Integrating radiomics and machine learning for the diagnosis and prognosis of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a prevalent and aggressive liver cancer that poses significant challenges in diagnosis and prognosis. Recent advancements in radiomics and machine learning (ML) offer promising solutions to enhance the accuracy of HCC diagnosis, treatment response prediction, and survival prognosis. Radiomics, which extracts quantitative features from medical images, captures the complex tumor heterogeneity that is often undetectable with traditional imaging methods. When combined with ML algorithms, these features can be used to differentiate between various stages of HCC, predict treatment outcomes, and assess long-term survival. This review explores key radiomic features, including texture, shape, and intensity, and their integration with ML techniques like binary classification models, XGBoost, LightGBM, and deep learning architectures. We also discuss the challenges faced in model interpretation, data heterogeneity, and the integration of multi-modal data. Despite the promising potential of these technologies, the clinical adoption of radiomics and ML models in HCC management will require overcoming these obstacles through standardization and improved interpretability.