Hepatic tumors pose significant diagnostic challenges due to their diverse biological behaviors and the inherent limitations of traditional imaging methods. These limitations often result in delayed or imprecise diagnoses, especially in resource-limited settings where advanced tools are not readily available. This study investigates the application of radiomics and Machine Learning (ML) to address these challenges, focusing on the classification of hepatic tumors and the prediction of early recurrence in Hepatocellular Carcinoma (HCC).

Radiomic features were extracted from multi-phase CT images and used to train machine learning models, achieving an accuracy of 80% in tumor classification and 71% in HCC recurrence prediction. To ensure clinical relevance and usability, the models were integrated into a user-friendly web application. This integration emphasizes the feasibility of applying advanced machine learning techniques in practical, real-world medical contexts, highlighting their potential to enhance diagnostic precision, especially in resource-constrained

environments.