Static cytometry and neural networks in the discrimination of lower urinary system lesions.

OBJECTIVES: To investigate the potential of morphometry and artificial intelligence tools for the discrimination of benign and malignant lower urinary system lesions. METHODS: The study group included 50 cases of lithiasis, 61 cases of inflammation, 99 cases of benign prostatic hyperplasia, 5 cases of in situ carcinoma, 71 cases of grade I transitional cell carcinoma of the bladder (TCCB), and 184 cases of grade II and grade III TCCB. Images of voided urine smears stained by the Giemsa technique were analyzed by a custom image analysis system. The analysis gave a data set of features from 45,452 cells. A learning vector quantizer (LVQ)-type neural network (NN) was used to discriminate benign from malignant cells on the basis of the extracted morphometric and textural features. The data from 13,636 randomly selected cells were used as a training set and the data from the remaining 31,816 cells made up the test set. Similarly, in an attempt to discriminate at the patient level, 30% of the cases randomly selected were used to train an LVQ NN and the remaining 329 cases were used for the test. RESULTS: The application of the LVQ NN enabled the correct classification of 95.42% of the benign cells and 86.75% of the malignant cells, giving an overall accuracy rate of 90.63%. At the patient level, the LVQ NN enabled the correct classification of 100% of benign cases and 95.6% of malignant cases, giving an overall accuracy rate of 97.57%. CONCLUSIONS: NNs combined with image analysis offer useful information in the discrimination of benign and malignant cells and lesions of the lower urinary system.

Back propagation neural network in the discrimination of benign from malignant lower urinary tract lesions.

PURPOSE: We investigated the potential value of morphometry and artificial intelligence tools to discriminate between benign and malignant lower urinary tract lesions. MATERIALS AND METHODS: The lesions included lithiasis in 50 cases, inflammation in 61, benign prostatic hyperplasia in 99, carcinoma in situ in 5, and grade I and grades II and III transitional cell carcinoma of the bladder in 71 and 184, respectively. Images of routine processed voided urine smears stained by the Giemsa technique were analyzed using a custom image analysis system, providing a data set of 45,452 cells. A neural net model of the back propagation type was used to discriminate benign from malignant cells based on the extracted morphometric and textural features. Data from 13,636 randomly selected cells (30% of the total data) were used as a training set and the data from the remaining 31,816 cells comprised the test set. In a similar attempt to discriminate at the patient level data on 30% of those randomly selected were used to train a back propagation neural net and data on the remaining 329 were used for testing. RESULTS: Application of the back propagation neural net enabled the correct classification of 95.34% of benign and 86.71% of malignant cells with overall 90.57% accuracy. At the patient level the back propagation neural net enabled the correct classification of 100% of those with benign and 94.51% of those with malignant disease with overall 96.96% accuracy. CONCLUSIONS: The use of neural nets and image morphometry may increase the speed of cytological diagnosis and the diagnostic accuracy of voided urine cytology.

Comparing neural networks in the discrimination of benign from malignant lower urinary tract lesions.

OBJECTIVE: To compare the performance of two different neural networks (NNs) in the discrimination of benign and malignant lower urinary tract lesions. MATERIALS AND METHODS: A group of patients was evaluated, comprising 50 cases of lithiasis, 61 of inflammation, 99 of benign prostatic hyperplasia (BPH), five of in situ carcinoma, 71 of grade I transitional cell carcinoma of the bladder (TCCB), and 184 of grade II and grade III TCCB. Images of routinely processed voided urine smears were stained using the Giemsa technique and analysed using an image-analysis system, providing a dataset of 45452 cells. Two NN models of the back propagation (BP) and learning vector quantizer (LVQ) type were used to discriminate benign from malignant cells and lesions, based on morphometric and textural features. The data from 13636 randomly selected cells (30% of the total data) were used as a training set and data from the remaining 31816 cells comprised the test set. Similarly, in an attempt to discriminate patients, 30% of the cases, selected randomly, were used to train a BP and an LVQ NN, with the remaining 329 cases used for the test set. The data used for training and testing were the same for the two kinds of classifiers. RESULTS: The two NNs gave similar results, with an overall accuracy of discrimination of approximately 90.5% at the cellular level and of approximately 97% for individual patients. There were no statistically significant differences between the two NNs at the cellular or patient level. CONCLUSIONS: The use of NNs and image morphometry could increase the diagnostic accuracy of voided urine cytology; despite the different nature of the two classifiers, the results obtained were very similar.