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Aims: We compared the intracoronary beta-brachytherapy using a liquid rhenium-188 filled balloon with the slow-release, polymer-based, paclitaxel-eluting Taxus-Express stent for treatment of in-stent restenoses., Patients, Methods: During the same study period, patients with restenoses in bare-metal stents were either treated with Taxus-Express stents (n = 50) or beta-brachytherapy after successful angioplasty (n = 51). For brachytherapy 30 Gy in 0.5 mm tissue depth were administered. The irradiated segment exceeded the traumatized segment 7.5 mm on both sides. Primary endpoint was the minimal lumen diameter (MLD) at the target lesion at six months follow-up. Angiographic follow-up was available in 78% (n = 79/101) and clinical follow-up in all patients., Results: Baseline parameters did not differ statistically. The Taxus-Express stent resulted in a significantly larger MLD and a significantly lower percent diameter stenosis post intervention compared to beta-brachytherapy, which both maintained until angiographic follow-up (primary endpoint 2.44 +/- 0.74 mm versus 1.73 +/- 0.74 mm, p < 0.0001). Therefore, Taxus-Express stents were associated with a lower angiographic restenosis rate compared with beta-brachytherapy, both for the target lesion (6.1% versus 17.4%) and the total segment (9.1% versus 23.9%). Moreover, use of Taxus-stent was associated with a clinical benefit based on a significantly lower MACE rate compared with beta-brachytherapy (p < 0.05)., Conclusions: Paclitaxel-eluting Taxus-Express stents resulted in superior clinical and angiographic outcomes compared to intracoronary beta-brachytherapy with a liquid (188)Re filled balloon for treatment of restenosis within a bare-metal stent.

Purpose: To identify novel genomic regions of interest in acute myeloid leukemia (AML) with complex karyotypes, we applied comparative genomic hybridization to microarrays (array-CGH), allowing high-resolution genome-wide screening of genomic imbalances., Patients and Methods: Sixty AML cases with complex karyotypes were analyzed using array-CGH; parallel analysis of gene expression was performed in a subset of cases., Results: Genomic losses were found more frequently than gains. The most frequent losses affected 5q (77%), 17p (55%), and 7q (45%), and the most frequent genomic gains 11q (40%) and 8q (38%). Critical segments could be delineated to genomic fragments of only 0.8 to a few megabase-pairs of DNA. In lost/gained regions, gene expression profiling detected a gene dosage effect with significant lower/higher average gene expression levels across the genes located in the respective regions. Furthermore, high-level DNA amplifications were identified in several regions: 11q23.3-q24.1 (n = 7), 21q22 (n = 6), 11q23.3 (n = 5), 13q12 (n = 3), 8q24 (n = 3), 9p24 (n = 2), 12p13 (n = 2), and 20q11 (n = 2). Parallel analysis of gene expression in critical amplicons displayed overexpressed candidate genes (eg, C8FW and MYC in 8q24)., Conclusion: In conclusion, a large spectrum of genomic imbalances, including novel recurring changes in AML with complex karyotypes, was identified using array-CGH. In addition, the combined analysis of array-CGH data with gene expression profiles allowed the detection of candidate genes involved in the pathogenesis of AML.

Objectives: We studied the value of cardiac magnetic resonance imaging (CMRI) before and after closure of patent foramen ovale (PFO) in patients with cryptogenic ischemic events., Background: Cardiac magnetic resonance imaging is a powerful noninvasive tool for detailed assessment of cardiac anatomy and function. The relevance of CMRI compared with transesophageal echocardiography (TEE) in patients undergoing transcatheter PFO closure has not been evaluated so far., Methods: Contrast-enhanced CMRI and TEE were performed in 75 patients before and after PFO closure. Twelve months after PFO closure, both imaging techniques were repeated in 61 patients with contrast application. To determine provokable atrial right-to-left shunting in CMRI, we applied a contrast-enhanced perfusion imaging technique. Detection of atrial septal aneurysm (ASA) was achieved by means of a high-resolution cine imaging technique., Results: Before PFO closure, ASA was seen with CMRI in 28 of 75 cases (37.3%), compared with 47 of 75 (62.7%) cases using TEE. There were a total of 211 CMRI studies with a corresponding TEE performed in 75 patients. No shunt was present in 107 of 211 studies with both techniques. Contrast-enhanced right-to-left shunting was detected by CMRI in 48 of 72 (66.6%) cases with moderate or severe shunts seen with TEE, but only in 6 of 32 (18.8%) studies with mild shunts with TEE. Anomalous venous returns were excluded in all patients. In two patients, coronary anomalies were seen., Conclusions: The present CMRI technique is inferior to TEE in detection of contrast-enhanced right-to-left shunting and identification of ASA.

The present study examined the association of myocardial perfusion reserve index (MPRI) in cardiovascular magnetic resonance (CMR) with coronary microvascular dysfunction (CMD) and serum levels of markers of inflammation or endothelial activation. Twelve patients with typical angina pectoris without coronary artery disease were enrolled in this study, and CMR perfusion was analyzed using a steady-state-free-precession sequence with 3 short axis slices per heartbeat during first pass of 0.025 mmol Gadolinium-DTPA/kg body weight. The upslope of myocardial signal intensity curves was used to calculate MPRI. CMD was assessed by intracoronary Doppler flow measurement and biplane angiography. Both MPRI and CMD were assessed during endothelium-independent stimulation with intravenous adenosine and during endothelium-dependent stimulation with intracoronary infusion of acetylcholine. Serum values of soluble CD40 ligand (sCD40L), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-alpha), soluble intercellular adhesion molecule-1 (sICAM-1), and C-reactive protein (CRP) were measured. Impaired MPRI correlated significantly with a decrease in coronary blood flow reserve after both endothelium-dependent (p = 0.033) and endothelium-independent (p = 0.022) stimulation. Serum levels above the median of all normal ranged biomarkers sCD40L, TNF-alpha, IL-6, sICAM-1 and CRP were associated with an impaired MPRI for stimulation with adenosine as well as acetylcholine. In multivariable analyses, sCD40L (p < 0.001) and TNF-alpha (p = 0.011) were significantly associated with a decrease in MPRI on adenosine, as were TNF-alpha (p = 0.016) and sICAM-1 (p = 0.022) for a decrease in MPRI on acetylcholine. MPRI on adenosine significantly correlated with MPRI on acetylcholine (p < 0.001). Therefore, the present study demonstrates safety and feasibility of an intracoronary infusion of acetylcholine during CMR perfusion analysis, thus allowing direct assessment of endothelial dependent vasomotor function at the myocardial level by CMR. Furthermore, we show that an impaired myocardial perfusion reserved in CMR is associated with established biomarkers of early atherosclerosis and significantly correlated with CMD. CMR combined with adenosine could be proposed as a non-invasive tool to evaluate CMD.

Purpose: Malignant tumors of the pancreas are frequently indistinguishable from inflammatory tumors arising in the context of a chronic pancreatitis with the use of conventional imaging techniques. Thus, cytologic analysis of cells obtained by abdominal ultrasound, computed tomography, or endoscopic ultrasound-guided fine needle aspiration biopsy is required for diagnosis. However, the reliability of cytologic analyses of pancreatic fine needle aspirates remains unsatisfactory, with a diagnostic accuracy of < or =80%. The purpose of the current study was therefore to develop a novel diagnostic approach based on expression profiling of biopsy material using a specialized diagnostic cDNA array., Experimental Design: Previous gene expression profiling studies were reevaluated to design a 558-feature diagnostic array. Minimal amounts of residual material from pancreatic cytology samples as well as surgically resected tumor and control tissue specimens were analyzed using the diagnostic array and a newly developed statistical classification system., Results and Conclusions: Our diagnostic approach resulted in 95% accurate differentiation between ductal adenocarcinomas and nonmalignant tumors of the pancreas. The diagnostic array, in conjunction with conventional diagnostic procedures, is thus suitable to significantly improve the reliability of pancreatic cancer diagnostics and can be expected to become a valuable new tool in the routine workup of suspect masses in the pancreas.

Pancreatic ductal adenocarcinoma (PDAC) carries the most dismal prognosis of all solid tumours. Both the late clinical presentation of patients, due to lack of early symptoms, as well as the rapid and aggressive course of the disease contribute to the extremely high mortality of this malignancy. Recently, a multistep progression model for PDAC integrating morphological, clinical and molecular evidence has been proposed. Putative precursor lesions, termed pancreatic intraepithelial neoplasia (PanIN), are classified into three different grades (PanIN-1 through -3) based on the degree of cellular atypia they display. We have conducted large-scale expression profiling analyses of microdissected cells from normal pancreatic ducts, PanINs of different grades and PDACs using whole-genome oligonucleotide microarrays. Verification of hybridisation results for selected genes was performed using quantitative real-time PCR and immunohistochemical analyses on PanIN tissue microarrays. Comparison of the expression profiles demonstrated that the greatest changes in gene expression occur between PanIN stages 1B and 2, suggesting that PanIN-2 may represent the first truly preneoplastic stage in PDAC progression. Our results identify a large number of potential target genes for the development of novel molecular diagnostic and therapeutic tools for the prevention and early diagnosis of PDAC and provide novel insights into the pathophysiological mechanisms involved in tumour progression in the pancreas.

Pancreatic stellate cells (PSCs) are thought to be the primary source of the extensive fibrotic reaction characteristic of pancreatic cancer and chronic pancreatitis in humans. PSCs share many morphological and functional characteristics with hepatic stellate cells (HSCs), whose central role in liver fibrosis is well established. However, it has remained unclear if hepatic and pancreatic stellate cells are derived from a common cell lineage and if they are completely similar or if they possess organ-specific features. We have analysed the transcriptomes of HSCs, PSCs and skin fibroblasts to assess how the transcriptional phenotype of stellate cells differs from that of a typical fibroblast lineage cell and if there is evidence for a common stellate cell precursor. To this end, we have performed expression profiling of primary cultures of human HSCs, PSCs and skin fibroblasts using 23,000-feature 'whole genome' oligonucleotide micro-arrays. Expression data were verified using real-time PCR. The expression profiles of HSCs and PSCs displayed a great extent of similarity, clearly separating them from the fibroblasts. Predominantly extracellular and cell surface genes, but also signalling molecules, transcription factors and novel neural markers, were concordantly expressed in both stellate cell types. Despite this high degree of similarity, distinct differences in expression patterns were observed between HSCs and PSCs, reflecting organ-specific variations of the common stellate cell-specific phenotype.

Motivation: Microarray experiments generate vast amounts of data. The unknown or only partially known functional context of differentially expressed genes may be assessed by querying the Gene Ontology database via GOMiner. Resulting tree representations are difficult to interpret and are not suited for visualization of this type of data. Methods are needed to effectively visualize these complex set relationships., Results: We present a visualization approach for set relationships based on Venn diagrams. The proposed extension enhances the usual notion of Venn diagrams by incorporating set size information. The cardinality of the sets and intersection sets is represented by their corresponding circle (polygon) sizes. To avoid local minima, solutions to this problem are sought by evolutionary optimization. This generalized Venn diagram approach has been implemented as an interactive Java application (VennMaster) specifically designed for use with GOMiner in the context of the Gene Ontology database., Availability: VennMaster is platform-independent (Java 1.4.2) and has been tested on Windows (XP, 2000), Mac OS X, and Linux. Supplementary information and the software (free for non-commercial use) are available at http://www.informatik.uni-ulm.de/ni/mitarbeiter/HKestler/vennm together with a user documentation., Contact: hans.kestler@medizin.uni-ulm.de.

Aims: Because of its high spatial resolution and tissue contrast, magnetic resonance imaging (MRI) was used to assess cardiac structure and function in a large population of patients with acute myocardial infarction (AMI)., Methods and Results: One hundred and ten patients were studied by MRI 6.1 +/- 2.2 days after AMI. Infarct size (IS), persistent microvascular obstruction (PMO), left and right ventricular (LV/RV) volumes, and functions were measured. The same MRI measurements were repeated in 89 patients after a mean follow-up period of 225 +/- 92 days. IS was 11.9 +/- 7.3% of total LV muscle mass. PMO was detected in 51/110 (46.4%) patients and comprised 15.6 +/- 8.5% of IS and 2.8 +/- 2.3% of LV muscle mass. Papillary muscle infarct was seen in 26%, RV infarction in 16%, pericarditis in 40%, and pericardial effusion in 66% of the patients. During follow-up, there were 16 major adverse cardiac events (MACE) including seven deaths. IS, PMO, and amount of transmural infarction were predictive for LV adverse remodelling defined as > 20% increase in LV end-diastolic volume. Multivariable analysis revealed LV end-diastolic volume, LV ejection fraction, and PMO as significant predictors for the occurrence of MACE., Conclusion: MRI is a highly sensitive and reliable tool to detect morphologic and functional sequelae of AMI providing baseline MRI parameters with relevant predictive power for LV adverse remodelling and occurrence of MACE.

Axillary lymph node status is a major prognostic factor in mammary carcinoma. It is clinically desirable to predict the axillary lymph node status from data from the mammary cancer specimen. In the study, the axillary lymph node status, routine histological parameters and flow-cytometric data were retrospectively obtained from 1139 specimens of invasive mammary cancer. The ten variables: age, tumour type, tumour grade, tumour size, skin infiltration, lymphangiosis carcinomatosa, pT4 category, percentage of tumour cells in G2/M- and S-phases of the cell cycle, and ploidy index were considered as predictor variables, and the single variable lymph node metastasis pN (0 for pN0, or 1 for pN1 or pN2) was used as an output variable. A stepwise logistic regression analysis, with the axillary lymph node as a dependent variable, was used for feature selection. Only lymphangiosis carcinomatosa and tumour size proved to be significant as independent predictor variables; the other variables were non-contributory. Three paradigms with supervised learning rules (multilayer perceptron, learning vector quantisation and support vector machines) were used for the purpose of prediction. If any of these paradigms was used with the information from all ten input variables, 73% of cases could be correctly predicted, with specificity ranging from 82 to 84% and sensitivity ranging from 60 to 63%. If only the two significant input variables were used, lymphangiosis carcinomatosa and tumour diameter, the prediction accuracy was no worse. Nearly identical results were obtained by two different techniques of cross-validation (leave-one-out against ten-fold cross validation). It was concluded that: artificial neural networks can be used for risk stratification on the basis of routine data in individual cases of mammary cancer; and lymphangiosis carcinomatosa and tumour size are independent predictors of axillary lymph node metastasis in mammary cancer.

Unlabelled: Ejection fraction (EF) and end-diastolic and end-systolic volume index (EDVI/ ESVI) derived from ventriculography are important prognostic parameters. Cine magnetic resonance imaging (MRI) using a steady-state, free-precession sequence (SSFP) offers excellent delineation of the endocardial borders and highly reproducible and accurate results for cardiac volumes. We evaluated MRI volumetry against routine x-ray ventriculography. In 200 patients EF, EDVI and ESVI were measured with MRI volumetry and x-ray ventriculography. The same MRI protocol was applied to 102 healthy persons in order to establish reference values. In healthy subjects mean EF was 68.8% +/- 5.4% (range 59-84%), mean EDVI 69 +/- 10 (43-90) and mean ESVI 22 +/- 5.8 (10-35 ml). In the patients, overall correlation (Spearman's R) of MRI with ventriculography was 0.86 for EF, 0.77 for EDVI and 0.88 for ESVI. For postextrasystolic beats (38% of the measurements), R was 0.73/0.65/0.73 for EF/EDVI/ESVI. MRI correlated best with biplane ventriculography during sinus rhythm (0.96/0.85/0.93); the worst correlation (0.78/0.81/0.83) resulted from patients with wall motion abnormalities in comparison to monoplane x-ray ventriculography., Conclusion: Contemporary MRI volumetry compares well to invasive data obtained under optimal conditions. In view of the known limitations of single plane ventriculography, MRI seems to allow exact volumetry independent from regional wall motion abnormalities.

Tumor samples of 53 patients with t(11;14)-positive mantle cell lymphomas (MCLs) were analyzed by matrix-based comparative genomic hybridization (matrix-CGH) using a dedicated DNA array. In 49 cases, genomic aberrations were identified. In comparison to chromosomal CGH, a 50% higher number of aberrations was found and the high specificity of matrix-CGH was demonstrated by fluorescence in situ hybridization (FISH) analyses. The 11q gains and 13q34 deletions, which have not been described as frequent genomic aberrations in MCL, were identified by matrix-CGH in 15 and 26 cases, respectively. For several genomic aberrations, novel consensus regions were defined: 8p21 (size of the consensus region, 2.4 megabase pairs [Mbp]; candidate genes: TNFRSF10B, TNFRSF10C, TNFRSF10D); 10p13 (2.7 Mbp; BMI1); 11q13 (1.4 Mbp; RELA); 11q13 (5.2 Mbp; CCND1); 13q14 (0.4 Mbp; RFP2, BCMSUN) and 13q34 (6.9 Mbp). In univariate analyses correlating genomic aberrations and clinical course, 8p- and 13q14- deletions were associated with an inferior overall survival. These data provide a basis for further studies focusing on the identification of pathogenetically or clinically relevant genes in MCL.

Genomic analyses aimed at the detection of high-level DNA amplifications were performed on 13 widely used pancreatic cancer cell lines and 6 pancreatic tumor specimens. For these analyses, array-based comparative genomic hybridization (Matrix-CGH) onto dedicated microarrays was used. In comparison with chromosomal CGH (eight amplifications), a >3-fold number of DNA amplifications was detected (n = 29). The most frequent amplifications mapped to 7p12.3 (three pancreatic cancer cell lines and three pancreatic tumor specimens), 8q24 (four pancreatic cancer cell lines and one pancreatic tumor specimen), 11q13 (three pancreatic cancer cell lines and three pancreatic tumor specimens), and 20q13 (four pancreatic cancer cell lines and three pancreatic tumor specimens). Genes contained in the consensus regions were MYC (8q24), EGFR (7p12.3), and FGF3 (11q13). In six of seven pancreatic cancer cell lines and pancreatic tumor specimens with 20q13 amplifications, the novel candidate gene NFAT C2, which plays a role in the activation of cytokines, was amplified. Other amplifications also affected genes for which a pathogenetic role in pancreatic carcinoma has not been described, such as BCL10 and BCL6, two members of the BCL family. A subset of amplified genes was checked for overexpression by means of real-time PCR, revealing the highest expression levels for BCL6 and BCL10. Thus, Matrix-CGH allows the detection of a high number of amplifications, resulting in the identification of novel candidate genes in pancreatic cancer.

Data emerging from DNA microarray experiments are usually difficult to interpret. While the level of expression of several thousand genes can be measured in a single experiment, only a few dozen experiments are normally carried out, leading to data sets of very high dimensionality and low cardinality. The computational analysis of gene expression data makes significant usage of machine learning and statistical methods. Nevertheless, caution should be used in the blind adoption of these methods, as this usually leads to an over-interpretation of the expression profiles. The following presentation provides an overview of up-to-date principles of biostatistical analysis. A potential application for the analysis of high-dimensional expression profiles of prostate cancer is given.

Expression profiling analyses were used to elucidate the functional relevance of RAS proteins in mediating the effect of TGFB1 on the transcriptional phenotype of the pancreatic cancer cell line PANC-1. Despite the presence of one mutated KRAS2 allele in parental PANC-1 pancreatic cancer cells, RAS-dependent signal transduction remained susceptible to stimulation by EGF and TGFB1. To analyze the impact of RAS proteins on the TGFB1-induced transcriptional phenotype, we used PANC-1 cells stably transfected with a dominant negative HRAS(S17N) mutant or with a constitutively active KRAS2(G12V) mutant. TGFB1 treatment of mock-transfected PANC-1 cells led to an expression profile suggestive of epithelial-mesenchymal transdifferentiation (EMT). Profiling of the HRAS(S17N)-expressing clone demonstrated that induction of endogenous RAS activity by TGFB1 is required for the development of the TGFB1-induced transcriptional phenotype of PANC-1 cells. The expression of the KRAS2(G12V) mutant by itself repressed transcription of markers of epithelial differentiation and induced transcription of several extracellular matrix-associated genes. This effect was not enhanced further by TGFB1 treatment. In contrast, transcript levels of genes associated with proliferation and cell cycle progression did not appear to be the primary targets of the synergism between the RAS- and TGFB1-dependent cascades. The introduction of the dominant negative and the constitutively active RAS mutants induced partly overlapping and partly inverse effects on the TGFB1-induced expression profile of PANC-1 cells. Additional mechanisms such as the induction of autocrine loops and the use of different RAS isoforms or alternate, ERK-independent signaling pathways may be involved in the interaction between the RAS- and the TGFB1-dependent signaling cascades., (Copyright 2004 Wiley-Liss, Inc.)

Background: Despite different biological mechanisms involved in the restenotic process of in-stent restenosis and restenosis after balloon angioplasty alone, the occurrence of a second restenosis has been reported in the same range. There are no data available comparing the outcome after re-angioplasty of such lesions. We analyzed in a matched pair comparison the clinical outcome and angiographic long-term result of patients with balloon angioplasty of a first in-stent restenosis versus patients with balloon re-angioplasty of a first balloon restenosis., Methods: Both groups consisted of 74 lesions matched by treated vessel, lesion location differentiated in proximal and non-proximal, and angiographic appearance of coronary artery disease differentiated in singular stenosis, diffuse or mixed pattern. Clinical follow-up was 100%. Angiographic follow-up was 78.4% after median 174 days., Results: Angiographic restenosis rate in matched pairs of patients (n=46/74) was significantly higher in the balloon restenosis group (41.3%, n=19/46) compared to the in-stent restenosis group (21.7%, n=10/46, p<0.042). There was no death or myocardial infarction. After clinical follow-up, target lesion revascularization rate was significantly lower in the in-stent restenosis group compared to the balloon restenosis group (12.1%, n=9/74 versus 27.0%, n=20/74; difference between groups 14.9%, 95% confidence interval 2.0-27.3%, p<0.023). Multivariate logistic regression analysis revealed as predictors for a second restenosis unstable angina pectoris, non-proximal lesion, restenosis after balloon angioplasty and the occurrence of the first restenosis within 90 days after initial intervention., Conclusion: Clinical and angiographic outcome after balloon angioplasty of a first in-stent restenosis was significantly better compared with balloon re-angioplasty of a first balloon restenosis.

Objective: To identify significant predictive factors determining category T1a and T1b in incidental prostatic carcinoma with classical and neural multivariate data analysis methods., Materials and Methods: Incidental prostatic carcinomas diagnosed in our department during 1990-99 (66 cases) were re-examined. Besides acquiring routine clinical and pathological data the tumours were assessed by scoring immunohistochemistry for proliferative activity and p53-overexpression. Tumour vascularization (angiogenesis) and epithelial texture variables were investigated by quantitative stereology. The data were evaluated by classical statistical methods (t-test, correlation analysis, logistic regression). Moreover, self-organizing feature maps (SOMs) were applied as an exploratory approach to unsupervised data analysis by artificial neural networks., Results: The proliferative fraction, p53 overexpression of tumour cell nuclei, preoperative prostate-specific antigen value and density of capillary vascularization correlated with the Gleason score in incidental prostatic carcinoma. In a stepwise logistic regression analysis with the tumour categories T1a and T1b as dependent variables, the Gleason score and the volume fraction of epithelial cells were significant independent predictors of the tumour category. The cases could be grouped into clusters of different degrees of malignancy using SOMs., Conclusions: Texture variables of tumour cells are of central importance for the extent of propagation in the prostate in incidental prostatic adenocarcinomas. Gleason score and quantitative stereological estimates of the volume fraction of tumour cells are significant predictors of T1a and T1b categories of incidental prostatic carcinoma. Unsupervised clustering of T1 prostate carcinoma cases by SOMs correlates well with the dichotomous classification into T1a and T1b according to the UICC.

B cell chronic lymphocytic leukemia (B-CLL) is characterized by a highly variable clinical course. Recurrent chromosomal imbalances provide significant prognostic markers. Risk-adapted therapy based on genomic alterations has become an option that is currently being tested in clinical trials. To supply a robust tool for such large scale studies, we developed a comprehensive DNA microarray dedicated to the automated analysis of recurrent genomic imbalances in B-CLL by array-based comparative genomic hybridization (matrix-CGH). Validation of this chip in a series of 106 B-CLL cases revealed a high specificity and sensitivity that fulfils the criteria for application in clinical oncology. This chip is immediately applicable within clinical B-CLL treatment trials that evaluate whether B-CLL cases with distinct chromosomal abnormalities should be treated with chemotherapy of different intensities and/or stem cell transplantation. Through the control set of DNA fragments equally distributed over the genome, recurrent genomic imbalances were discovered: trisomy of chromosome 19 and gain of the MYCN oncogene correlating with an elevation of MYCN mRNA expression.

The subclassification of incidental prostatic carcinoma into the categories T1a and T1b is of major prognostic and therapeutic relevance. In this paper an attempt was made to find out which properties mainly predispose to these two tumor categories, and whether it is possible to predict the category from a battery of clinical and histopathological variables using newer methods of multivariate data analysis. The incidental prostatic carcinomas of the decade 1990-99 diagnosed at our department were reexamined. Besides acquisition of routine clinical and pathological data, the tumours were scored by immunohistochemistry for proliferative activity and p53-overexpression. Tumour vascularization (angiogenesis) and epithelial texture were investigated by quantitative stereology. Learning vector quantization (LVQ) and support vector machines (SVM) were used for the purpose of prediction of tumour category from a set of 10 input variables (age, Gleason score, preoperative PSA value, immunohistochemical scores for proliferation and p53-overexpression, 3 stereological parameters of angiogenesis, 2 stereological parameters of epithelial texture). In a stepwise logistic regression analysis with the tumour categories T1a and T1b as dependent variables, only the Gleason score and the volume fraction of epithelial cells proved to be significant as independent predictor variables of the tumour category. Using LVQ and SVM with the information from all 10 input variables, more than 80 of the cases could be correctly predicted as T1a or T1b category with specificity, sensitivity, negative and positive predictive value from 74-92%. Using only the two significant input variables Gleason score and epithelial volume fraction, the accuracy of prediction was not worse. Thus, descriptive and quantitative texture parameters of tumour cells are of major importance for the extent of propagation in the prostate gland in incidental prostatic adenocarcinomas. Classical statistical tools and neuronal approaches led to consistent conclusions.

Background and Objective: In-vitro studies revealed that nitric oxide (NO) may affect rheological parameters. We studied the effect of highly-dosed NO-donor molsidomine on blood rheology and the impact of rheological parameters on the incidence of severe cardiovascular events., Patients and Methods: In this randomized, placebo-controlled and double-blind trial 166 patients (60 +/- 10 years) with stable angina pectoris and coronary intervention received molsidomine 3 x 8 mg t. i. d. (controlled release tablets) or placebo for 6 months. Patients with inflammatory/neoplastic disorders or elevated values of C-reactive protein were excluded from analysis. A rheological profile (plasma viscosity, blood viscosity, aggregation and flexibility of erythrocytes, filtrability of leukocytes, fibrinogen levels) was done initially and after 6 months. Adverse cardiovascular events (death, myocardial infarction, stroke, coronary/peripheral revascularization) were recorded during 12 months. Furthermore, the impact of rheological parameters regarding the occurrence of severe cardiovascular events (death, myocardial infarction, stroke) was evaluated during a follow-up of median 38 months., Results: The data of 137 patients (n = 71 placebo, n = 66 molsidomine) were analysed. The difference of rheological parameters between the two measurements did not vary between the two groups. Analysis of event-free survival with Kaplan-Meier technique revealed no difference between the two groups. Multivariate Cox regression analysis with adjustment for diabetes mellitus, smoking and therapy with statin showed a significant association of fibrinogen and plasma viscosity with the occurrence of severe cardiovascular events., Conclusion: Treatment with molsidomine 3 x 8 mg/day for 6 months does not improve blood rheology or reduce cardiovascular events. But elevated levels of fibrinogen and plasma viscosity were associated with the occurrence of severe cardiovascular events.

Recently, DNA microarray technology has opened new avenues for the understanding of lymphomas. By hybridization of cDNA to arrays containing >10,000 different DNA fragments, this approach allows the simultaneous evaluation of the mRNA expression of thousands of genes in a single experiment. Using sophisticated bioinformatic tools, the huge amount of raw data can be clustered resulting in (1) tumor subclassification, (2) identification of pathogenetically relevant genes, or (3) biological predictors for the clinical course. This approach already has provided novel insights into different entities of B-cell non-Hodgkin's lymphomas. Genomic DNA chip hybridization (matrix-CGH) is a complementary approach focussing on genomic aberrations. In this review, we discuss the impact of this new technology both with regard to methodological aspects as well as to novel findings influencing our understanding of lymphomas.

The aim of this study was to simplify the imaging of myocardial infarction based on theoretical aspects and patient variables and to define the optimal time for image acquisition. Thirteen patients with chronic myocardial infarction underwent magnetic resonance imaging. After injection of 0.2 mmol/kg body weight Gd-DTPA an inversion recovery turbo gradient echo sequence with different prepulse delays was applied every 3 to 5 minutes within an interval of 3 to 30 minutes. As parameters of investigation, the area of signal enhancement and the contrast between enhanced and nonenhanced myocardium were used. There was no influence of prepulse delay or time after contrast injection on the enhanced area. The contrast between enhanced and normal myocardium showed a peak at 6 minutes post Gd-DTPA injection and remained high. The contrast between blood and enhanced myocardium was best at 6 and 25 minutes with best intra- and interobserver variability. In conclusion, if a suitable contrast was achieved, the area of enhancement is independent of prepulse delay or imaging time. In most patients the highest contrast between blood, enhanced and normal myocardium is achieved 6 minutes and 25 minutes after contrast injection.

Staging of prostate cancer is a mainstay of treatment decisions and prognostication. In the present study, 50 pT2N0 and 28 pT3N0 prostatic adenocarcinomas were characterized by Gleason grading, comparative genomic hybridization (CGH), and histological texture analysis based on principles of stereology and stochastic geometry. The cases were classified by learning vector quantization and support vector machines. The quality of classification was tested by cross-validation. Correct prediction of stage from primary tumor data was possible with an accuracy of 74-80% from different data sets. The accuracy of prediction was similar when the Gleason score was used as input variable, when stereological data were used, or when a combination of CGH data and stereological data was used. The results of classification by learning vector quantization were slightly better than those by support vector machines. A method is briefly sketched by which training of neural networks can be adapted to unequal sample sizes per class. Progression from pT2 to pT3 prostate cancer is correlated with complex changes of the epithelial cells in terms of volume fraction, of surface area, and of second-order stereological properties. Genetically, this progression is accompanied by a significant global increase in losses and gains of DNA, and specifically by increased numerical aberrations on chromosome arms 1q, 7p, and 8p.

Comparative genomic hybridization (CGH) is an established genetic method which enables a genome-wide survey of chromosomal imbalances. For each chromosome region, one obtains the information whether there is a loss or gain of genetic material, or whether there is no change at that place. Therefore, large amounts of data quickly accumulate which must be put into a logical order. Cluster analysis can be used to assign individual cases (samples) to different clusters of cases, which are similar and where each cluster may be related to a different tumour biology. Another approach consists in a clustering of chromosomal regions by rewriting the original data matrix, where the cases are written as rows and the chromosomal regions as columns, in a transposed form. In this paper we applied hierarchical cluster analysis as well as two implementations of self-organizing feature maps as classical and neuronal tools for cluster analysis of CGH data from prostatic carcinomas to such transposed data sets. Self-organizing maps are artificial neural networks with the capability to form clusters on the basis of an unsupervised learning rule. We studied a group of 48 cases of incidental carcinomas, a tumour category which has not been evaluated by CGH before. In addition we studied a group of 50 cases of pT2N0-tumours and a group of 20 pT3N0-carcinomas. The results show in all case groups three clusters of chromosomal regions, which are (i) normal or minimally affected by losses and gains, (ii) regions with many losses and few gains and (iii) regions with many gains and few losses. Moreover, for the pT2N0- and pT3N0-groups, it could be shown that the regions 6q, 8p and 13q lay all on the same cluster (associated with losses), and that the regions 9q and 20q belonged to the same cluster (associated with gains). For the incidental cancers such clear correlations could not be demonstrated.

In this paper, learning algorithms for radial basis function (RBF) networks are discussed. Whereas multilayer perceptrons (MLP) are typically trained with backpropagation algorithms, starting the training procedure with a random initialization of the MLP's parameters, an RBF network may be trained in many different ways. We categorize these RBF training methods into one-, two-, and three-phase learning schemes. Two-phase RBF learning is a very common learning scheme. The two layers of an RBF network are learnt separately; first the RBF layer is trained, including the adaptation of centers and scaling parameters, and then the weights of the output layer are adapted. RBF centers may be trained by clustering, vector quantization and classification tree algorithms, and the output layer by supervised learning (through gradient descent or pseudo inverse solution). Results from numerical experiments of RBF classifiers trained by two-phase learning are presented in three completely different pattern recognition applications: (a) the classification of 3D visual objects; (b) the recognition hand-written digits (2D objects); and (c) the categorization of high-resolution electrocardiograms given as a time series (ID objects) and as a set of features extracted from these time series. In these applications, it can be observed that the performance of RBF classifiers trained with two-phase learning can be improved through a third backpropagation-like training phase of the RBF network, adapting the whole set of parameters (RBF centers, scaling parameters, and output layer weights) simultaneously. This, we call three-phase learning in RBF networks. A practical advantage of two- and three-phase learning in RBF networks is the possibility to use unlabeled training data for the first training phase. Support vector (SV) learning in RBF networks is a different learning approach. SV learning can be considered, in this context of learning, as a special type of one-phase learning, where only the output layer weights of the RBF network are calculated, and the RBF centers are restricted to be a subset of the training data. Numerical experiments with several classifier schemes including k-nearest-neighbor, learning vector quantization and RBF classifiers trained through two-phase, three-phase and support vector learning are given. The performance of the RBF classifiers trained through SV learning and three-phase learning are superior to the results of two-phase learning, but SV learning often leads to complex network structures, since the number of support vectors is not a small fraction of the total number of data points.

Objective: The choice of therapy for prostatic cancer should depend on a rational preoperative estimate of tumor stage. Artificial neural networks were used to predict postoperative staging of prostatic cancer from sextant biopsies and routinely available preoperative data., Methods: In group I (97 cases), nonorgan confinement (tumor stage > or =pT3a) was predicted on the basis of age and six histopathological variables from sextant biopsies. In group II (77 cases), nonorgan confinement and extraprostatic organ infiltration (tumor classification > or =pT3b) were predicted from age, four histopathological variables, the preoperative PSA level, and the total prostate volume estimated by preoperative ultrasonography. Learning vector quantization (LVQ) networks were applied for this purpose and compared to multilayer perceptrons (MLP) and linear discriminant analysis (LDA)., Results: Nonorgan confinement could be predicted correctly in 90% of newly presented cases from sextant biopsy histopathology alone. A similar accuracy of predicting nonorgan confinement (83%) was obtained by combining preoperative biopsy histology with clinical data. Extraprostatic organ infiltration could be predicted correctly in 82%. The best results were obtained by LVQ networks, followed by MLP networks and LDA., Conclusion: The postoperative tumor stage of prostatic cancer can be estimated with high accuracy, sensitivity and specificity from preoperative routine parameters using artificial neural networks, especially LVQ networks. The results suggest that this methodology should be evaluated in a larger prospective study.

Receiver operator characteristic (ROC) curves are recommended to assess the diagnostic value of tests depending on a single cut-off value of a continuous variable. These ROC curves show the true-positive rate (sensitivity) against the false-positive rate (1-specificity). It is desirable, especially in situations with small samples of observations, to display confidence bounds of the ROC curve. This paper presents a Perl program which calculates the ROC curve and its distribution-free confidence bounds. A simple user interface also written in Perl permits their display.

Comparative genomic hybridization (CGH) is a modern genetic method which enables a genome-wide survey of chromosomal imbalances. For each chromosome region, one obtains the information whether there is a loss or gain of genetic material, or whether there is no change at that region. Usually it is not possible to evaluate all 46 chromosomes of a metaphase, therefore several (up to 20 or more) metaphases are analyzed per individual, and expressed as average. Mostly one does not study one individual alone but groups of 20-30 individuals. Therefore, large amounts of data quickly accumulate which must be put into a logical order. In this paper we present the application of a self-organizing map (Genecluster) as a tool for cluster analysis of data from pT2N0 prostate cancer cases studied by CGH. Self-organizing maps are artificial neural networks with the capability to form clusters on the basis of an unsupervised learning rule, i.e., in our examples it gets the CGH data as only information (no clinical data). We studied a group of 40 recent cases without follow-up, an older group of 20 cases with follow-up, and the data set obtained by pooling both groups. In all groups good clusterings were found in the sense that clinically similar cases were placed into the same clusters on the basis of the genetic information only. The data indicate that losses on chromosome arms 6q, 8p and 13q are all frequent in pT2N0 prostatic cancer, but the loss on 8p has probably the largest prognostic importance.

Stereology and stochastic geometry can be used as auxiliary tools for diagnostic purposes in tumour pathology. Whether first-order parameters or stochastic-geometric functions are more important for the classification of the texture of biological tissues is not known. In the present study, volume and surface area per unit reference volume, the pair correlation function and the centred quadratic contact density function of epithelium were estimated in three case series of benign and malignant lesions of glandular tissues. The information provided by the latter functions was summarized by the total absolute areas between the estimated curves and their horizontal reference lines. These areas are considered as indicators of deviation of the tissue texture from a completely uncorrelated volume process and from the Boolean model with convex grains, respectively. We used both areas and the first-order parameters for the classification of cases using artificial neural networks (ANNs). Learning vector quantization and multilayer feedforward networks with backpropagation were applied as neural paradigms. Applications included distinction between mastopathy and mammary cancer (40 cases), between benign prostatic hyperplasia and prostatic cancer (70 cases) and between chronic pancreatitis and pancreatic cancer (60 cases). The same data sets were also classified with linear discriminant analysis. The stereological estimates in combination with ANNs or discriminant analysis provided high accuracy in the classification of individual cases. The question of which category of estimator is the most informative cannot be answered globally, but must be explored empirically for each specific data set. Using learning vector quantization, better results could often be obtained than by multilayer feedforward networks with backpropagation.

Patients susceptible to malignant arrhythmias often have an increased beat-to-beat variation of the T-wave of the electrocardiogram. Variability analysis of the T-wave is increasingly used for non-invasive risk assessment. The aim of this study is to evaluate intra-QRS beat-to-beat signal variation and to compare it to ST-T variation. The beat-to-beat, microvolt variation of the QRS and the ST-T segment from 44 patients with coronary heart disease at high risk of suffering from malignant arrhythmias and from 51 healthy volunteers are compared. Variation analysis is carried out on 250 consecutive sinus beats from high-resolution electrocardiograms. The individual beats are filtered using a waveform-independent, cubic spline-filter. A variability index of the QRS and ST-T segments is calculated as the integrated standard deviation of corresponding samples inside the area of interest. Patients at risk of suffering from malignant arrhythmias have a significantly higher variability index of both the QRS (median 44.5 ms against 34.7 ms, p < 0.001) and the ST-T segment (median 20.5 ms against 9.8 ms, p < 0.001) compared to the group of healthy subjects. The discriminative ability of the odds variability indices of the QRS and ST-T segments are not statistically different, the ratios being 7.8 (QRS) and 12.6 (ST-T). We conclude that patients at high risk of suffering from malignant arrhythmias are characterised by an increased beat-to-beat microvolt variation of both the QRS and the ST-T segment. Further studies are necessary to evaluate the prognostic potential of depolarisation variability.

Objective: To report a methodological feasibility study in a small series of patients with node-negative organ-confined prostatic cancer, using artificial neural networks to predict tumour progression after radical prostatectomy and thus help to identify high-risk patients who would benefit from adjuvant treatment., Patients and Methods: A group of 20 patients with pT2N0 prostatic cancer and postoperative tumour progression was compared with a control group of 20 patients with no progression, matched for age, duration of follow-up and preoperative serum prostate-specific antigen level. Histopathological data were obtained from the radical prostatectomy specimens, i.e. the Gleason score, World Health Organisation (WHO) grade and maximum diameter of the tumour transects. The volume and surface area of the epithelial tumour component and of the lumina of the neoplastic glands per unit tissue volume were estimated by morphometric methods. To predict recurrence, multilayer feedforward networks with backpropagation (MLFF-BP), two implementations of learning vector quantization (LVQ), and linear discriminant analysis (LDA) were applied. The ability of these models to correctly classify new cases was tested using the 'leave-one-out' technique., Results: Progression was predicted correctly in 85% of newly presented cases from the three routine histopathological variables alone. On the basis of the four morphometric variables alone progression was predicted correctly in 93% of cases. The use of all seven variables as input data only slightly improved the quality of prediction. The best results were obtained by the LVQ networks and LDA, followed by MLFF-BP networks., Conclusions: In this methodological feasibility study, the progression of pT2N0 prostatic cancer after radical prostatectomy could be predicted with good accuracy, sensitivity and specificity from routine variables or morphometric texture variables using artificial neural networks. These results suggest that this approach should be assessed in a prospective study with more cases.

Background: Cytological smears obtained from the cervix are routinely examined under the microscope as part of screening programs for the early detection of cervical cancer. The aim of the present study was to investigate whether a simple feature extraction approach using only standard image processing techniques combined with a neural classifier would lead to acceptable results that might serve as a starting point for the development of a fully automated screening system., Materials and Methods: Gray-value images of 106 cervical smears (512 x 512 pixels) divided into two groups--inconspicuous (57) and atypical (49)--by an experienced pathologist on the basis of the original smears were employed to evaluate the method. From these images, 31 features quantifying properties of either the cell nucleus or the cytoplasm were extracted. These features were categorized with three different architectures of a neural classifier: learning vector quantization (LVQ), multilayer perceptron (MLP) and a single perceptron., Conclusions: The results show a reclassification accuracy of about 91% for all three algorithms. Sensitivity was uniform at approximately 78%, and specificity varied between 75% and 91% in the leave-one-out evaluation. These very good results provide strong encouragement for further studies involving PAP scores and colour images.

The patch-clamp technique was used to examine the presence of large conductance calcium-activated potassium channels (BKCa) in human endothelial cells and to characterize their properties in terms of voltage dependence, ion conduction and blockade by iberiotoxin (IbTX). Experiments were performed using cell-attached and outside-out configurations on human umbilical vein endothelial cells (HUVEC). For the experiments HUVECs, which were passaged 6-19 times, were used. In early passages channel activities were absent suggesting the appearance of BKCa depending on cell culture time. The inverse logarithmic voltage sensitivity was 10.17 mV (median) for cell-attached recordings and 12.10 mV (median) for outside-out patches (membrane voltage range: 60-120 mV, symmetrical 140 mM K+ solutions). The I/V relationship was quasilinear in the range of 0-80 mV and exhibited a nonlinear behaviour under further depolarization suggesting some kind of saturation mechanism. Using a sigmoid function to fit the data, channel conductance was calculated as 172.9 pS (median) for cell-attached patches and as 262.1 pS (median) for outside-out patches. IbTX, known as one of the most selective blockers of BKCa was perfused to outside-out patches. In two out of three experiments there was complete block of the ion channel after 1 min.