Contrast-Enhanced Ultrasound Liver Imaging Reporting and Data System in Hepatocellular Carcinoma ≤5 cm: Biological Characteristics and Patient Outcomes.

Introduction: The present study aimed to evaluate the influence of biological characteristics of hepatocellular carcinoma (HCC) on the Liver Imaging Reporting and Data System (LI-RADS) v2017 category of contrast-enhanced ultrasound (CEUS) in patients with high risk and compare the outcomes among different categories after radical resection. Methods: Between June 2017 and December 2020, standardized CEUS data of liver nodules were prospectively collected from multiple centers across China. We conducted a retrospective analysis of the prospectively collected data on HCCs measuring no more than 5 cm, as diagnosed by pathology. LI-RADS categories were assigned after thorough evaluation of CEUS features. Then, CEUS LI-RADS categories and major features were compared in different differentiation, Ki-67, and microvascular invasion (MVI) statuses. Differences in recurrence-free survival (RFS) among different LI-RADS categories were further analyzed. Results: A total of 293 HCC nodules in 293 patients were included. This study revealed significant differences in the CEUS LI-RADS category of HCCs among differentiation (p < 0.001) and levels of Ki-67 (p = 0.01) and that poor differentiation (32.7% in LR-M, 12% in LR-5, and 6.2% in LR-4) (p < 0.001) and high level of Ki-67 (median value 30%) were more frequently classified into the LR-M category, whereas well differentiation (37.5% in LR-4, 15.1% in LR-5, and 11.5% in LR-M) and low levels of Ki-67 (median value 11%) were more frequently classified into the LR-4 category. No significant differences were found between MVI and CEUS LI-RADS categories (p > 0.05). With a median follow-up of 23 months, HCCs assigned to different CEUS LI-RADS classes showed no significant differences in RFS after resection. Conclusions: Biological characteristics of HCC, including differentiation and level of Ki-67 expression, could influence major features of CEUS and impact the CEUS LI-RADS category. HCCs in different CEUS LI-RADS categories showed no significant differences in RFS after resection.

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

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.

Managing the water-energy-food nexus in China by adjusting critical final demands and supply chains: An input-output analysis.

The rapid population growth in China has increased the demand for limited water, energy and food resources. Because the resource supply is constrained by future uncertainties such as climate change, it is necessary to examine the connections among water, energy and food resources from the perspective of the relevant final demands. Based on an input-output model and structural path analysis, this study aims to explore the hidden connections among water, energy and food resources by identifying important final demands and examine how these resources are embodied in upstream production and downstream consumption processes along the supply chain. The water-energy-food nexus approach in this research identifies where and how these resources intersect in economic sectors. By simultaneously considering the water, energy and food footprints, synergistic effects can be maximized among these resource systems. The results reveal that urban household consumption and fixed capital formation have large impacts on water-energy-food resources. Besides, agriculture, construction and service sectors have the largest water-energy-food footprints. For each resource, we rank the top-20 supply chain paths from the final demands to the upstream production sectors, and six critical supply chain paths are identified as important contributors to the consumption of all these resources. Compared with independent approach to manage water, energy and food resources, the nexus approach identifies the critical linkages of the water, energy and food systems and helps to formulate integrated policies to effectively manage these resources across sectors and actors. Synergistic strategies for conserving water, energy, and food resources can be achieved through avoiding unnecessary waste in end uses and improving resource use efficiency along critical supply chains. This research can help consumers, industries and the government make responsible consumption and production decisions to conserve water, energy and food resources.