Hepatic alveolar echinococcosis accompanied by lung and kidney metastases: a case description of imaging findings.
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ABSTRACT:
SUBMITTER: Gao X
PROVIDER: S-EPMC10784018 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Hepatic alveolar echinococcosis accompanied by lung and kidney metastases: a case description of imaging findings.
Gao Xukun X Tan Huaqing H Zhu Mengdie M Wang Yuxuan Y Zhang Yumeng Y Cao Yuntai Y Fan Haining H
Quantitative imaging in medicine and surgery 20231020 1
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Project description:Alveolar echinococcosis (AE) is considered as a fatal zoonosis caused by the larvae of Echinococcus multilocularis. The lungs and brain are the most common metastatic organs. We report a human case of hepatic alveolar echinococcosis accompanied by lung and brain metastasis. In particular, the patient had a history of tuberculosis and the lung lesions were easily misdiagnosed as lung abscesses. The lesions of liver and lung underwent radical resection and confirmed as alveolar echinococcosis by pathological examination. The patient had no surgical complications after operation and was discharged after symptomatic treatment. Unfortunately, the patient later developed multiple intracerebral AE metastases. We required the patient to take albendazole orally for life and follow up.
| S-EPMC8255489 | biostudies-literature
Project description:BackgroundPrimary cardiac echinococcosis is rare, and cardiac alveolar echinococcosis is even rarer and more unusual. Reported cases of this disease are extremely limited, and multimodal imaging provides an important guide to treatment and decision-making. We report a case of cardiac alveolar echinococcosis. A 31-year-old male patient with no significant history was diagnosed to have a space-occupying lesion in the mediastinum. Transthoracic echocardiography showed a cystic mass anterior to the right lateral aspect of the right atrium, which did not show enhancement on the enhanced scan. Multidetector computed tomography (MDCT) and cardiac magnetic resonance showed a cystic space-occupying lesion in the right lateral aspect of the right atrium, with mild enhancement of the edges of the lesion and multiple small vesicles on the enhancement scan. Clinicians operated on the patient under suspicion of cardiac echinococcosis and successfully removed the lesion. Ultimately, postoperative histopathologic examination revealed cardiac alveolar echinococcosis. The patient recovered well and was discharged with regular postoperative oral albendazole tablets and regular follow-up reviews.ConclusionWe report a case of cardiac alveolar echinococcosis with multimodal imaging features and therapeutic strategies, an extremely rare cardiac occupying disease. Multimodal imaging is of great help in the diagnosis of this disease, and surgical resection and histopathological diagnosis are essential. After surgery, treatment and follow-up will be carried out based on the results of the histological examination. This rare case emphasizes the integrated diagnosis of cardiac alveolar echinococcosis with clinical, multimodal imaging and pathologic data.
| S-EPMC11834188 | biostudies-literature
Project description:OBJECTIVE:This study is aimed at determining the preoperative nutritional status of patients with hepatic alveolar echinococcosis (HAE), and subsequently establish a concise and reasonable nutritional evaluation indicator. The established evaluation method could be used for clinical preoperative risk assessment and prediction of post-operation recovery. METHODS:The basic patient information on height, body weight, BMI and hepatic encephalopathy of 93 HAE patients were examined. Subsequently, abdominal ultrasonography, blood coagulation and liver function tests were done on the patients. Liver function was assessed using the Child-Pugh improved grading method while nutritional status was evaluated using the European Nutrition Risk Screening 2002 (NRS 2002) method. Additional parameters including hospitalization time, the hemoglobin (HGB) level on the 3rd day after the operation, and the number of postoperative complications of HAE patients were also recorded. RESULTS:The NRS 2002 score was negatively correlated with body weight, body mass index (BMI)and albumin (ALB) (P<0.01), and positively correlated with the transverse and longitudinal diameters of the lesions (P<0.01). A worse grading of liver function was associated with a low ALB and a high NRS 2002 score (P<0.01). Results of the NRS 2002 score indicate that the hospitalization time of the normal nutrition group was significantly shorter than that of the malnourished group (P < 0.05). The HGB level of the control group on the 3rd day after the operation was significantly higher than that of the malnourished group (P < 0.05), and the number of postoperative complications was lower than that of malnutrition group (P < 0.05). CONCLUSION:Malnutrition is common in HAE patients. The nutritional status of HAE patients is related to many clinical factors, such as Child-Pugh classification of liver function, size of the lesion, and ALB among others. Although both BMI and ALB can be used as primary screening indicators for malnutrition in HAE patients, NRS 2002 is more reliable and prudent in judging malnutrition in HAE patients. Therefore, BMI and ALB are more suitable for preoperative risk assessment and prediction of postoperative recovery.
| S-EPMC7039506 | biostudies-literature
Project description:ObjectiveThis study aims to evaluate the effectiveness of a nomogram model constructed using Diffusion Kurtosis Imaging (DKI) and 3D Arterial Spin Labeling (3D-ASL) functional imaging techniques in distinguishing between cerebral alveolar echinococcosis (CAE) and brain metastases (BM).MethodsProspectively collected were 24 cases (86 lesions) of patients diagnosed with CAE and 16 cases (69 lesions) of patients diagnosed with BM at the affiliated hospital of Qinghai University from 2018 to 2023, confirmed either pathologically or through comprehensive diagnosis. Both patient groups underwent DKI and 3D-ASL scanning. DKI parameters (Kmean, Dmean, FA, ADC) and cerebral blood flow (CBF) were analyzed for the parenchymal area, edema area, and symmetrical normal brain tissue area in both groups. There were 155 lesions in total in the two groups of patients. We used SPSS to randomly select 70% as the training set (108 lesions) and the remaining 30% as the test set (47 lesions) and performed a difference analysis between the two groups. The independent factors distinguishing CAE from BM were identified using univariate and multivariate logistic regression analyses. Based on these factors, a diagnostic model was constructed and expressed as a nomogram.ResultUnivariate and multivariate logistic regression analyses identified nDmean1 and nCBF1 in the lesion parenchyma area, as well as nKmean2 and nDmean2 in the edema area, as independent factors for distinguishing CAE from BM. The model's performance, measured by the area under the ROC curve (AUC), had values of 0.942 and 0.989 for the training and test sets, respectively. Calibration curves demonstrated that the predicted probabilities were highly consistent with the actual values, and DCA confirmed the model's high clinical utility.ConclusionThe nomogram model, which incorporates DKI and 3D-ASL functional imaging, effectively distinguishes CAE from BM. It offers an intuitive, accurate, and non-invasive method for differentiation, thus providing valuable guidance for subsequent clinical decisions.
| S-EPMC11484367 | biostudies-literature
Project description:In September 2023, a patient in Italy who had never traveled abroad was referred for testing for suspected hepatic cystic echinococcosis. Lesions were incompatible with cystic echinococcosis; instead, autochthonous alveolar echinococcosis was confirmed. Alveolar echinococcosis can be fatal, and awareness must be raised of the infection's expanding distribution.
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| S-EPMC10784085 | biostudies-literature
Project description:BackgroundCerebral alveolar echinococcosis (CAE) and brain metastases (BM) share similar in locations and imaging appearance. However, they require distinct treatment approaches, with CAE typically treated with chemotherapy and surgery, while BM is managed with radiotherapy and targeted therapy for the primary malignancy. Accurate diagnosis is crucial due to the divergent treatment strategies.PurposeThis study aims to evaluate the effectiveness of radiomics and machine learning techniques based on magnetic resonance imaging (MRI) to differentiate between CAE and BM.MethodsWe retrospectively analyzed MRI images of 130 patients (30 CAE and 100 BM) from Xinjiang Medical University First Affiliated Hospital and The First People's Hospital of Kashi Prefecture, between January 2014 and December 2022. The dataset was divided into training (91 cases) and testing (39 cases) sets. Three dimensional tumors were segmented by radiologists from contrast-enhanced T1WI images on open resources software 3D Slicer. Features were extracted on Pyradiomics, further feature reduction was carried out using univariate analysis, correlation analysis, and least absolute shrinkage and selection operator (LASSO). Finally, we built five machine learning models, support vector machine, logistic regression, linear discrimination analysis, k-nearest neighbors classifier, and Gaussian naïve bias and evaluated their performance via several metrics including sensitivity (recall), specificity, positive predictive value (precision), negative predictive value, accuracy and the area under the curve (AUC).ResultsThe area under curve (AUC) of support vector classifier (SVC), linear discrimination analysis (LDA), k-nearest neighbors (KNN), and gaussian naïve bias (NB) algorithms in training (testing) sets are 0.99 (0.94), 1.00 (0.87), 0.98 (0.92), 0.97 (0.97), and 0.98 (0.93), respectively. Nested cross-validation demonstrated the robustness and generalizability of the models. Additionally, the calibration plot and decision curve analysis demonstrated the practical usefulness of these models in clinical practice, with lower bias toward different subgroups during decision-making.ConclusionThe combination of radiomics and machine learning approach based on contrast enhanced T1WI images could well distinguish CAE and BM. This approach holds promise in assisting doctors with accurate diagnosis and clinical decision-making.
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