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

Autor: Jia-peng Wu, Wen-zhen Ding, Yu-ling Wang, Sisi Liu, Xiao-qian Zhang, Qi Yang, Wen-jia Cai, Xiao-ling Yu, Fang-yi Liu, Dexing Kong, Hui Zhong, Jie Yu, Ping Liang
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: International Journal of Hyperthermia, Vol 39, Iss 1, Pp 595-604 (2022)
Druh dokumentu: article
ISSN: 02656736
1464-5157
0265-6736
DOI: 10.1080/02656736.2022.2062463
Popis: 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
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