PURPOSE: An Internet-web-based artificial neural network has been developed for practicing clinical oncologists and medical researchers as part of an ongoing program designed for the implementation of advanced neural networks for prognostic estimates and eventually for management/treatment decisions in individual patients with colonic cancer. METHODS: An interdisciplinary team of academic oncologists and physicists has configured and implemented a Partial Logistic Artificial Neural Network and trained it to predict cancer-related survival in patients with confirmed colorectal cancer by using a database (1,558 patients) made available for the study by the Information & Statistics Division of National Health Service Scotland. The reliability of the trained network was evaluated against Kaplan-Meier observed survival plots of a random sample of 300 patients not used in the training but forming part of the same data set. RESULTS: The predicted survival curves obtained as the output from the artificial neural network showed close agreement with observed actual survival rates of a cohort of 300 patients with four grades of risk of dying from the cancer within five years of diagnosis. CONCLUSIONS: The web-based Partial Logistic Artificial Neural Network system accurately predicts survival after staging and treatment of colonic cancer. It can be made web-accessible where it is powerful enough to serve hundreds of users simultaneously.
PURPOSE: An Internet-web-based artificial neural network has been developed for practicing clinical oncologists and medical researchers as part of an ongoing program designed for the implementation of advanced neural networks for prognostic estimates and eventually for management/treatment decisions in individual patients with colonic cancer. METHODS: An interdisciplinary team of academic oncologists and physicists has configured and implemented a Partial Logistic Artificial Neural Network and trained it to predict cancer-related survival in patients with confirmed colorectal cancer by using a database (1,558 patients) made available for the study by the Information & Statistics Division of National Health Service Scotland. The reliability of the trained network was evaluated against Kaplan-Meier observed survival plots of a random sample of 300 patients not used in the training but forming part of the same data set. RESULTS: The predicted survival curves obtained as the output from the artificial neural network showed close agreement with observed actual survival rates of a cohort of 300 patients with four grades of risk of dying from the cancer within five years of diagnosis. CONCLUSIONS: The web-based Partial Logistic Artificial Neural Network system accurately predicts survival after staging and treatment of colonic cancer. It can be made web-accessible where it is powerful enough to serve hundreds of users simultaneously.