Estimation of tumor stage and lymph node status in patients with colorectal adenocarcinoma using probabilistic neural networks and logistic regression.

Staging colorectal adenocarcinoma on the basis of biopsy specimens could identify patients who might benefit from neoadjuvant therapy without undergoing resection first. In this study, we evaluated the ability of artificial neural networks with genetic algorithms and multivariate logistic regression to predict the stage of 99 patients with primary colorectal adenocarcinoma by analyzing age, tumor grade, and immunoreactivity to p53 and bcl-2 with use of endoscopically obtained biopsy specimens. We correlated results with regional lymph node status and tumor stage, identified in subsequent colectomy specimens. bcl-2 and p53 protein expression were demonstrated by immunohistochemical methods, using formalin-fixed, paraffin-embedded biopsy tissues. Tumor grade was evaluated in hematoxylinand eosin-stained sections. Patients were divided into training (n = 75) and testing cases (n = 24). Several probabilistic neural networks with genetic algorithm models were trained, using the four prognostic features as input neurons and regional lymph node status or stage as output neurons. Data were analyzed with univariate statistics and multivariate logistic regression. The cases were divided into training (n = 40) and testing (n = 59). The best two models classified correctly the lymph node status of 20 of 24 test patients (specificity, 80%; sensitivity, 85%; positive predictive value, 86%) and the tumor stage of 21 of 24 test patients (specificity, 82%; sensitivity, 92%; positive predictive value, 85%), respectively. Tumor grade and p53 protein were statistically significant (P < .05) by analysis of variance for lymph node status and tumor stage. Logistic regression models with these two independent variables correctly estimated the probability of lymph node metastases in 44 of 59 test cases and the tumor stage of 43 of 59 test cases, respectively. Results indicated the usefulness of probabilistic neural networks in the population studied, but the findings should be validated with large groups of patients.