ArticleDiagnostics (Basel, Switzerland)2020
Machine Learning Model Comparison in the Screening of Cholangiocarcinoma Using Plasma Bile Acids Profiles.
Article in Diagnostics (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Predicting Iron Deficiencies Using Routine Complete Blood Cell Count Parameters: A Machine Learning Approach and Evaluation.Journal of clinical medicine · 2026Article
- The Use and Perceptions of AI Chatbots in Medical Research: An International Cross-Sectional Survey.Cureus · 2026Article
- The role of bile acid metabolism-related genes in prognosis assessment of hepatocellular carcinoma and identification of NPC1 as a biomarker.Frontiers in endocrinology · 2025Article
- Application of AI on cholangiocarcinoma.Frontiers in oncology · 2024Review
- Bile acids as drivers and biomarkers of hepatocellular carcinoma.World journal of hepatology · 2022Review
- Novel approaches in search for biomarkers of cholangiocarcinoma.World journal of gastroenterology · 2022Review
- Artificial intelligence and cholangiocarcinoma: Updates and prospects.World journal of clinical oncology · 2022Review
- Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology.World journal of gastroenterology · 2021Review
- Plasma Bile Acid Profile in Patients with and without Type 2 Diabetes.Metabolites · 2021Article
- Establishment of a Potential Serum Biomarker Panel for the Diagnosis and Prognosis of Cholangiocarcinoma Using Decision Tree Algorithms.Diagnostics (Basel, Switzerland) · 2021Article
- Circulating Bile Acids Profiles in Obese Children and Adolescents: A Possible Role of Sex, Puberty and Liver Steatosis.Diagnostics (Basel, Switzerland) · 2020Article
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9 authors.
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Abstract
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.
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