[Efficiency of artificial neural networks for prediction of regional lymph node metastasis].
A retrospective study of 90 patients suffering from laryngeal carcinoma who were surgically treated in the Department of Otolaryngology, Collegium Medicum, Jagiellonian University, Cracow, Poland, has been performed. In 26 patients metastases in regional lymph nodes has been found. Various clinical, histopathological and kariometric factors have been subject to analysis. The artificial neural networks techniques have been used for prediction of presence of metastases in lymph nodes. The artificial neural networks exhibit an ability to generalize, i.e. they can learn from exemplary sets of data. Therefore the patients data have been divided at random into two sets forming a learning set (70 patients) and a test set (20 patients). A series of simulations have been carried out, using networks of various architectures and experimentally selected learning parameters, giving the prediction results between 71% and 96% of correct recognitions. The best prediction results have been achieved for the network of triple layer perceptron type including 6 neurons in the hidden layer. The evaluation of factors related to the presence of metastases in lymph nodes has been also carried out using logistic regression method for several groups of patients including various numbers of cases. The data sets of the remaining patients have been used for testing the reliability of that statistical method, giving between 56% and 64% of correct recognitions. Therefore the possibilities of prediction of metastases presence in lymph nodes are better for the artificial neural networks method (up to 96% of correct classifications) i.m. comparison to the logistic regression method (only up to 64% of correct classifications for the same data sets).
Dobroś
W
W
Klinika Otolaryngologii Collegium Medicum UJ w Krakowie.
Izworski
A
A
Gil
K
K
Lech
T
T
D006801:Humans; D008207:Lymphatic Metastasis / Q000473:pathology*; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012189:Retrospective Studies