Preoperative prediction of hepatocellular carcinoma microvascular invasion based on magnetic resonance imaging feature extraction artificial neural network

Figure 2 Tumor contours were labeled on each patient’s T1-weighted imaging plain image. After preprocessing, image features were acquired by pyradiomics, and then the correlation between features and microvascular invasion. was analyzed. Features with P value < 0.05 were selected as input to the artificial neural network. GLCM: Grey level co-occurrence matrix; GLRLM: Grey level run length matrix; GLSZM: Gray level size zone matrix.