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.
Keywords: Hepatocellular carcinoma; ultrasound; radiomics; deep learning; prognosis.