RT Journal Article
T1 FibroGENE: A gene-based model for staging liver fibrosis.
A1 Eslam, Mohammed
A1 Hashem, Ahmed M
A1 Romero-Gomez, Manuel
A1 Berg, Thomas
A1 Dore, Gregory J
A1 Mangia, Alessandra
A1 Chan, Henry Lik Yuen
A1 Irving, William L
A1 Sheridan, David
A1 Abate, Maria Lorena
A1 Adams, Leon A
A1 Weltman, Martin
A1 Bugianesi, Elisabetta
A1 Spengler, Ulrich
A1 Shaker, Olfat
A1 Fischer, Janett
A1 Mollison, Lindsay
A1 Cheng, Wendy
A1 Nattermann, Jacob
A1 Riordan, Stephen
A1 Miele, Luca
A1 Kelaeng, Kebitsaone Simon
A1 Ampuero, Javier
A1 Ahlenstiel, Golo
A1 McLeod, Duncan
A1 Powell, Elizabeth
A1 Liddle, Christopher
A1 Douglas, Mark W
A1 Booth, David R
A1 George, Jacob
A1 International Liver Disease Genetics Consortium (ILDGC), 
K1 Chronic hepatitis B
K1 Chronic hepatitis C
K1 Data mining analysis
K1 Fibrosis
K1 IFNL
K1 NASH
K1 Non-alcoholic steatohepatitis
AB The extent of liver fibrosis predicts long-term outcomes, and hence impacts management and therapy. We developed a non-invasive algorithm to stage fibrosis using non-parametric, machine learning methods designed for predictive modeling, and incorporated an invariant genetic marker of liver fibrosis risk. Of 4277 patients with chronic liver disease, 1992 with chronic hepatitis C (derivation cohort) were analyzed to develop the model, and subsequently validated in an independent cohort of 1242 patients. The model was assessed in cohorts with chronic hepatitis B (CHB) (n=555) and non-alcoholic fatty liver disease (NAFLD) (n=488). Model performance was compared to FIB-4 and APRI, and also to the NAFLD fibrosis score (NFS) and Forns' index, in those with NAFLD. Significant fibrosis (⩾F2) was similar in the derivation (48.4%) and validation (47.4%) cohorts. The FibroGENE-DT yielded the area under the receiver operating characteristic curve (AUROCs) of 0.87, 0.85 and 0.804 for the prediction of fast fibrosis progression, cirrhosis and significant fibrosis risk, respectively, with comparable results in the validation cohort. The model performed well in NAFLD and CHB with AUROCs of 0.791, and 0.726, respectively. The negative predictive value to exclude cirrhosis was>0.96 in all three liver diseases. The AUROC of the FibroGENE-DT performed better than FIB-4, APRI, and NFS and Forns' index in most comparisons. A non-invasive decision tree model can predict liver fibrosis risk and aid decision making.
YR 2015
FD 2015-12-01
LK http://hdl.handle.net/10668/9627
UL http://hdl.handle.net/10668/9627
LA en
DS RISalud
RD Apr 6, 2025