Hepatic Alveolar Echinococcosis: Predictive Biological Activity Based on Radiomics of MRI

A deep learning radiomics model based on CT images for predicting the biological activity of hepatic cystic echinococcosis

Introduction: Hepatic cystic echinococcosis (HCE) is a widely seen parasitic infection. Biological activity is crucial for treatment planning. This work aims to explore the potential applications of a deep learning radiomics (DLR) model, based on CT images, in predicting the biological activity grading of hepatic cystic echinococcosis. Methods: A retrospective analysis of 160 patients with hepatic echinococcosis was performed (127 and 33 in training and validation sets). Volume of interests (VOIs) were drawn, and radiomics features and deep neural network features were extracted. Feature selection was performed on the training set, and radiomics score (Rad Score) and deep learning score (Deep Score) were calculated. Seven diagnostics models (based on logistic regression algorithm) for the biological activity grading were constructed using the selected radiomics features and two deep model features respectively. All models were evaluated using the receiver operating characteristic curve, and the area under the curve (AUC) was calculated. A nomogram was constructed using the combined model, and its calibration, discriminatory ability, and clinical utility were assessed. Results: 12, 6 and 10 optimal radiomics features, deep learning features were selected from two deep learning network (DLN) features, respectively. For biological activity grading of hepatic cystic echinococcosis, the combined model demonstrated strong diagnostic performance, with an AUC value of 0.888 (95% CI: 0.837-0.936) in the training set and 0.876 (0.761-0.964) in the validation set. The clinical decision analysis curve indicated promising results, while the calibration curve revealed that the nomogram's prediction result was highly compatible with the actual result. Conclusion: The DLR model can be used for predicting the biological activity grading of hepatic echinococcosis.

Value of intralesional and perilesional radiomics for predicting the bioactivity of hepatic alveolar echinococcosis

Objectives

To investigate the value of intralesional and perilesional radiomics based on computed tomography (CT) in predicting the bioactivity of hepatic alveolar echinococcosis (HAE).

Materials and methods

In this retrospective study, 131 patients who underwent surgical resection and diagnosed HAE in pathology were included (bioactive, n=69; bioinactive, n=62). All patients were randomly assigned to the training cohort (n=78) and validation cohort (n=53) in a 6:4 ratio. The gross lesion volume (GLV), perilesional volume (PLV), and gross combined perilesional volume (GPLV) radiomics features were extracted on CT images of portal vein phase. Feature selection was performed by intra-class correlation coefficient (ICC), univariate analysis, and least absolute shrinkage and selection operator (LASSO). Radiomics models were established by support vector machine (SVM). The Radscore of the best radiomics model and clinical independent predictors were combined to establish a clinical radiomics nomogram. Receiver operating characteristic curve (ROC) and decision curves were used to evaluate the predictive performance of the nomogram model.

Results

In the training cohort, the area under the ROC curve (AUC) of the GLV, PLV, and GPLV radiomic models was 0.774, 0.729, and 0.868, respectively. GPLV radiomic models performed best among the three models in training and validation cohort. Calcification type and fibrinogen were clinical independent predictors (p<0.05). The AUC of the nomogram-model-based clinical and GPLV radiomic signatures was 0.914 in the training cohort and 0.833 in the validation cohort. The decision curve analysis showed that the nomogram had greater benefits compared with the single radiomics model or clinical model.

Conclusion

The nomogram model based on clinical and GPLV radiomic signatures shows the best performance in prediction of the bioactivity of HAE. Radiomics including perilesional tissue can significantly improve the prediction efficacy of HAE bioactivity.

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.