An Interpretable Machine Learning Model for the Differentiation of Liver Cysts and Liver Tumors Based on Computed Tomography (CT) Imaging

Abstract

Background: Metastatic liver tumors (MLT) and parasitic liver cysts (PLC) are common liver conditions that often exhibit similar imaging characteristics, making accurate diagnosis challenging using imaging alone. This overlap can result in diagnostic errors and delayed treatment, particularly in resource-limited settings or when invasive procedures such as biopsies are not feasible due to risk or unavailability. This study aimed to develop a reliable and transparent machine learning approach to distinguish MLT from PLC using radiomic features derived from computed tomography (CT). Methods: We propose an explainable radiomics-based machine learning framework for the non-invasive, accurate, and interpretable discrimination of MLT and PLC, designed to assist radiologists in reducing diagnostic ambiguity and expediting patient management. This retrospective study included 30 adult patients, comprising 15 with liver metastases and 15 with pathologic hepatic cysts. Radiomic features were extracted from pre-treatment CT scans using PyRadiomics. Feature selection was performed using three complementary methods: Mutual Information, Lasso regression, and LightGBM importance ranking. HistGradientBoosting classifiers were then trained on each selected feature set. Results: Model performance was evaluated using 5-fold cross-validation and assessed with ROC AUC, accuracy, precision, recall, and F1-score. SHAP analysis was applied to interpret the models and identify key radiomic biomarkers. Statistical comparisons were performed using DeLong’s test for AUCs, McNemar’s test for classification agreement, and paired t-tests for metrics such as accuracy and F1-score. The Mutual Information-based model achieved the highest mean AUC (0.9717 ± 0.0267), significantly outperforming the other models (p < 0.035). Key features contributing to classification included texture entropy, interquartile range, and gray level non-uniformity. Conclusion: We developed a robust and interpretable machine learning framework for differentiating metastatic liver tumors from parasitic liver cysts using CT-derived radiomic features. The integration of Mutual Information feature selection, ensemble learning, and SHAP explainability ensured high diagnostic accuracy, strong calibration, and transparency. The proposed framework demonstrates substantial clinical relevance and holds promise for real-world implementation.