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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.

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.

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.