A prediction model for the grade of liver fibrosis using magnetic resonance elastography

• Main Authors: Yusuke Mitsuka, Yutaka Midorikawa, Hayato Abe, Naoki Matsumoto, Mitsuhiko Moriyama, Hiroki Haradome, Masahiko Sugitani, Shingo Tsuji, Tadatoshi Takayama
• Format: Article
• Language: English
• Published: BMC 2017-11-01
• Series: BMC Gastroenterology
• Subjects: Liver fibrosis; Prediction model; Liver stiffness measurement; Magnetic resonance elastography
• Online Access: http://link.springer.com/article/10.1186/s12876-017-0700-z

Abstract Background Liver 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. Methods We 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. Results First, 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%. Conclusions The prediction model based on LSM, ICGR15, and platelet count can accurately and reproducibly predict liver fibrosis grade.