Novel algorithm for non-invasive assessment of fibrosis in NAFLD.
Novel algorithm for non-invasive assessment of fibrosis in NAFLD.
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DOI：
10.1371/journal.pone.0062439
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Canbay A
中科院分区：
文献类型：
--
作者：
Sowa JP;Heider D;Bechmann LP;Gerken G;Hoffmann D;Canbay A
肝脏疾病的各种情况和肝活检的缺点要求非侵入性选择来评估肝纤维化。非侵入性评分对于识别具有缓慢进展的纤维化过程的患者特别有用，如非酒精性脂肪性肝病（NAFLD），其应接受纤维化的组织学检查。通过机器学习技术（逻辑回归、k-最近邻、线性支持向量机、基于规则的系统、决策树和随机森林（RF））分析了126例因病态肥胖症接受减肥手术的患者的经典肝脏血清参数、透明质酸（HA）和细胞死亡标志物。评估评估数据集预测纤维化的特异性、敏感性和准确性。纤维化评分为1或2的患者之间的单个参数（ALT、AST、M30、M60、HA）均无显著差异。然而，使用RF结合这些参数预测纤维化的准确性达到79%，灵敏度超过60%，特异性为77%。此外，RF确定细胞死亡标志物M30和M65比经典肝脏参数对决策更重要。基于血清参数，即使只有少量纤维化组织可用，纤维化评分系统的生成似乎也是可行的。应进行新标志物（即细胞死亡参数）的前瞻性评价，以确定最佳纤维化预测因子集。
Various conditions of liver disease and the downsides of liver biopsy call for a non-invasive option to assess liver fibrosis. A non-invasive score would be especially useful to identify patients with slow advancing fibrotic processes, as in Non-Alcoholic Fatty Liver Disease (NAFLD), which should undergo histological examination for fibrosis. Classic liver serum parameters, hyaluronic acid (HA) and cell death markers of 126 patients undergoing bariatric surgery for morbid obesity were analyzed by machine learning techniques (logistic regression, k-nearest neighbors, linear support vector machines, rule-based systems, decision trees and random forest (RF)). Specificity, sensitivity and accuracy of the evaluated datasets to predict fibrosis were assessed. None of the single parameters (ALT, AST, M30, M60, HA) did differ significantly between patients with a fibrosis score 1 or 2. However, combining these parameters using RFs reached 79% accuracy in fibrosis prediction with a sensitivity of more than 60% and specificity of 77%. Moreover, RFs identified the cell death markers M30 and M65 as more important for the decision than the classic liver parameters. On the basis of serum parameters the generation of a fibrosis scoring system seems feasible, even when only marginally fibrotic tissue is available. Prospective evaluation of novel markers, i.e. cell death parameters, should be performed to identify an optimal set of fibrosis predictors.
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