Radiomics analysis of ultrasound to predict recurrence of hepatocellular carcinoma after microwave ablation

International Journal of Hyperthermia

Sisi Liu¹  Fang-yi Liu¹  Wen-zhen Ding¹  Wen-jia Cai¹  Xiao-ling Yu¹  Jie Yu¹  Yu-ling Wang¹  Qi Yang²  Hui Zhong³  Xiao-qian Zhang⁴  Dexing Kong⁴  Jia-peng Wu⁵  Ping Liang⁵ 

[1] Department of Interventional Ultrasound, Chinese PLA General Hospital, Beijing, China;Department of Medical Ultrasound, Peking University Shenzhen Hospital, Shenzhen, China;Key Laboratory of Biomedical Information Engineering of Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi' an Jiaotong University, Xi' an, China;School of Mathematical Sciences, Zhejiang University, Hangzhou, China;School of Medicine, Nankai University, Tianjin, China;

关键词: Hepatocellular carcinoma;    ultrasound;    radiomics;    deep learning;    prognosis;   

DOI  :  10.1080/02656736.2022.2062463

来源: DOAJ

【 摘 要 】

Objective To develop and validate an ultrasonic radiomics model for predicting the recurrence and differentiation of hepatocellular carcinoma (HCC). Convolutional neural network (CNN) ResNet 18 and Pyradiomics were used to analyze gray-scale-ultrasonic images to predict the prognosis and degree of differentiation of HCC.Methods This retrospective study enrolled 513 patients with HCC who underwent preoperative grayscale-ultrasonic imaging, and their clinical characteristics were observed. Patients were randomly divided into training (n = 413) and validation (n = 100) cohorts. CNN ResNet 18 and Pyradiomics were used to analyze ultrasonic images of HCC and peritumoral images to develop a prognostic and differentiation model. Clinical characteristics were integrated into the radiomics model and patients were stratified into high- and low-risk groups. The predictive effect was evaluated using the C-index and receiver operating characteristic (ROC) curve.Results The model combined with ResNet 18 and clinical characteristics achieved a good predictive ability. The C-indices of early recurrence (ER), late recurrence (LR), and recurrence-free survival (RFS) were 0.695 (0.561–0.789), 0.715 (0.623–0.800) and 0.721 (0.647–0.795), respectively, in the validation cohort, which was superior to the clinical model and ultrasonic semantic model. The model could stratify patients into high- and low-risk groups, which showed significant differences (p < 0.001) in ER, LR, and RFS. The area under the curve for predicting the degree of HCC differentiation was 0.855 and 0.709 in the training and validation cohorts, respectively.Conclusion We developed and validated a radiomics model to predict HCC recurrence and HCC differentiation, which could also acquire pathological information in a noninvasive manner.KEY RESULTSA hepatocellular carcinoma (HCC) prognostic prediction model was developed and validated by convolutional neural network (CNN) ResNet 18-based gray-scale ultrasound (US).A differentiation of HCC prediction model was developed for preoperative prediction avoiding invasive operation.Compared with Pyradiomics, CNN ResNet was more suitable for extracting information from US images.

【 授权许可】

Unknown