ArticleBMC medical imaging2024
Histopathological correlations of CT-based radiomics imaging biomarkers in native kidney biopsy.
Article in BMC medical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- 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
- Hierarchical organ aging signatures from routine abdominal CT add incremental disease risk stratification beyond blood biomarkers.medRxiv : the preprint server for health sciences · 2026Article
- Associations between CT radiomics analyses and kidney biopsy in patients with kidney disease.BMC nephrology · 2026Article
- Unmet needs and challenges in the development of noninvasive diagnostics for kidney disease.Kidney research and clinical practice · 2026Article
- CT-based renal and body-composition radiomics model to improve the detection ability of diabetic kidney disease in patients with type 2 diabetes mellitus.Frontiers in endocrinology · 2026Article
- Dual-phase CT radiomics for acute kidney injury prediction after out-of-hospital cardiac arrest.Frontiers in radiology · 2026Article
- Accurate Clinical Entity Recognition and Code Mapping of Anatomopathological Reports Using BioClinicalBERT Enhanced by Retrieval-Augmented Generation: A Hybrid Deep Learning Approach.Bioengineering (Basel, Switzerland) · 2025Article
- Machine learning and quantitative computed tomography radiomics prediction of postoperative functional recovery in paraplegic dogs.Veterinary surgery : VS · 2025Article
- Evaluating sequence contributions to MRI radiomics for glioblastoma survival: single vs fusion models.Frontiers in oncology · 2025Article
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12 authors.
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Abstract
backgroundKidney biopsy is the standard of care for the diagnosis of various kidney diseases. In particular, chronic histopathologic lesions, such as interstitial fibrosis and tubular atrophy, can provide prognostic information regarding chronic kidney disease progression. In this study, we aimed to evaluate historadiological correlations between CT-based radiomic features and chronic histologic changes in native kidney biopsies and to construct and validate a radiomics-based prediction model for chronicity grade.
methodsWe included patients aged ≥ 18 years who underwent kidney biopsy and abdominal CT scan within a week before kidney biopsy. Left kidneys were three-dimensionally segmented using a deep learning model based on the 3D Swin UNEt Transformers architecture. We additionally defined isovolumic cortical regions of interest near the lower pole of the left kidneys. Shape, first-order, and high-order texture features were extracted after resampling and kernel normalization. Correlations and diagnostic metrics between extracted features and chronic histologic lesions were examined. A machine learning-based radiomic prediction model for moderate chronicity was developed and compared according to the segmented regions of interest (ROI).
resultsOverall, moderate correlations with statistical significance (P < 0.05) were found between chronic histopathologic grade and top-ranked radiomic features. Total parenchymal features were more strongly correlated than cortical ROI features, and texture features were more highly ranked. However, conventional imaging markers, including kidney length, were poorly correlated. Top-ranked individual radiomic features had areas under receiver operating characteristic curves (AUCs) of 0.65 to 0.74. Developed radiomics models for moderate-to-severe chronicity achieved AUCs of 0.89 (95% confidence interval [CI] 0.75-0.99) and 0.74 (95% CI 0.52-0.93) for total parenchymal and cortical ROI features, respectively.
conclusionSignificant historadiological correlations were identified between CT-based radiomic features and chronic histologic changes in native kidney biopsies. Our findings underscore the potential of CT-based radiomic features and their prediction model for the non-invasive assessment of kidney fibrosis.
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