ArticleAbdominal radiology (New York)2022
The predictive value of renal parenchymal information for renal function impairment in patients with ADPKD: a multicenter prospective study.
Article in Abdominal radiology (New York), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Diffusion MRI Radiomics Predict Renal Function Decline in ADPKD.Kidney international reports · 2026Article
- Artificial intelligence-based method for renal function automatic assessment of each kidney using plain computed tomography (CT) scans.Physical and engineering sciences in medicine · 2026Article
- Developing a CT radiomics-based model for assessing split renal function using machine learning.Japanese journal of radiology · 2025Article
- A novel CT-based radiomics approach for kidney function evaluation in ADPKD: a pilot study.Clinical kidney journal · 2025Article
- Reliability of radiomic analysis on multiparametric MRI for patients affected by autosomal dominant polycystic kidney disease.Scientific reports · 2025Article
- Magnetic resonance imaging preprocessing and radiomic features for classification of autosomal dominant polycystic kidney disease genotype.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Diffusion magnetic resonance imaging for kidney cyst volume quantification and non-cystic tissue characterisation in ADPKD.European radiology · 2023Article
- Abdominal Imaging in ADPKD: Beyond Total Kidney Volume.Journal of clinical medicine · 2023Review
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6 authors.
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Abstract
objectiveAlthough the guideline indicates that total kidney volume (TKV) is an important detection indicator in patients with autosomal dominant polycystic kidney disease (ADPKD), this study attempted to demonstrate that renal parenchymal information, combined with parenchymal volume and radiomics features, may have more valuable clinical guiding significance.
methodsA totals of 340 ADPKD patients with normal renal function were prospectively collected and followed-up for five years, with renal function tests and non-contrast computed tomography (CT) performed every six months. The relationship between renal function impairment and renal parenchymal volume (RPV) as along with radiomics features was explored using a multiple linear regression model and multiple logistic regression. Then, a combined model of RPV with radiomics features was constructed to comprehensively evaluate its predictive value.
resultsCompared with TKV, decreased RPV presented a closer relationship with renal function impairment, namely, with the impairment rate (RPV: 82.3% vs. TVK: 67.1%) and eGFR (RPV: r = 0.614, p < 0.001 vs. TKV: r = -0.452, p < 0.001), and showed higher predictive power (RPV: AUC = 0.752 [95%CI 0.692-0.805], p < 0.001 vs. TKV: AUC = 0.711 [95%CI 0.649-0.768], p < 0.001). Correspondingly, the radiomics analysis that was derived from the renal parenchyma also showed a satisfactory result (AUC = 0.849 [95%Cl 0.797-0.892], p < 0.001). Importantly, the predictive power for renal function impairment was further improved in the combined model of RPV and radiomics features (AUC = 0.902 [95%Cl 0.857-0.937], p < 0.001).
conclusionRenal parenchyma information may be a sensitive biomarker of renal function impairment in ADPKD, which can provide a new approach for clinically monitoring renal function, and may greatly improve the pre-hospital prevention and treatment effects.
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