RESUMO
Background: Ultrasound-guided microwave ablation (MWA) has become a popular method for treating malignant liver tumors. However, few studies have investigated its use in the treatment of hepatoalveolar echinococcosis (HAE). This study aimed to explore the effectiveness and safety of contrast-enhanced ultrasound combined with two-dimensional ultrasound-guided MWA for the treatment of HAE in difficult/dangerous locations. Methods: Data from 81 patients, who were diagnosed with hepatic alveolar hydatid disease in difficult/dangerous locations between January 2018 and January 2023, and underwent contrast-enhanced ultrasonography combined with two-dimensional ultrasound-guided MWA, were analyzed. After undergoing MWA, patients were followed up to determine whether the lesions recurred and to evaluate the therapeutic effect of MWA. Preoperatively, individualized strategies were designed for lesions in different locations, and different auxiliary ablation technologies were used for contrast-enhanced ultrasound combined with two-dimensional ultrasound-guided MWA to achieve complete inactivation of lesions in difficult/dangerous locations. Results: MWA was performed on 89 HAE lesions in 81 patients. The median diameter of the lesions was 2.86 cm (interquartile range [IQR] 2.36-3.49 cm). The complete ablation rate after surgery was 100%, with a recurrence rate of 11.11%, and median follow-up of 24 months (IQR 12-48 months). The incidence of minor complications was 14.81%; no serious complications or deaths occurred. Compared with before surgery, TB, DB, alanine aminotransferase, and aspartate aminotransferase levels increased (p < 0.001), albumin platelets and activated partial thromboplastin time decreased (p < 0.05), with no statistical difference in prothrombin time (p > 0.05). Conclusion: MWA may be a safe and effective method for treating HAE in difficult/dangerous locations, and may represent a new and alternative option for this patient population.
RESUMO
OBJECTIVE: To evaluate the short-term clinical outcomes of radiofrequency ablation (RFA) using a radiofrequency (RF) needle device for varicose ulcers. METHODS: From September 2020 to September 2021, a total of 80 patients with varicose ulcers were included in this study. Based on the different surgical methods, the patients were divided into RF group and control groups, with 40 cases in each group. In the RF group, RFA was performed using an RF needle device and foam sclerotherapy was used for superficial veins. The control group was treated with conventional high-ligation stripping. The surgical data, hospitalization data, clinical efficacy, and postoperative complications of two groups were compared. Meanwhile, the correlation between RBC, HB, HCT, and ulcer healing time was analyzed. RESULTS: Compared to the control group, RF group had shorter surgery time, duration in the hospital, and less intraoperative bleeding (p < .05). The VCSS and CIVIQ scores in RF group were significantly higher than that in control group (p < .05). The healing time of ulcers was shorter in the RF group (x2 = 19.766, p = .000). The RF group had fewer postoperative complications. There was a positive correlation between RBC, HB, and HCT, and ulcer healing time (p < .05). CONCLUSION: The use of the RF needle device for RFA to treat patients with varicose ulcers showed acceptable short-term clinical outcomes with less incidence of trauma, faster recovery, and fewer complications.
RESUMO
BACKGROUND: Ultrasonography is the most widely used technique to diagnose echinococcosis; however, challenges in using this technique and the demand on medical resources, especially in low-income or remote areas, can delay diagnosis. We aimed to develop a deep convolutional neural network (DCNN) model based on ultrasonography to identify echinococcosis and its types, especially alveolar echinococcosis. METHODS: This retrospective, large-scale, multicentre study used ultrasound images from patients assessed at 84 hospitals in China, obtained between Jan 1, 2002, and Dec 31, 2021. Patients with a diagnosis of cystic echinococcosis, alveolar echinococcosis, or seven other types of focal liver lesions were included. We tested ResNet-50, ResNext-50, and VGG-16 as the backbone network architecture for a classification DCNN model and input the perinodular information from the ultrasound images. We trained and validated the DCNN model to diagnose and classify echinococcosis using still greyscale ultrasound images of focal liver lesions in four stages: differentiating between echinococcosis and other focal liver lesions (stage one); differentiating cystic echinococcosis, alveolar echinococcosis, and other focal liver lesions (stage two); differentiating cystic echinococcosis, alveolar echinococcosis, benign other focal liver lesions, and malignant focal liver lesions (stage three); and differentiating between active and transitional cystic echinococcosis and inactive cystic echinococcosis (stage four). We then tested the algorithm on internal, external, and prospective test datasets. The performance of DCNN was also compared with that of 12 radiologists recruited between Jan 15, 2022, and Jan 28, 2022, from Qinghai, Xinjiang, Anhui, Henan, Xizang, and Beijing, China, with different levels of diagnostic experience for echinococcosis and other focal liver lesions in a subset of ultrasound data that were randomly chosen from the prospective test dataset. The study is registered at ClinicalTrials.gov (NCT03871140). FINDINGS: The study took place between Jan 1, 2002, and Dec 31, 2021. In total, to train and test the DCNN model, we used 9631 liver ultrasound images from 6784 patients (2819 [41·7%] female patients and 3943 [58·3%] male patients) from 87 Chinese hospitals. The DCNN model was trained with 6328 images, internally validated with 984 images, and tested with 2319 images. The ResNet-50 network architecture outperformed VGG-16 and ResNext-50 and was generalisable, with areas under the receiver operating characteristic curve (AUCs) of 0·982 (95% CI 0·960-0·994), 0·984 (0·972-0·992), and 0·913 (0·886-0·935) in distinguishing echinococcosis from other focal liver lesions; 0·986 (0·966-0·996), 0·962 (0·946-0·975), and 0·900 (0·872-0·924) in distinguishing alveolar echinococcosis from cystic echinococcosis and other focal liver lesions; and 0·974 (0·818-1·000), 0·956 (0·875-0·991), and 0·944 (0·844-0·988) in distinguishing active and transitional cystic echinococcosis from inactive echinococcosis in the three test datasets. Specifically, in patients with the hepatitis B or hepatitis C virus, the model could distinguish alveolar echinococcosis from hepatocellular carcinoma with an AUC of 0·892 (0·812-0·946). In identifying echinococcosis, the model showed significantly better performance compared with senior radiologists from a high-endemicity area (AUC 0·942 [0·904-0·967] vs 0·844 [0·820-0·866]; p=0·027) and improved the diagnostic ability of junior, attending, and senior radiologists before and after assistance with AI with comparison of AUCs of 0·743 (0·714-0·770) versus 0·850 (0·826-0·871); p<0·0001, 0·808 (0·782-0·832) versus 0·886 (0·864-0·905); p<0·0001, and 0·844 (0·820-0·866) versus 0·870 (0·847-0·890); p=0·092, respectively. INTERPRETATION: The DCNN model was shown to be accurate and robust, and could improve the ultrasound diagnostic ability of radiologists for echinococcosis and its types for highly endemic and remote regions. FUNDING: National Natural Science Foundation of China and National Key Research & Development Program of China. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.