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

Ren, Bo; Wang, Jian; Miao, Zhoulin; Xia, Yuwei; Liu, Wenya; Zhang, Tieliang; Aikebaier, Aierken

doi:10.1155/2021/6681092

Cited by 6 publications

(4 citation statements)

References 38 publications

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“…The main manifestations of early lesions were multivesicle-like structures without solid internal components. Therefore, the polyvesicle-like structure is considered the main pathological manifestation of early HAE, and early HAE in mice has a similar pathological basis to HAE in humans (15)(16)(17)(18). This is consistent with the findings of the current study.…”

Section: Discussionsupporting

confidence: 92%

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

Tian

et al. 2022

Front. Vet. Sci.

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ObjectiveThe 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.MethodsC57 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.ResultsThe 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.ConclusionThe intrahepatic portal vein infection HAE mouse model was successfully established.

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Section: Discussionsupporting

confidence: 92%

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

Tian

et al. 2022

Front. Vet. Sci.

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“…In recent years, artificial intelligence algorithms have rapidly developed and achieved many successes in medical image data mining, such as the application of radiomics in clinical diagnosis of medical images, and the contribution of deep learning in aspects such as clinical auxiliary diagnosis, surgical path planning, lesion segmentation and measurement, etc. In previous studies, Ren established a radiomics model to predict the biological activity of hepatic hydatid based on MRI images, and the optimal diagnostic model had a prediction performance of AUC = 0.830 ± 0.053 in the validation set 21 .…”

Section: Discussionmentioning

confidence: 99%

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

Damola,

Yang,

Abulaiti

et al. 2023

Preprint

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Objective This work aims to explore the potential applications of a deep learning radiomics (DLR) model, which is based on CT images, in predicting the biological activity grading of hepatic cystic echinococcosis. Methods A retrospective analysis of 160 patients with hepatic echinococcosis (109 cases of CE1 and 51 cases of CE2) was performed. A training set of 127 cases and a validation set of 33 cases were randomly divided from the data. Volume of interests (VOIs) were drawn from each patient’s CT image, 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 A total of 12 optimal features were selected from the radiomics features, and 6 and 10 optimal features were selected from two deep learning network (DLN) features (3D-ResNet-34, 3D-ResNet-50), 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 that the DLR model had a greater clinical benefit than the single radiomics model and deep feature model, 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.

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“…The untreated patients may reach a mortality rate higher than 90% 7 . Imaging diagnostic techniques for AE included computed tomography (CT) 8,9 , magnetic resonance imaging (MRI) 10,11 and ultrasound 12,13 , but the imaging features of AE patients in the early stage of infection are lack of specificity. Thus, most of the diagnosed AE patients have entered the later stage of the disease and missed the best opportunity for surgical operation.…”

Section: Alveolar Echinococcosis (Ae) Is a Zoonotic Parasitic Disease...mentioning

confidence: 99%

Spatiotemporal transcriptomic profiling reveals the dynamic immunological landscape of alveolar echinococcosis

Ou,

Li,

Ren

et al. 2024

Preprint

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Alveolar echinococcosis (AE) is caused by the invasive growth of the metacestodes ofEchinococcus multilocularis(E. multilocularis). The early diagnosis, management, and treatment of AE remains challenging. Herein, we integrated bulk RNA-seq, scRNA-seq, and ST technologies to reveal the immune characteristics both spatially and chronologically inE. multilocularisinfected mouse liver. An unprecedented high-resolution spatial atlas of theE. multilocularisinfection foci was obtained, revealing the pivotal role of neutrophils,Spp1+monocyte-derived macrophages (MoMFs), and fibroblasts in AE progression. We observed continuous recruitment of neutrophils and macrophages into the infected tissues, indicating prolonged immune stimulation by the growing metacestodes. However, their spatial distribution patterns and functions changed during the infection cause. At the early infection stage,Il1bhineutrophils aggregated in the lesion displayed the highest pattern-recognition receptor activity and may kill the metacestodes through NETosis, but their death in the infection foci might also trigger immune suppression. Meanwhile,Spp1+MoMFs attached to the protoscoleces to clear the infection. At the late infection stage, the neutrophils failed to infiltrate into the lesion although their continuous recruitment into the infected liver. Even though theSpp1+MoMFs enclosing the microcysts ofE. multilocularismay retain their pathogen-killing ability, they may also promote fibrosis and angiogenesis through interaction with fibroblasts. The pro- and anti-inflammatory phenotype balance of these immune cells may be associated with the transition of parasite control strategy from “active killing” to “negative segregation” by the host. Our findings identify key molecular traits involved in AE development, which may benefit the treatment of echinococcosis.

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

Cited by 6 publications

References 38 publications

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

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

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

Spatiotemporal transcriptomic profiling reveals the dynamic immunological landscape of alveolar echinococcosis

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