The capacity of computed tomography in preoperative determination of surgery volume in kidney tumors

Relevance: Computed tomography allows detecting small tumors. However, surgical tactics cannot always be determined in advance. The purpose of the research was to assess the capacity of computed tomography in the preoperative determination of surgery volume in kidney tumors. Results: 548 patients were treated for kidney neoplasms. They were divided into three groups by computed tomography based on the R.E.N.A.L. scale: with a high risk of complications – 265 patients (48.4%), medium risk – 107 (19.5%), and low risk – 176 (32.1%). All operations were performed in the planned volume depending on the identified risk group for complications and resectability of kidney neoplasms; no changes to the plan of surgical interventions were made. The preoperative assessment of the kidney angioarchitectonics and the tumor relation to the pyelocaliceal system and the organ parenchyma helped determine the surgery volume and the possibility for organ-reserving interventions in 283 patients and radical nephrectomy in 265 patients. Conclusion: Such a highly informative method as computed tomography made allows early detection of small-sized kidney tumors to provide an opportunity for organ-preserving surgery and improved treatment outcome

The capacity of computed tomography in the preoperative determination of surgery volume in kidney tumors

Relevance: Computed tomography allows detecting small tumors. However, surgical tactics cannot always be determined in advance. The purpose of the research was to assess the capacity of computed tomography in preoperative determination of surgery volume in kidney tumors. Results: 548 patients were treated for kidney neoplasms. They were divided into three groups by computed tomography based on the R.E.N.A.L. scale: with a high risk of complications – 265 patients (48.4%), medium risk – 107 (19.5%), and low risk – 176 (32.1%). All operations were performed in the planned volume depending on the identified risk group for complications and resectability of kidney neoplasms; no changes to the plan of surgical interventions were made. The preoperative assessment of the kidney angioarchitectonics and the tumor relation to the pyelocaliceal system and the organ parenchyma helped determine the surgery volume and the possibility for organ-reserving interventions in 283 patients and radical nephrectomy in 265 patients. Conclusion: Such a highly informative method as computed tomography made allows early detection of small-sized kidney tumors to provide an opportunity for organ-preserving surgery and improved treatment outcome

Predicting miRNA-Disease Associations Based on Heterogeneous Graph Attention Networks

In recent years, more and more evidence has shown that microRNAs (miRNAs) play an important role in the regulation of post-transcriptional gene expression, and are closely related to human diseases. Many studies have also revealed that miRNAs can be served as promising biomarkers for the potential diagnosis and treatment of human diseases. The interactions between miRNA and human disease have rarely been demonstrated, and the underlying mechanism of miRNA is not clear. Therefore, computational approaches has attracted the attention of researchers, which can not only save time and money, but also improve the efficiency and accuracy of biological experiments. In this work, we proposed a Heterogeneous Graph Attention Networks (GAT) based method for miRNA-disease associations prediction, named HGATMDA. We constructed a heterogeneous graph for miRNAs and diseases, introduced weighted DeepWalk and GAT methods to extract features of miRNAs and diseases from the graph. Moreover, a fully-connected neural networks is used to predict correlation scores between miRNA-disease pairs. Experimental results under five-fold cross validation (five-fold CV) showed that HGATMDA achieved better prediction performance than other state-of-the-art methods. In addition, we performed three case studies on breast neoplasms, lung neoplasms and kidney neoplasms. The results showed that for the three diseases mentioned above, 50 out of top 50 candidates were confirmed by the validation datasets. Therefore, HGATMDA is suitable as an effective tool to identity potential diseases-related miRNAs.

Towards the doctrine of hypernephromas

By hypernephromas, it is customary to mean kidney neoplasms, which are rarely found in clinics, but, nevertheless, occupy the first place among other renal neoplasms.

Prediction of miRNA-Disease Association Using Deep Collaborative Filtering

The existing studies have shown that miRNAs are related to human diseases by regulating gene expression. Identifying miRNA association with diseases will contribute to diagnosis, treatment, and prognosis of diseases. The experimental identification of miRNA-disease associations is time-consuming, tremendously expensive, and of high-failure rate. In recent years, many researchers predicted potential associations between miRNAs and diseases by computational approaches. In this paper, we proposed a novel method using deep collaborative filtering called DCFMDA to predict miRNA-disease potential associations. To improve prediction performance, we integrated neural network matrix factorization (NNMF) and multilayer perceptron (MLP) in a deep collaborative filtering framework. We utilized known miRNA-disease associations to capture miRNA-disease interaction features by NNMF and utilized miRNA similarity and disease similarity to extract miRNA feature vector and disease feature vector, respectively, by MLP. At last, we merged outputs of the NNMF and MLP to obtain the prediction matrix. The experimental results indicate that compared with other existing computational methods, our method can achieve the AUC of 0.9466 based on 10-fold cross-validation. In addition, case studies show that the DCFMDA can effectively predict candidate miRNAs for breast neoplasms, colon neoplasms, kidney neoplasms, leukemia, and lymphoma.

Current methods of radiological diagnosis of kidney neoplasms (literature review)

We presented an overview of domestic and foreign studies on the diagnosis of renal malignancies published in publicly available electronic specialized medical publications. Taking into account that every year the share of oncological diseases in the structure of the total incidence is constantly growing, and that cancer is one of the main causes of death and disability in the working age population, currently, the search for new diagnostic methods to detect kidney tumors still remains a pressing problem located at the junction of several medical disciplines, in particular, oncology, urology, radiation diagnostics and radiation therapy. Over the past decade, the diagnosis of malignant kidney neoplasms has undergone significant changes and has stepped far forward. Because of that the ability to detect the disease in the early stages of development increases. In the study, we examined the most widespread methods, methods that have already lost relevance, as well as new methods, such as magnetic resonance imaging, computed tomography ultrasonography, radiography, etc. We also considered the possibilities of differential diagnosis of benign and malignant neoplasms.

miRNA-Disease Association Prediction with Collaborative Matrix Factorization

As one of the factors in the noncoding RNA family, microRNAs (miRNAs) are involved in the development and progression of various complex diseases. Experimental identification of miRNA-disease association is expensive and time-consuming. Therefore, it is necessary to design efficient algorithms to identify novel miRNA-disease association. In this paper, we developed the computational method of Collaborative Matrix Factorization for miRNA-Disease Association prediction (CMFMDA) to identify potential miRNA-disease associations by integrating miRNA functional similarity, disease semantic similarity, and experimentally verified miRNA-disease associations. Experiments verified that CMFMDA achieves intended purpose and application values with its short consuming-time and high prediction accuracy. In addition, we used CMFMDA on Esophageal Neoplasms and Kidney Neoplasms to reveal their potential related miRNAs. As a result, 84% and 82% of top 50 predicted miRNA-disease pairs for these two diseases were confirmed by experiment. Not only this, but also CMFMDA could be applied to new diseases and new miRNAs without any known associations, which overcome the defects of many previous computational methods.