# Can radiomics-based innovations improve the diagnosis of kidney fibrosis in diabetic nephropathy?

## TL;DR

This paper explores how radiomics, combined with AI, can improve diagnosing kidney fibrosis in diabetic kidney disease, offering non-invasive and personalized diagnostic tools.

## Contribution

The paper highlights the novel integration of radiomics with genomics and AI to enhance diagnosis and treatment of diabetic nephropathy.

## Key findings

- Radiomics combined with AI can create predictive models for diabetic kidney disease diagnosis.
- Integration with genomics and metabolomics improves understanding of disease mechanisms.
- Challenges include lack of standardization and limited model interpretability.

## Abstract

Radiomics is a promising quantitative imaging technique that extracts and analyzes high-throughput features from medical images, providing detailed structural and functional information. It has gained significant attention in diabetic kidney disease (DKD) research, particularly in assessing renal fibrosis and predicting treatment outcomes. Radiomics offers a novel approach for accurate DKD diagnosis and holds potential for personalized treatment strategies. When combined with artificial intelligence and machine learning, it can create predictive models that improve clinical decision-making. Integrating radiomics with genomics and metabolomics further enhances understanding of disease mechanisms and facilitates biomarker discovery. Despite its potential, challenges such as lack of standardization, complex feature selection, limited model interpretability and inadequate clinical validation remain. Future advancements in imaging technologies, more efficient algorithms and large-scale clinical studies are expected to establish radiomics as a critical tool in precision medicine for DKD, enabling more accurate and personalized non-invasive diagnostics and therapies in nephrology.

GRAPHICAL ABSTRACT