Optimizing Liver Stiffness Assessment in HCV Patients: A Machine Learning Approach to Identify Confounding Factors in Fibrosis Estimation

Hepatitis C Virus (HCV) infection is a significant global health concern with approximately 1.5 million new infections yearly. The choice of the most appropriate HCV treatment depends on several factors, including liver fibrosis status. Current guidelines recommend liver fibrosis evaluation using non-invasive techniques such as Liver Stiffness Measurement (LSM) using liver elastography. Although LSM revolutionized patient care in the last decade, allowing biopsy-free treatments, several factors can lead to overestimation or underestimation of liver stiffness values, affecting management strategies. This study presents a machine-learning approach using an eXtreme Gradient Boosting model to predict possible LSM inaccuracies in a cohort of 509 HCV-positive treated patients. The dataset, characterized by 55 variables, underwent feature reduction and balancing to mitigate class imbalance to train the predictive algorithm. The developed model can identify inaccuracy in LSM and achieves an accuracy of 88.0% on the training set and 92.0% on the test set. Furthermore, it exhibited a consistent mean Area Under the Curve One-vs-One (AUC-ovo) of 0.97 across both datasets. The model’s performance in predicting abnormal LSM may enable healthcare providers to tailor treatment plans more precisely, optimize patient follow-up, and reduce unnecessary invasive procedures. These findings highlight the potential of machine learning in improving patient care in the context of chronic HCV management.