An Overview of Hepatic Echinococcosis and the Characteristic CT and MRI Imaging Manifestations

Detection and subtyping of hepatic echinococcosis from plain CT images with deep learning: a retrospective, multicentre study

BACKGROUND

Hepatic echinococcosis is a severe endemic disease in some underdeveloped rural areas worldwide. Qualified physicians are in short supply in such areas, resulting in low rates of accurate diagnosis of this condition. In this study, we aimed to develop and evaluate an artificial intelligence (AI) system for automated detection and subtyping of hepatic echinococcosis using plain CT images with the goal of providing interpretable assistance to radiologists and clinicians.

METHODS

We developed EDAM, an echinococcosis diagnostic AI system, to provide accurate and generalisable CT analysis for distinguishing hepatic echinococcosis from hepatic cysts and normal controls (no liver lesions), as well as subtyping hepatic echinococcosis as alveolar or cystic echinococcosis. EDAM includes a slice-level prediction model for lesion classification and segmentation and a patient-level diagnostic model for patient classification. We collected a plain CT database (n=700: 395 cystic echinococcosis, 122 alveolar echinococcosis, 130 hepatic cysts, and 53 normal controls) for developing EDAM, and two additional independent cohorts (n=156) for external validation of its performance and generalisation ability. We compared the performance of EDAM with 52 experienced radiologists in diagnosing and subtyping hepatic echinococcosis.

FINDINGS

EDAM showed reliable performance in patient-level diagnosis on both the internal testing data (overall area under the receiver operating characteristic curve [AUC]: 0·974 [95% CI 0·936-0·994]; accuracy: 0·952 [0·939-0·965] for cystic echinococcosis, 0·981 [0·973-0·989] for alveolar echinococcosis; sensitivity: 0·966 [0·951-0·979] for cystic echinococcosis, 0·944 [0·908-0·970] for alveolar echinococcosis) and the external testing set (overall AUC: 0·953 [95% CI 0·840-0·973]; accuracy: 0·929 [0·915-0·947] for cystic echinococcosis, 0·936 [0·919-0·950] for alveolar echinococcosis; sensitivity: 0·913 [0·879-0·944] for cystic echinococcosis, 0·868 [0·841-0·897] for alveolar echinococcosis). The sensitivity of EDAM was robust across images from different CT manufacturers. EDAM outperformed most of the enrolled radiologists in detecting both alveolar echinococcosis and cystic echinococcosis.

INTERPRETATION

EDAM is a clinically applicable AI system that can provide patient-level diagnoses with interpretable results. The accuracy and generalisation ability of EDAM demonstrates its potential for clinical use, especially in underdeveloped areas.

FUNDING

Project of Qinghai Provincial Department of Science and Technology of China, National Natural Science Foundation of China, and Tsinghua-Fuzhou Institute of Data Technology Project.

TRANSLATION

For the Chinese translation of the abstract see Supplementary Materials section.

Can contrast-enhanced ultrasound differentiate the type of hepatic echinococcosis: cystic echinococcosis or alveolar echinococcosis?

BACKGROUND

Hepatic echinococcosis (HE) is a zoonotic disease caused by Echinococcus, and Echinococcus granulosus and E. multilocularis are the most common, causing cystic echinococcosis (CE) and alveolar echinococcosis (AE), respectively. Contrast-enhanced ultrasound (CEUS) is an imaging technique which has been recommended for identifying focal lesions in the liver. However, the effect of CEUS on the differentiation of hepatic echinococcosis type remains unclear.

METHODS

Twenty-five patients with 46 HE lesions confirmed by histopathology in our hospital from December 2019 to May 2022 were reviewed by conventional ultrasound (US) and CEUS examinations, respectively. After US was completed, the CEUS study was performed. A bolus injection of 1.0-1.2 ml of a sulfur hexafluoride-filled microbubble contrast agent (SonoVue^®) was administered. The images and clips of the lesions by US and CEUS were reviewed retrospectively. The lesions detected using US were evaluated including the location, size, morphology, margin, internal echogenicity and the internal Doppler signal. The lesions detected using CEUS were evaluated including the enhancement degree, enhancement pattern and enhancing boundary in different phases. The diagnoses of lesions by US or CEUS were respectively recorded. By taking the histopathology as the gold standard, the paired Chi-square test was performed with statistical software (IBM SPSS; IBM Corp., Armonk, NY, USA), and the results of differentiation of HE type by US and CEUS were statistically analyzed.

RESULTS

A total of 46 lesions were involved in 25 patients, including 10 males (40.0%) and 15 females (60.0%) aged 15-55 (42.9 ± 10.3) years. By histopathology, 24 lesions of nine patients were diagnosed as CE and 22 lesions of 16 patients were diagnosed as AE. Among the 46 HE lesions, compared with histopathological examination, the accuracy rate was 65.2% and 91.3% in US and CEUS findings, respectively. Among the 24 CE lesions, 13 lesions were correctly differentiated by US, and 23 by CEUS. The difference between US and CEUS was statistically significant (Chi-square test, [Formula: see text] = 8.10, df = 23, P < 0.005). Among the total 46 HE lesions, 30 lesions were correctly differentiated by US, and 42 by CEUS. The difference between US and CEUS was statistically significant (Chi-square test, [Formula: see text] = 10.08, df = 45, P < 0.005).

CONCLUSIONS

CEUS is a more effective technique than US for differentiating the type of HE between CE and AE. It could be a reliable tool in the differentiation of HE.

Feasibility analysis and study of an intrahepatic portal vein infection hepatic alveolar echinococcosis C57 mouse model

Objective

The aim of the study was to establish and study an intrahepatic portal vein infection hepatic alveolar echinococcosis (HAE) C57 mouse model and provide a theoretical basis for clinical research on HAE.

Methods

C57 mice were used to establish the HAE mouse model. The location, size, morphology, appearance, and pathological changes in liver lesions in different groups of mice were characterized using ultrasound, magnetic resonance imaging (MRI), and haematoxylin and eosin staining.

Results

The mortality rate of the C57 mice was 20%, and the success rate of infection was 75%. The abdominal ultrasound images and MRIs clearly indicated the location, size, shape, and appearance of the liver lesions and the relationship between the lesions and the adjacent organs. The size, morphology, and signal of the livers in the control group were normal. The pathological results of the experimental group indicated a hepatic vesicular acinar cyst, while those of the control group exhibited normal livers.

Conclusion

The intrahepatic portal vein infection HAE mouse model was successfully established.

Calcifying nanoparticles initiate the calcification process of mesenchymal stem cells in vitro through the activation of the TGF-β1/Smad signaling pathway and promote the decay of echinococcosis

The role of the calcifying nanoparticles (CNPs) in the calcification process of the outer cyst wall in hepatic cystic echinococcosis (HCE) remains unknown. CNPs were isolated from the tissues of the patients with HCE. Western blotting, alkaline phosphatase staining, and alizarin staining were performed to detect the cellular calcium ion deposition induced by the CNPs. CCK-8 and flow cytometry assays were conducted to determine the effect of CNPs on the apoptosis of mesenchymal stem cells (MSCs). Western blot experiments were performed to examine the expression levels of apoptosis-related factors and TGF-β1/Smad signaling pathway constituents. Treatment with CNPs induced the differentiation of MSCs. Calcium-related proteins, including OPN, BMP-2, and RUNX2, were upregulated after the CNP treatment. Similarly, CNP exposure increased the cellular calcium ion deposition in MSCs. In addition, the expression of Bax and Caspase-8 was elevated by the CNPs in MSCs. Treatment with CNPs promoted MSC apoptosis and inhibited the MSC growth. The TGF-β1/Smad signaling pathway was also activated after the CNP treatment. This study indicated that CNPs may play a critical role in initiating calcification of the outer cyst wall of HCE and promote the decay of echinococcosis, providing a new strategy for the treatment of hepatic echinococcosis.

Disseminated alveolar echinococcosis in a patient diagnosed by metagenomic next-generation sequencing: A case report

Background

Alveolar echinococcosis (AE) is a parasitic zoonosis with high mortality and disability rates. Diverse clinical manifestations and mimicking of differential diagnoses such as tuberculosis and malignancy pose a diagnostic dilemma. With the rapid development of molecular diagnostic techniques in recent years, metagenomic next-generation sequencing (mNGS) has become an attractive approach for the etiological diagnosis of infectious diseases.

Case presentation

we report a case of 51-year-old Chinese Tibetan male presented with 3-year low-back pain and 4-month discomfort in the right upper quadrant of the abdomen. He had been in good health. He was diagnosed with tuberculosis and was given anti-tuberculosis treatment a month prior to the visit, but the symptoms were not relieved. Abdominal computerized tomography (CT) revealed a hypodense lesion with uneven enhancement in the liver, and two ring-enhancing cystic lesions in the right abdominal wall. Lumbar spine enhanced MRI showed lesions of mixed density with uneven enhancement in the L1 vertebra and paraspinal tissue. The pathological results of the liver biopsy revealed parasitic infection and possibly echinococcosis. The metagenomic next-generation sequencing (mNGS) of the puncture fluid of abdominal cysts using Illumina X10 sequencer revealed 585 sequence reads matching Echinococcus multilocularis. Disseminated AE was diagnosed. Albendazole (400 mg, twice daily) was used, and the patient was in stable condition during follow-up.

Conclusions

mNGS may be a useful tool for the diagnosis of AE. The case would help clinicians to improve their diagnostic skills.