The utility of Vision Transformer in preoperatively predicting microvascular invasion status of hepatocellular carcinoma.

BACKGROUND:Microvascular invasion (MVI) is a risk factor for early recurrence and poor prognosis of hepatocellular carcinoma (HCC). Preoperative assessment of MVI status is beneficial for clinical therapy and prognosis evaluation. METHODS:A total of 305 surgically resected patients were included retrospectively. All recruited patients underwent plain and contrast-enhanced abdominal CT. They were then randomly divided into training and validation sets in a ratio of 8:2. Self-attention-based ViT-B/16 and ResNet-50 analyzed CT images to predict MVI status preoperatively. Then, Grad-CAM was used to generate an attention map showing the high-risk MVI patches. Using five-fold cross validation, the performance of each model was evaluated. RESULTS:Among 305 HCC patients, 99 patients were pathologically MVI-positive and 206 were MVI-negative. ViT-B/16 with fusion phase predicted the MVI status with an AUC of 0.882 and an accuracy of 86.8% in the validation set, which is similar to ResNet-50 with an AUC of 0.875 and an accuracy of 87.2%. The fusion phase improved performance a bit as compared to the single phase used for MVI prediction. The influence of peritumoral tissue on predictive ability was limited. A color visualization of the suspicious patches where microvascular has invaded was presented by attention maps. CONCLUSION:ViT-B/16 model can predict preoperative MVI status in CT images of HCC patients. Assisted by attention maps, it can assist patients in making tailored treatment decisions.