Potential of radial basis function neural networks in discriminating benign from malignant lesions of the lower urinary tract.
OBJECTIVE
To investigate the potential value of morphometry and neural network tools for discriminating benign from malignant nuclei and lesions of the lower urinary tract.


METHODS
The study group consisted of 33 cases of lithiasis, 41 cases of inflammation, 66 cases of benign hyperplasia of the prostate, 4 cases of carcinoma in situ, 48 cases of grade 1 transitional cell carcinoma of the bladder (TCCB) and 123 cases of grade 2 and 3 TCCB. Images of routinely processed voided urine smears stained by the Giemsa technique were analyzed by a custom image analysis system. Analysis of the images gave a data set of features from 31,158 nuclei. A radial basis function (RBF)-type neural network was employed to discriminate benign from malignant nuclei, based on the extracted morphometric and textural features. Subsequently a second RBF classifier was employed to discriminate benign from malignant cases. The nuclei from 156 randomly selected cases (50% of total cases) was used as a training set, and the nuclei from the remaining 159 cases made up the test set. Similarly, in an attempt to discriminate at the patient level, the same 156 cases were used to train an RBF classifier; the remaining 159 cases were used for the test set. The cases used for training and testing the 2 classifiers (nuclear and patient level) were the same for the 2 kinds of classifiers.


RESULTS
Application of the RBF classifier permitted the correct classification of 93.64% of benign nuclei and 85.61% of malignant, giving an overall accuracy of 84.45%. At the patient level the RBF classifier permitted an overall accuracy of 94.97%. These results were on the test sets.


CONCLUSIONS
The role of nuclear morphologic features in the cytologic diagnosis of lower urinary tract alterations was confirmed by the results of this study. The observed overlap in feature space indicates that the nuclear characteristics do not form strictly separate clusters; that fact explains the difficulty morphologists have with reproducible identification of nuclei from the lower urinary tract. Application of RBF offers good classification at the nuclear and patient level and promises to become a powerful tool for everyday practice in the cytologic laboratory.
Karakitsos
Petros
P
Department of Cytopathology, Attikon University Hospital, Athens, Greece.
Pouliakis
Abraham
A
Kordalis
George
G
Georgoulakis
John
J
Kittas
Christos
C
Kyroudes
Aspasia
A
D000465:Algorithms; D001185:Artificial Intelligence; D002295:Carcinoma, Transitional Cell / Q000145:classification / Q000175:diagnosis / Q000473:pathology; D002467:Cell Nucleus / Q000473:pathology; D003937:Diagnosis, Differential; D006801:Humans; D019044:Image Cytometry / Q000379:methods*; D007091:Image Processing, Computer-Assisted; D007249:Inflammation / Q000175:diagnosis / Q000473:pathology; D007620:Karyometry; D020347:Lithiasis / Q000175:diagnosis / Q000473:pathology; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity; D014551:Urinary Tract / Q000473:pathology; D014556:Urine / Q000166:cytology; D014571:Urologic Neoplasms / Q000175:diagnosis* / Q000473:pathology