Negrini Davide, Zecchin Patrick, Ruzzenente Andrea, Bagante Fabio, De Nitto Simone, Gelati Matteo, Salvagno Gian Luca, Danese Elisa, Lippi Giuseppe
Department of Laboratory Medicine, University-Hospital of Padova, 35128 Padova, Italy.
Clinical Biochemistry Section, Department of Neurological, Biomedical and Movement Sciences, University of Verona, 37134 Verona, Italy.
Diagnostics (Basel). 2020 Aug 2;10(8):551. doi: 10.3390/diagnostics10080551.
Bile acids (BAs) assessments are garnering increasing interest for their potential involvement in development and progression of cholangiocarcinoma (CCA). Since machine learning (ML) algorithms are increasingly used for exploring metabolomic profiles, we evaluated performance of some ML models for dissecting patients with CCA or benign biliary diseases according to their plasma BAs profiles. We used ultra-performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) for assessing plasma BAs profile in 112 patients (70 CCA, 42 benign biliary diseases). Twelve normalisation procedures were applied, and performance of six ML algorithms were evaluated (logistic regression, k-nearest neighbors, naïve bayes, RBF SVM, random forest, extreme gradient boosting). Naïve bayes, using direct bilirubin concentration for normalisation of BAs, was the ML model displaying better performance in the holdout set, with an Area Under Curve (AUC) of 0.95, 0.79 sensitivity, 1.00 specificity. This model, also characterised by 1.00 positive predictive value and 0.73 negative predictive value, displayed a globally excellent accuracy (86.4%). The accuracy of the other five models was lower, and AUCs ranged 0.75-0.95. Preliminary results of this study show that application of ML to BAs profile analysis can provide a valuable contribution for characterising bile duct diseases and identifying patients with higher likelihood of having malignant pathologies.
胆汁酸（BAs）评估因其可能参与胆管癌（CCA）的发生和发展而越来越受到关注。由于机器学习（ML）算法越来越多地用于探索代谢组学特征，我们根据血浆BAs特征评估了一些ML模型对CCA患者或良性胆道疾病患者进行分类的性能。我们使用超高效液相色谱串联质谱法（UHPLC-MS/MS）评估了112例患者（70例CCA，42例良性胆道疾病）的血浆BAs特征。应用了12种归一化程序，并评估了六种ML算法的性能（逻辑回归、k近邻、朴素贝叶斯、径向基函数支持向量机、随机森林、极端梯度提升）。使用直接胆红素浓度对BAs进行归一化的朴素贝叶斯是在验证集中表现更好的ML模型，曲线下面积（AUC）为0.95，灵敏度为0.79，特异性为1.00。该模型的阳性预测值为1.00，阴性预测值为0.73，整体准确率很高（86.4%）。其他五个模型的准确率较低，AUC范围为0.75 - 0.95。本研究的初步结果表明，将ML应用于BAs特征分析可为胆管疾病的特征描述和识别具有更高恶性病变可能性的患者提供有价值的贡献。
