Interpretable radiomics model based on magnetic resonance imaging to predict responses to transarterial chemoembolization for hepatocellular carcinoma

Figure 3 Feature selection via least absolute shrinkage and selection operator regression. A: Least absolute shrinkage and selection operator regression coefficient path diagram. As the penalty parameter increases, the feature coefficients gradually decrease toward zero. The features with nonzero coefficients at the λ (1-standard error) line were ultimately selected, resulting in 3, 2, and 1 optimal feature from the features of the fat-saturation T2-weighted imaging, arterial phase, and portal venous phase datasets for subsequent model construction; B: Least absolute shrinkage and selection operator regression parameter diagram. The two vertical dashed lines indicate the selected values using cross-validation: The optimal value was obtained by applying the minimum criteria and 1 of the minimum criteria (1-standard error criteria).