Automatic quantification of microvessel density in urinary bladder carcinoma

Seventy-three TUR-T biopsies from bladder carcinoma were evaluated regarding microvessel density, defined as microvessel number (nMVD) and cross-section endothelial cell area (aMVD). A semi-automatic and a newly developed, automatic image analysis technique were applied in immunostainings, performed according to an optimized staining protocol. In 12 cases a comparison of biopsy material and the corresponding cystectomy specimen were tested, showing a good correlation in 11 of 12 cases (92%). The techniques proved reproducible for both nMVD and aMVD quantifications related to total tumour area. However, the automatic method was dependent on high immunostaining quality. Simultaneous, semi-automatic quantification of microvessels, stroma and epithelial fraction resulted in a decreased reproducibility. Quantification in ten images, selected in a descending order of MVD by subjective visual judgement, showed a poor observer capacity to estimate and rank MVD. Based on our results we propose quantification of MVD related to one tissue compartment. When staining quality is of high standard, automatic quantification is applicable, which facilitates quantification of multiple areas and thus, should minimize selection variability. © 1999 Cancer Research Campaig

Urinary Bladder Tumor Grade Diagnosis Using On-line Trained Neural Networks

This paper extends the line of research that considers the application of Artificial Neural Networks (ANNs) as an automated system, f or the assi nment of tumors rade. One hundred twenty nine cases were classified accordin to the WHO radin system by experienced patholoists in three classes: Grade I, Grade II and Grade III. 36 morpholo ical and textural, cell nuclei feig res represented each case. Thesef eatures were used as an input to the ANN classifier, which was trained usin a novel stochastic trainin al orithm, namely, the Adaptive Stochastic On-Line method. The resultin automated classification system achieved classification accuracyof 90%, 94.9% and 97.3%f. tumors of Grade I, II and III respectively.