Ultrasound identification of hepatic echinococcosis using a deep convolutional neural network model in China: a retrospective, large-scale, multicentre, diagnostic accuracy study.

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.

Differential diagnosis between hepatic alveolar echinococcosis and intrahepatic cholangiocarcinoma with conventional ultrasound and contrast-enhanced ultrasound.

BACKGROUND: Misclassifications of hepatic alveolar echinococcosis (HAE) as intrahepatic cholangiocarcinoma (ICC) may lead to inappropriate treatment strategies. The aim of this study was to explore the differential diagnosis with conventional ultrasound and contrast-enhanced ultrasound (CEUS). METHODS: Sixty HAE lesions with 60 propensity score-matched ICC lesions were retrospectively collected. The 120 lesions were randomly divided into a training set (n = 80) and a testing set (n = 40). In the training set, the most useful independent conventional ultrasound and CEUS features was selected for differentiating between HAE and ICC. Then, a simplified US scoring system for diagnosing HAE was constructed based on selected features with weighted coefficients. The constructed US score for HAE was validated in both the training set and the testing set, and diagnostic performance was evaluated. RESULTS: Compared with ICC lesions, HAE lesions were mostly located in the right lobe and had mixed echogenicity, a pseudocystic appearance and foci calcifications on conventional ultrasound. On CEUS, HAE lesions showed more regular rim-like enhancement than ICC lesions and had late washout with a long enhancement duration. The simplified US score consisted of echogenicity, pseudocystic/calcification, bile duct dilatation, enhancement pattern, enhancement duration, and marked washout. In the testing set, the sensitivity, specificity, LR+, LR- and the area under the ROC curve for the score to differentiate HAE from ICC were 80.0, 81.3%, 4.27, 0.25 and 0.905, respectively. CONCLUSIONS: The US score based on typical features from both conventional ultrasound and CEUS could accurately differentiate HAE from ICC.

Microwave ablation combining surgery for the treatment of multiorgan cystic echinococcosis: A case report.

Cystic echinococcosis (CE) is a global public health problem associated with a high overall disease burden. Multiple organ systems are involved in approximately 20% of cases, and treatment is challenging and rarely reported. In this study, we described microwave ablation (MWA) combining surgery for the treatment of a multiorgan CE patient. The patient underwent percutaneous MWA for a hepatic CE3b lesion and exploratory resection of the pelvic cavity lesions. The hepatic lesion was effectively treated by MWA, and invasiveness was reduced by avoiding hepatectomy. The patient had a favorable prognosis at a 20-month's follow-up. More studies are needed to evaluate its usefulness in CE treatment.

Thermal Field Distributions of Ablative Experiments Using Cyst-mimicking Phantoms: Comparison of Microwave and Radiofrequency Ablation.

RATIONALE AND OBJECTIVES: The objective of this study was to explore the thermal field distribution of cystic lesions undergoing microwave ablation (MWA) and radiofrequency ablation (RFA) using in vitro phantoms. MATERIALS AND METHODS: Cyst-mimicking lesions filled with sodium chloride (NaCl) solution in acrylamide phantoms were treated with MWA and RFA in vitro. The radiofrequency electrodes or MWA antennas were implanted in the centers of the artificial cystic lesions. We used temperature fields located 5, 15, and 25 mm from the electrode or the antenna to plot the temperature-rise curves. Solid phantoms without cysts were also fabricated as controls. RESULTS: The temperature within cysts increased faster and reached a higher maximum temperature during MWA than during RFA, and this result was independent of the NaCl solution concentration. RFA treatment caused the temperatures within the lesion to increase significantly faster in the cysts containing 0.9% NaCl than in those containing 5.0% NaCl. However, the MWA temperature-rise curves were only weakly affected by the ionic concentration. The median temperature difference values between the 5- and 15-mm points were markedly lower in the 0.9% NaCl cyst-mimicking phantom (P <0.001) than in the solid phantom after either MWA or RFA. CONCLUSIONS: Our data indicate that MWA is a more effective technique for focal cystic lesions than RFA and has higher overall energy utilization. MWA was also less affected by the ionic concentration of the cystic fluid.