Correlation between CT and anatomopathological staging of kidney cancer

Epidemiological profile of kidney cancer in Brazil: a multiregional ecological study

INTRODUCTION

Renal neoplasia is a complex and heterogeneous disease, characterized by high morbidity and mortality.

OBJECTIVE

To analyze the temporal trend of hospitalization rates (HRs) for renal neoplasia in Brazil, segmented by region, states (UFs, Unidades da Federação in Portuguese), and population characteristics, from 2013 to 2023.

METHODS

Ecological study using data from the Hospital Information System, by analyzing Hospital Admission Authorizations, covering the period from 2013 to 2023. The annual trend of HRs was analyzed using generalized linear regression with the Prais-Winsten method by calculating the Annual Percentage Change (APC), considering sex, age, race/color, and region/state (UF). A significance level of 5% was adopted for the analyses.

RESULTS

A total of 31,388 hospitalizations for renal neoplasia were recorded in Brazil during the period, showing a significant upward trend in HRs (APC: 9.12; 95%CI: 5.30; 13.1; p < 0.001). The increase was observed in both sexes and in all regions. Among the states, most showed stationary trends. The highest average HRs were identified among young elderly individuals (3.31/100,000) and long-lived elderly individuals (2.51/100,000).

CONCLUSION

HRIs due to renal neoplasia in Brazil showed a significant upward trend between 2013 and 2023, with regional variations, a predominance in males, and a higher incidence in the over-60 age group.

Radiology Imaging Scans for Early Diagnosis of Kidney Tumors: A Review of Data Analytics-Based Machine Learning and Deep Learning Approaches

Plenty of disease types exist in world communities that can be explained by humans’ lifestyles or the economic, social, genetic, and other factors of the country of residence. Recently, most research has focused on studying common diseases in the population to reduce death risks, take the best procedure for treatment, and enhance the healthcare level of the communities. Kidney Disease is one of the common diseases that have affected our societies. Sectionicularly Kidney Tumors (KT) are the 10th most prevalent tumor for men and women worldwide. Overall, the lifetime likelihood of developing a kidney tumor for males is about 1 in 466 (2.02 percent) and it is around 1 in 80 (1.03 percent) for females. Still, more research is needed on new diagnostic, early, and innovative methods regarding finding an appropriate treatment method for KT. Compared to the tedious and time-consuming traditional diagnosis, automatic detection algorithms of machine learning can save diagnosis time, improve test accuracy, and reduce costs. Previous studies have shown that deep learning can play a role in dealing with complex tasks, diagnosis and segmentation, and classification of Kidney Tumors, one of the most malignant tumors. The goals of this review article on deep learning in radiology imaging are to summarize what has already been accomplished, determine the techniques used by the researchers in previous years in diagnosing Kidney Tumors through medical imaging, and identify some promising future avenues, whether in terms of applications or technological developments, as well as identifying common problems, describing ways to expand the data set, summarizing the knowledge and best practices, and determining remaining challenges and future directions.