Hepatocellular carcinoma (HCC) is the sixth most common cancer in the world and the second most common cause of cancer-related death (1), and the incidence rate is increasing year by year (2). For early HCC patients, surgical resection is still an effective treatment (3). However, HCC is prone to relapse and metastasis, and the prognosis is poor (4, 5). Some studies have shown that recurrence is closely related to pathological manifestation (6). Several studies have shown that Edmondson-Steiner grade is an essential preoperative predictor of HCC (7, 8). For low-grade HCC with isolated lesions ≤ 2cm, the 3-year and 5-year relapse-free survival rates were 64% and 50%, respectively, while the 3-year and 5-year relapse-free survival rates for high-grade HCC were 39% and 29%, respectively (9, 10). According to the research, the recurrence rate of high-grade HCC is higher than that of low-grade HCC (11, 12). Moreover, compared with high-grade HCC, low-grade HCC has a higher surgical cure rate (13, 14) and higher short-term and long-term survival rates. Therefore, accurate prediction of the Edmondson-Steiner grade of HCC is highly significant for clinical decision-making, treatment plan optimization, and prognosis prediction (15–18).

Postoperative histopathological examination of resected tumor specimens is the gold standard for diagnosis and pathological grading of HCC (19). A needle biopsy can provide preliminary pathological grading before the operation. However, the risk of complications and misdiagnosis must always be considered (20). At present, some studies have shown that imaging examination may have the potential to reflect pathological grading (21, 22). MRI combined with liver-specific contrast agents, and diffusion-weighted imaging (DWI) has been proven to help evaluate the histological grading of HCC (23–25). Wu et al. showed that the radiomics characteristics of non-enhanced MRI and clinical factors helped predict the HCC grade before operation (26). The area under the ROC curve (AUC) of the best model for HCC grading prediction was 0.80. Jiseon et al. believed that CT texture analysis can provide texture features significantly related to higher tumor grade (27). Seitz et al. believe that CEUS and MRI have equal value for the differentiation and regulation of newly discovered liver tumors in clinical practice. Contrast-enhanced ultrasound (CEUS) and MRI are reliable for differentiating benign and malignant lesions and diagnosing hepatic hemangiomas and FNH. Characterization of metastasis and hepatocellular carcinoma is also very reliable (28). Compared with CT and MRI, CEUS provides dynamic perfusion images with fewer application restrictions (29). Liu et al. report that CEUS can give helpful information for the differential diagnosis of HCC, indicating its use in clinical practice (30). Wang et al. showed that the combination of the radiomics model of gray scale ultrasound and contrast-enhanced ultrasound with clinical information has potential clinical value for tumor grading (31). However, the quality of ultrasonic imaging depends significantly on the operator. Most of the previous studies are based on subjective feature recognition, which largely depends on the experience of doctors. These features may reveal pathological features that the naked eye can recognize (32).

Radiomics has been increasingly applied to the field of medical image analysis. It extracts massive amounts of information from radiological images for more in-depth mining, prediction, and analysis to help doctors diagnose more accurately. This study aimed to explore the value of sonographic features in predicting the Edmondson-Steiner grade of HCC before surgery.

Experienced doctors conducted the ROI segmentation. Figure 3 shows a segmentation example of a patient in different image phases.

A total of 1338 features were extracted from the 3D CEUS images, and the number of selected features was 987 by the independent sample t-test. Then, after the selection by LASSO, ten features from seven categories were chosen finally: First Order Statistics, Shape-based (3D), GLCM, GLRLM, GLSZM, GLDM, and NGTDM. See Figure 4 .

Based on the selected features, five single prediction models, LR, RF, KNN, NB, and XGB, were constructed by using a five-fold cross-validation method in the training set. Then, SVM was used as the meta-classifier to build the integrated prediction model. Table 1 shows the performance index in the training set, and Table 2 shows the performance in the testing set of all the models. The corresponding ROCs can be found in Figure 5 .

Although significant progress has been made in treating HCC in recent years, the postoperative recurrence rate is still high, leading to poor clinical prognosis (35). Pathological grading is one of the critical factors in determining the postoperative recurrence of HCC. Compared with low-grade, high-grade HCC has a higher recurrence rate and shorter survival period, requiring expanded surgical margins and more frequent postoperative follow-up (36). Accurate preoperative prediction of pathological grading can provide a theoretical basis for individualized treatment decisions. However, the evaluation of HCC grading in clinical practice can only rely on pathological analysis of tumor tissue obtained after liver resection or puncture, which limits its value in guiding treatment decision-making before surgery.

Imaging examination is essential for diagnosing and preoperatively evaluating HCC. The commonly used contrast-enhanced imaging techniques in clinical practice include contrast-enhanced CT (CECT), contrast-enhanced MRI (CEMRI), hepatobiliary-specific MRI, and contrast-enhanced ultrasound (CEUS). Among the above four image types, CEUS differs from CECT, CEMRI, and hepatobiliary-specific MRI. The contrast agent of CEUS is a pure-blood pool contrast agent, which can accurately display the vascular architecture within the tumor. In addition, CEUS belongs to three-dimensional temporal imaging, which can reflect real-time changes in blood flow perfusion of tumor tissue (37). Previous studies have shown that factors such as tumor diameter, boundary, echo, clearance time, clearance degree, and LI-RADS classification of CEUS are related to the pathological grading of HCC, but their predictive ability is still weak (38–40).

Radiomics has been applied to the diagnosis of HCC and postoperative efficacy evaluation, and other aspects by analyzing existing images of patients without additional examinations or increasing the economic burden on patients (41, 42). This study used CEUS to predict the Edmondson-Steiner grading of HCC before operation. First, we extracted the Radiomics features from the enhanced three phases CEUS images and reduced the dimension of the features using independent sample t-test and LASSO. Next, five individual classifiers were trained using the five-fold cross-validation, and then the classifiers were integrated and obtained a prediction model with good performance.

The experimental results show that the prediction performance of the stacking ensemble model is much better than that of the individual classifier prediction model. For example, from Table 2 , we can find that the accuracy and AUC of the ensemble model can reach 0.891 and 0.931, respectively.

The results of the additional testing set show that the performance of KNN compared to all other models, as shown in Figure 6 and Table 3 , has an accuracy and AUC of 0.901 and 0.940. The ensemble model also showed a good performance with an AUC of 0.937.

Radiomics is a new field that aims to explore the potential relationship between medical images and tumor cell phenotypic characteristics in a non-invasive manner. According to previous studies, radiomics plays an essential role in tumor grading. Table 4 lists recent research on the Edmondson-Steiner grading of HCC.

This study innovatively selected three representative phases of images from CEUS as the objects for feature extraction and analysis. By using ensemble learning methods, the classification performance has been significantly improved compared to existing studies. See Table 4 .

Compared with enhanced CT and MRI, CEUS is real-time dynamic imaging, which has the advantages of low cost, safety, portability, and non-radiation, incomparable to other imaging technologies. The CEUS-based diagnosis is promising and can be used more widely in clinical practice.

However, our research has some limitations. First, we only classified HCC patients into two types: high-grade and low-grade. In the future study, the sample size will be further expanded, and the value of Radiomics for multi-category classification of HCC in CEUS will be further explored. Secondly, most of the enrolled patients had a history of viral hepatitis. The robustness of the proposed method needs to be verified in heterogeneous liver diseases with other high-risk factors of HCC. Third, we selected three phases of CEUS images by experienced doctors, which may have errors caused by subjective factors. Fourth, the ultrasound images came from a variety of different devices. The difference in ultrasonic parameters between patients could impact the experimental results. Fifth, our data comes from one center. In the future, we will take multi-center research to improve our experiments’ generalization performance.

This study proposed an automated Edmondson-Steiner grading method for HCC with CEUS. Based on three-dimensional dynamically enhanced ultrasound images, we used Radiomics methods to extract image bio-markers, made effective feature selection, and then used the integrated classifier to build a prediction model. The experimental results show that the method proposed in this study has good classification performance on independent testing sets, which can provide quantitative analysis support for clinical decision-making.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

The studies involving human participants were reviewed and approved by the Shengjing Hospital of China Medical University Ethics Committee. The patients/participants provided their written informed consent to participate in this study.

YW presented clinical problems, completed data collection, drawing segmentation labels, and establishing and evaluating models. DY and FL completed image data processing, model building and optimization. HS and XP helped applying the Radiomics and machine learning method. YH confirms the accuracy of segmentation labels and guides clinical related issues. HL and ST are responsible for the overall design and guidance. All authors contributed to the article and approved the submitted version.