Detection and Staging of Liver Fibrosis using Additive Logistic Models

Cristian Vicas, Sergiu Nedevschi, Monica Lupsor, and Radu Badea

Keywords

Liver fibrosis, non-invasive diagnosis, medical diagnostics, additive logistic regression models

Abstract

Fibrosis and cirrhosis are the main complications of chronic liver diseases. At present, liver biopsy is the golden standard for evaluating liver fibrosis. However, this is an invasive procedure, hence the interest in developing non–invasive approaches. The present study identifies novel possibilities for non-invasive fibrosis evaluation. We included 591 hepatitis C patients. Fibrosis was assessed using the Metavir score. A number of 93 features were obtained from each patient using B-mode ultrasound, Doppler ultrasound, transient elastography and common biochemical and cytological measurements. The patients were grouped according to fibrosis stages and additive logistic regression models were built. Cross-validation along with Area Under Curve (AUROC) was used to measure the classification performance. The AUROC of 0.90 was recorded when discriminating between fibrosis stage =3 and fibrosis stage 4.

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