Intelligent clinical decision supports for interferon treatment in chronic hepatitis C and B based on i-biopsy™

In chronic hepatitis C and B, which can progress to cirrhosis and liver cancer, Interferon is the only effective treatment, for carefully selected patients, but it is very expensive. Some of the selection criteria are based on liver biopsy, an invasive, costly and painful medical procedure. Developing an efficient selection system, based on non-invasive medical procedures, could be in the patients benefit and also save money. We investigated the capability of a knowledge discovery in data approach, using computational intelligence tools, to integrate information from various non-invasive data sources - imaging, clinical, and laboratory data - to assist the interferon therapeutical decision, mainly by predicting with acceptable accuracy the results of the biopsy. The resulted intelligent systems, tested on 700 patients with chronic hepatitis C and 500 patients with chronic hepatitis B, based on C5.0 decision trees and boosting, predict with 100% accuracy the results of the liver biopsy. Also, by integrating other patients selection criteria, they offer a non-invasive support for the correct Interferon therapeutic decision. To our best knowledge, these decision systems outperformed all similar systems published in the literature and offer a realistic opportunity to replace liver biopsy in this medical context.