ArticleBMC gastroenterology2017
A prediction model for the grade of liver fibrosis using magnetic resonance elastography.
Article in BMC gastroenterology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Plasma Arginase-1 Level Is Associated with the Mental Status of Outpatients with Chronic Liver Disease.Diagnostics (Basel, Switzerland) · 2021Article
- Effect of acupuncture combined with Shenqi Yigan Decoction on liver function and T cell subsets in patients with HBV-induced liver fibrosis.American journal of translational research · 2021Article
- Prediction of esophageal varices by liver and spleen MR elastography.European radiology · 2019Article
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9 authors.
Funding
Abstract
backgroundLiver stiffness measurement (LSM) has recently become available for assessment of liver fibrosis. We aimed to develop a prediction model for liver fibrosis using clinical variables, including LSM.
methodsWe performed a prospective study to compare liver fibrosis grade with fibrosis score. LSM was measured using magnetic resonance elastography in 184 patients that underwent liver resection, and liver fibrosis grade was diagnosed histologically after surgery. Using the prediction model established in the training group, we validated the classification accuracy in the independent test group.
resultsFirst, we determined a cut-off value for stratifying fibrosis grade using LSM in 122 patients in the training group, and correctly diagnosed fibrosis grades of 62 patients in the test group with a total accuracy of 69.3%. Next, on least absolute shrinkage and selection operator analysis in the training group, LSM (r = 0.687, P < 0.001), indocyanine green clearance rate at 15 min (ICGR15) (r = 0.527, P < 0.001), platelet count (r = -0.537, P < 0.001) were selected as variables for the liver fibrosis prediction model. This prediction model applied to the test group correctly diagnosed 32 of 36 (88.8%) Grade I (F0 and F1) patients, 13 of 18 (72.2%) Grade II (F2 and F3) patients, and 7 of 8 (87.5%) Grade III (F4) patients in the test group, with a total accuracy of 83.8%.
conclusionsThe prediction model based on LSM, ICGR15, and platelet count can accurately and reproducibly predict liver fibrosis grade.
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