The value of histopathological prognostic factors in superficial bladder cancer

Cell-nuclear data reduction and prognostic model selection in bladder tumor recurrence

OBJECTIVE

The paper aims at improving the prediction of superficial bladder recurrence. To this end, feedforward neural networks (FNNs) and a feature selection method based on unsupervised clustering, were employed.

MATERIAL AND METHODS

A retrospective prognostic study of 127 patients diagnosed with superficial urinary bladder cancer was performed. Images from biopsies were digitized and cell nuclei features were extracted. To design FNN classifiers, different training methods and architectures were investigated. The unsupervised k-windows (UKW) and the fuzzy c-means clustering algorithms were applied on the feature set to identify the most informative feature subsets.

RESULTS

UKW managed to reduce the dimensionality of the feature space significantly, and yielded prediction rates 87.95% and 91.41%, for non-recurrent and recurrent cases, respectively. The prediction rates achieved with the reduced feature set were marginally lower compared to the ones attained with the complete feature set. The training algorithm that exhibited the best performance in all cases was the adaptive on-line backpropagation algorithm.

CONCLUSIONS

FNNs can contribute to the accurate prognosis of bladder cancer recurrence. The proposed feature selection method can remove redundant information without a significant loss in predictive accuracy, and thereby render the prognostic model less complex, more robust, and hence suitable for clinical use.

Advanced soft computing diagnosis method for tumour grading

OBJECTIVE

To develop an advanced diagnostic method for urinary bladder tumour grading. A novel soft computing modelling methodology based on the augmentation of fuzzy cognitive maps (FCMs) with the unsupervised active Hebbian learning (AHL) algorithm is applied.

MATERIAL AND METHODS

One hundred and twenty-eight cases of urinary bladder cancer were retrieved from the archives of the Department of Histopathology, University Hospital of Patras, Greece. All tumours had been characterized according to the classical World Health Organization (WHO) grading system. To design the FCM model for tumour grading, three experts histopathologists defined the main histopathological features (concepts) and their impact on grade characterization. The resulted FCM model consisted of nine concepts. Eight concepts represented the main histopathological features for tumour grading. The ninth concept represented the tumour grade. To increase the classification ability of the FCM model, the AHL algorithm was applied to adjust the weights of the FCM.

RESULTS

The proposed FCM grading model achieved a classification accuracy of 72.5%, 74.42% and 95.55% for tumours of grades I, II and III, respectively.

CONCLUSIONS

An advanced computerized method to support tumour grade diagnosis decision was proposed and developed. The novelty of the method is based on employing the soft computing method of FCMs to represent specialized knowledge on histopathology and on augmenting FCMs ability using an unsupervised learning algorithm, the AHL. The proposed method performs with reasonably high accuracy compared to other existing methods and at the same time meets the physicians' requirements for transparency and explicability.

Genetic aberrations of c-myc and CCND1 in the development of invasive bladder cancer

Detrusor muscle invasive transitional cell carcinoma is associated with poor prognosis and is responsible for the majority of bladder cancer related deaths. Amplifications of c-myc and CCND1 are associated with detrusor-muscle-invasive transitional cell carcinoma, however, their precise role in driving disease progression is unclear. Fluorescence in situ hybridisation on archival tissue from 16 patients with primary diagnosis of > or = pT2 transitional cell carcinoma and 15 cases with primary pTa/pT1 disease subsequently progressing to detrusor-muscle-invasion was performed, in the latter group both pre and post muscle invasive events were studied. No patients presenting with >/=pT2 had amplification of c-myc, two out of 16 (12.5%) had CCND1 amplification. Of patients who developed > or = pT2, two out of 15 (13.3%) had amplification of c-myc, both in > or = pT2, five out of 15 (33.3%) had CCND1 amplification, two in pTa/pT1 tumours, three in > or = pT2 transitional cell carcinomas. In total, two out of 31 (6.5%) of patients' > or = pT2 TCCs were amplified for c-myc and six out of 31 (19%) were amplified for CCND1. Eighty-seven per cent (40 out of 46) of tumours were polysomic for chromosome 8 and 80% (37 out of 46) were polysomic for chromosome 11 and this reflected the high copy numbers of c-myc and CCND1 observed. In almost all cases an increase in c-myc/CCND1 copy number occurred prior to invasion and persisted in advanced disease. Amplification of CCND1 or alterations in c-myc/CCND1 early in bladder cancer may have clinical relevance in promoting and predicting progression to detrusor-muscle-invasive transitional cell carcinoma.