Prognostic value of plasma IL-27 on biological viability of hepatic cystic echinococcosis

Identification of lesion bioactivity in hepatic cystic echinococcosis using a transformer-based fusion model

BACKGROUND

Differentiating whether hepatic cystic echinococcosis (HCE) lesions exhibit biological activity is essential for developing effective treatment plans. This study evaluates the performance of a Transformer-based fusion model in assessing HCE lesion activity.

METHODS

This study analyzed CT images and clinical variables from 700 HCE patients across three hospitals from 2018 to 2023. Univariate and multivariate logistic regression analyses were conducted for the selection of clinical variables to construct a clinical model. Radiomics features were extracted from CT images using Pyradiomics to develop a radiomics model. Additionally, a 2D deep learning model and a 3D deep learning model were trained using the CT images. The fusion model was constructed using feature-level fusion, decision-level fusion, and a Transformer network architecture, allowing for the analysis of the discriminative ability and correlation among radiomics features, 2D deep learning features, and 3D deep learning features, while comparing the classification performance of the three multimodal fusion models.

RESULTS

In comparison to radiomics and 2D deep learning features, the 3D deep learning features exhibited superior discriminative ability in identifying the biological activity of HCE lesions. The Transformer-based fusion model demonstrated the highest performance in both the internal validation set and the external validation set, achieving AUC values of 0.997 (0.992-1.000) and 0.944 (0.911-0.977), respectively, thereby outperforming both the feature-level and decision-level fusion models, and enabling precise differentiation of HCE lesion biological activity.

CONCLUSION

The Transformer multimodal fusion model integrates clinical features, radiomics features, and both 2D and 3D deep learning features, facilitating accurate differentiation of the biological activity of HCE lesions and exhibiting significant potential for clinical application.

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.

Integrative analysis of serum-derived exosomal lncRNA profiles of alveolar echinococcosis patients

Alveolar echinococcosis is a severe zoonotic disease caused by the pseudotumoral intrahepatic development of the larval stage of the tapeworm Echinococcus multilocularis. The diagnosis of alveolar echinococcosis is hard since it has features of liver cancer. LncRNAs are among the non-coding RNAs that have received the most attention in recent biomarker studies. Here, we aimed to identify the serum-derived exosomal lncRNAs associated with alveolar echinococcosis in humans with RNA-seq. After RNA isolation from exosomes, we performed RNA-seq to determine the lncRNAs. We found 8 target genes in the cis direction and a total of 6468 gene targets for lncRNAs were identified in the trans direction. Totally 621 mRNA transcripts were found as differentially expressed between the controls and patients. 278 of them were up-regulated and 343 were down-regulated. Moreover, 234 lncRNAs were found as differentially expressed between the controls and patients. 58 of them were up-regulated, and 176 of them were down-regulated. The top five biological pathways regulated by identified lncRNAs were detected in this study. As a result, it is thought that these results will contribute to lncRNA-based biomarker studies that can be used in the early diagnosis of alveolar echinococcosis in humans.

Genome-wide profiling of the expression of serum derived exosomal circRNAs in patients with hepatic alveolar echinococcosis

The patients with hepatic alveolar echinococcosis is poorly detected due to invasive and slow growth. Thus, early diagnosis of hepatic alveolar echinococcosis is so important for patients. Circular RNAs are crucial types of the non-coding RNA. Recent studies have provided serum-derived exosomal circRNAs as potential biomarkers for detection of various diseases. The clinical importance of exosomal circRNAs in hepatic alveolar echinococcosis have never been explored before. Here, we investigated the serum-derived exosomal circRNAs in the diagnosis of hepatic alveolar echinococcosis. Firstly, High-throughput Sequencing was performed using 9 hepatic alveolar echinococcosis and 9 control samples to detect hepatic alveolar echinococcosis related circRNAs. Afterwards, bioinformatic analyzes were performed to identify differentially expressed circRNAs and pathway analyzes were performed. Finally, validation of the determined circRNAs was performed using RT-PCR. The sequencing data indicated that 59 differentially expressed circRNAs; 31 up-regulated and 28 down-regulated circRNA in hepatic alveolar echinococcosis patients. The top 5 up-regulated and down-regulated circRNAs were selected for validation by RT-qPCR assay. As a result of the verification, circRNAs that were significantly up- and down-regulated showed an expression profile consistent with the results obtained. Importantly, our findings suggested that identified exosomal circRNAs could be a potential biomarker for the detection of hepatic alveolar echinococcosis serum and may help to understand the pathogenesis of hepatic alveolar echinococcosis.