Evidence map›Paper›PMID 41188746›Full record

ArticleBMC medical imaging2025

Association of quantitative renal surface nodularity with the renal dysfunction progression in patients with arterial hypertension.

Qiong Wu, Jun Zhou, Haotian Yang, Liang Pan, Wei Xing, Jie Chen, Jiule Ding

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Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Qiong WuDepartment of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, 213003, China.
Jun ZhouDepartment of Radiology, The Changzhou No. 2 People's Hospital, Changzhou, Jiangsu, 213003, China.
Haotian YangDepartment of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, 213003, China.
Liang PanDepartment of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, 213003, China.
Wei XingDepartment of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, 213003, China.
Jie ChenDepartment of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, 213003, China.
Jiule DingDepartment of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, 213003, China. dingjiule@163.com.

Funding

Top Talent of Changzhou in the 14th Five-Year Plan 77
6 · The paper itself

Abstract

backgroundHypertensive nephropathy, a major cause of end-stage renal disease, lacks reliable noninvasive biomarkers. Renal surface nodularity (RSN) on CT may reflect nephrosclerosis, but existing methods fail to quantify nodularity heterogeneity or predict renal decline. This study developed a novel CT-based RSN metric and assessed its prognostic value in hypertensive patients.

methodsThis retrospective cohort study included hypertensive patients who underwent contrast-enhanced CT. Patients with bilateral renal surface irregularities were assigned to the RSN group, with age- (± 2 year) and sex-matched controls (non-RSN group) randomly selected at a 1:1 ratio. RSN was quantified using three surface roughness metrics. A semi-quantitative RSN score was also calculated based on the distribution and depth (> 50% cortical thickness) of localized surface defects. The primary endpoint was composite renal dysfunction progression (≥ 25% eGFR decline or initiation of renal replacement therapy). The secondary endpoint was defined as an annual decline in eGFR > 5 ml/min/1.73 m² per year.

resultsA total of 242 patients were included (median age, 64 years; 70.25% male). Strong linear correlations were observed between quantitative RSN metrics and semi-quantitative score (all ρ > 0.9, P < 0.001). Over a median follow-up of 38.00 months (Interquartile range: 22.00-56.25), patients in the RSN group had a significantly higher risk of renal dysfunction progression (33 vs. 5 events; HR = 5.22, P < 0.001). After adjusting sex, age, hypertension grade, diabetes, hyperlipidemia, eGFR, and renal volume, renal dysfunction progression was independently associated with arithmetic mean deviation (HR = 1.798, 95% CI: 1.296-2.495, P < 0.001), maximum height (HR = 1.606, 95% CI: 1.177-2.191, P = 0.003), and ten-point unevenness height (HR = 2.239, 95%CI: 1.567-3.197, P < 0.001), respectively. The similar statistical results trend was present between RSN and the secondary endpoint.

conclusionsCT-based quantitative RSN was developed as a novel imaging biomarker for renal dysfunction progression in patients with arterial hypertension.

Indexed as

HypertensionHypertension, RenalKidneyTomography, X-Ray ComputedAgedDisease ProgressionFemaleGlomerular Filtration RateHumansMaleMiddle AgedNephritisPrognosisRetrospective StudiesComputed tomographyHypertensionRenal dysfunctionRenal surface nodularity

Identifiers

PMID41188746
PMCPMC12584488

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.