AI-Driven Approach for Diagnosis and Treatment Selection for Hepatocellular Carcinoma

Abstract

Hepatocellular carcinoma (HCC) stands as the pre-vailing primary liver tumor, marked by elevated mortality rates. Around 80 % of instances emerge within cirrhotic livers, presenting a considerable obstacle in determining suitable therapeutic approaches. HCC can be classified into various categories, and by utilizing Barcelona Clinic Liver Cancer (BCLC) and Child- Pugh score stages, survival rate and duration for patients can be determined, as well as appropriate treatment plans devised. Several contributions are made by this paper. Firstly, the study partners with a hepatologist to pinpoint pertinent patient biomarkers used in forecasting Child-Pugh scores and BCLC stages. Subsequently, leveraging these predictions of stages and scores, personalized treatment strategies are suggested for patients. The dataset from the National Liver Institute in Egypt, comprising 1108 of patient records with 22 features, is evaluated using various machine learning models such as Random Forest, Gradient Boosting, Decision Trees, and Support Vector Machines (SVM). Results show that the Random Forest model achieves 92.77% accuracy in predicting Child-Pugh scores, while Gradient Boosting achieves 83.82 % accuracy in predicting BCLC stages. Subsequently, treatment plans are determined based on these stage and score predictions.