Yagin, Fatma HilalColak, CemilAl-Hashem, FahaidAlzakari, Sarah A.Alhussan, Amel AliAghaei, Mohammadreza2026-06-192026-06-1920252218-1989https://doi.org/10.3390/metabo15110716https://hdl.handle.net/20.500.12899/5164Background: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, often diagnosed at late stages due to the limited sensitivity of current screening tools. This study explores whether blood-based lipidomic profiling, combined with explainable artificial intelligence (XAI), can improve early and interpretable detection of HCC. Methods: We analyzed lipidomic data from 219 HCC patients and 219 matched healthy controls using liquid chromatography-mass spectrometry. An Explainable Boosting Machine (EBM) was employed to identify discriminatory lipid biomarkers and was compared against several standard machine learning algorithms. Results: The EBM model achieved superior performance with 87.0% accuracy, 87.7% sensitivity, 86.3% specificity, and an AUC of 91.8%, outperforming other models. Key lipid biomarkers identified included specific phosphatidylcholines (PC 38:2, PC 40:4), sphingomyelins (SM d40:2 B), and lysophosphatidylcholines (LPC 18:2), which exhibited significant alterations in HCC patients and highlighted disruptions in sphingolipid metabolism. Conclusions: Integration of lipidomics with explainable machine learning offers a powerful, transparent approach for HCC biomarker discovery, achieving high diagnostic accuracy while providing biological insights. This strategy holds promise for developing non-invasive, clinically interpretable screening tools to improve early detection of liver cancer.eninfo:eu-repo/semantics/openAccessHepatocellular CarcinomaLipidomicsBiomarkersMachine LearningExplainable Boosting MachineArticle10.3390/metabo151107161511412953022-s2.0-105022904473Q2WOS:001624176700001Q20000-0001-5406-098X0000-0002-9848-79580000-0001-7530-79610000-0001-5735-3825