ArticleAbdominal radiology (New York)2023
Ultrasound-based radiomics analysis in the assessment of renal fibrosis in patients with chronic kidney disease.
Article in Abdominal radiology (New York), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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14 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Noninvasive diagnosis of interstitial fibrosis in chronic kidney disease: a systematic review and meta-analysis.Renal failure · 2024Pooled it
- Diagnostic accuracy of AI-augmented renal ultrasound for degenerative kidney disorders: a systematic review and meta-analysis.Future science OA · 2026Review
- Ultrasound radiomics-based machine learning models for differentiating diabetic kidney disease from non-diabetic kidney disease in type 2 diabetes.BMC nephrology · 2026Article
- Artificial intelligence in chronic kidney disease: Early detection, risk prediction, and personalized treatment strategies.World journal of nephrology · 2026Review
- Nomograms with arterial spin labeling for diagnosing early-stage chronic kidney disease.International urology and nephrology · 2026Article
- Dual-center validation of a multi-sequence MRI radiomics nomogram for noninvasive assessment of renal fibrosis in chronic kidney disease.BMC medical imaging · 2026Article
- Current and Future Applications of AI-Driven Predictive Modeling and a Proposed Framework for AI-Bioprognostics in Kidney Care.Risk management and healthcare policy · 2026Review
- Multi-Modality Ultrasound Model for Renal Fibrosis Assessment in Chronic Kidney Disease: Integrating Grayscale and Color Doppler Ultrasound Radiomics with Shear Wave Elastography.International journal of general medicine · 2025Article
- Noninvasive prediction of T-score in IgA nephropathy using machine learning within the Oxford classification system.Frontiers in endocrinology · 2025Article
- Deep learning in renal ultrasound: applications, challenges, and future outlook.Frontiers in oncology · 2025Review
- Usefulness of Radiomics and Kidney Volume Based on Non-Enhanced Computed Tomography in Chronic Kidney Disease: Initial Report.Kidney & blood pressure research · 2025Article
- Histopathological correlations of CT-based radiomics imaging biomarkers in native kidney biopsy.BMC medical imaging · 2024Article
- Value of radiomics-based two-dimensional ultrasound for diagnosing early diabetic nephropathy.Scientific reports · 2023Article
- Renal fibrosis assessment in chronic kidney disease: Exploring varied kidney regions with ultrasound-based radiomics analysis.Science progressArticle
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeAssessment of renal fibrosis non-invasively in chronic kidney disease (CKD) patients is still a clinical challenge. In this study, we aimed to establish a radiomics model integrating radiomics features derived from ultrasound (US) images with clinical characteristics for the assessment of renal fibrosis severity in CKD patients.
methodsA total of 160 patients with CKD who underwent kidney biopsy and renal US examination were prospectively enrolled. Patients were classified into the mild or moderate-severe fibrosis group based on pathology results. Radiomics features were extracted from the US images, and a radiomics signature was constructed using the maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) regression algorithms. Multivariable logistic regression was employed to construct the radiomics model, which incorporated the radiomics signature and the selected clinical variables. The established model was evaluated for discrimination, calibration, and clinical utility in the derivation cohort and internal cross-validation (CV) analysis, respectively.
resultsThe radiomics signature, consisting of nine identified fibrosis-related features, achieved moderate discriminatory ability with an area under the receiver operating characteristic curve (AUC) of 0.72 (95% confidence interval (CI) 0.64-0.79). By combining the radiomics signature with significant clinical risk factors, the radiomics model showed satisfactory discrimination performance, yielding an AUC of 0.85 (95% CI 0.79-0.91) in the derivation cohort and a mean AUC of 0.84 (95% CI 0.77-0.92) in the internal CV analysis. It also demonstrated fine accuracy via the calibration curve. Furthermore, the decision curve analysis indicated that the model was clinically useful.
conclusionThe proposed radiomics model showed favorable performance in determining the individualized risk of moderate-severe renal fibrosis in patients with CKD, which may facilitate more effective clinical decision-making.
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