Deep learning-based radiomics based on contrast-enhanced ultrasound predicts early recurrence and survival outcome in hepatocellular carcinoma
Zhe Huang, Zhu Shu, Rong-Hua Zhu, Jun-Yi Xin, Lingling Wu, Hanzhang Wang, Jun Chen, Zhiwei Zhang, Hongchang Luo, Kaiyan Li
Abstract
BACKGROUND: Hepatocellular carcinoma (HCC) is the most common primary liver malignancy. AIM: To predict early recurrence (ER) and overall survival (OS) in patients with HCC after radical resection using deep learning-based radiomics (DLR). METHODS: A total of 414 consecutive patients with HCC who underwent surgical resection with available preoperative grayscale and contrast-enhanced ultrasound images were enrolled. The clinical, DLR, and clinical + DLR models were then designed to predict ER and OS. RESULTS: = 0.005). CONCLUSION: Compared to the clinical model, the clinical + DLR model significantly improves the accuracy of predicting OS in HCC patients after radical resection.