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.
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.
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.
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.
Abbreviations:MVI (microvascular invasion), HCC (hepatocellular carcinoma), CAD (computer-assisted diagnosis), CNN (convolutional neural network), ViT (Vision Transformer), PFS (progression free survival), OS (overall survival), GRAD-CAM (gradient-weighted class activation mapping), ROI (region of interest), RFA (radiofrequency ablation), CT (computed tomography), MRI (magnetic resonance imaging), AUC (area under curve), NLP (natural language processing), SOTA (State-of-the-Art), AP (arterial phase), VP (venous phase), DP (delay phase), PACS (Picture Archiving and Communication Systems), LR (learning rate), ROC (receiver operating characteristic), CV (computer vision)
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Published online: February 02, 2023
Accepted: January 30, 2023
Received: August 23, 2022
Publication stageIn Press Corrected Proof
The paper is not based on a previous communication to a society or meeting.
© 2023 International Hepato-Pancreato-Biliary Association Inc. Published by Elsevier Ltd. All rights reserved.