Integrating molecular, biochemical, and immunohistochemical features as predictors of hepatocellular carcinoma drug response using machine-learning algorithms

Notably, (NN) achieved the best prediction accuracy where the combined model using molecular and biochemical features exhibited exceptional predictive power, achieving solid accuracy of 0.9693 ∓ 0.0105 and average area under the ROC curve (AUC) of 0.94 ∓ 0.06 coming from three cross-validation iterations. Also, found seven molecular features, seven biochemical features, and one immunohistochemistry feature as promising biomarkers of treatment response. This comprehensive method has the potential to significantly advance personalized HCC therapy by allowing for more precise drug response estimation and assisting in the identification of effective treatment strategies.