDG-PET data from PD patients and HCs. Based on the assessments by medical experts and the results of our computational analysis we conclude that the first steps towards a diagnostic support system have been taken successfully

An application of generalized matrix learning vector quantization in neuroimaging

Background and objective: Neurodegenerative diseases like Parkinson’s disease often take several years before they can be diagnosed reliably based on clinical grounds. Imaging techniques such as MRI are used to detect anatomical (structural) pathological changes. However, these kinds of changes are usually seen only late in the development. The measurement of functional brain activity by means of [18F]fluorodeoxyglucose positron emission tomography (FDG-PET) can provide useful information, but its interpretation is more difficult. The scaled sub-profile model principal component analysis (SSM/PCA) was shown to provide more useful information than other statistical techniques. Our objective is to improve the performance further by combining SSM/PCA and prototype-based generalized matrix learning vector quantization (GMLVQ). Methods: We apply a combination of SSM/PCA and GMLVQ as a classifier. In order to demonstrate the combination’s validity, we analyze FDG-PET data of Parkinson’s disease (PD) patients collected at three different neuroimaging centers in Europe. We determine the diagnostic performance by performing a ten times repeated ten fold cross validation. Additionally, discriminant visualizations of the data are included. The prototypes and relevance of GMLVQ are transformed back to the original voxel space by exploiting the linearity of SSM/PCA. The resulting prototypes and relevance profiles have then been assessed by three neurologists. Results: One important finding is that discriminative visualization can help to identify disease-related properties as well as differences which are due to center-specific factors. Secondly, the neurologist assessed the interpretability of the method and confirmed that prototypes are similar to known activity profiles of PD patients. Conclusion: We have shown that the presented combination of SSM/PCA and GMLVQ can provide useful means to assess and better understand characteristic differences in FDG-PET data from PD patients and HCs. Based on the assessments by medical experts and the results of our computational analysis we conclude that the first steps towards a diagnostic support system have been taken successfully