Evidence map›Paper›PMID 41882619›Full record

ArticleBMC nephrology2026

Automated CT segmentation of kidneys successfully quantifies changes in total kidney volume in ICU patients, a retrospective cohort study.

A G W Biersma, B van Leer, M H Renes, J Pillay, J Koeze

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Article in BMC nephrology, 2026. 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

5 authors.

A G W BiersmaDepartment of Critical Care, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands. agwbiersma@gmail.com.ORCID 0009-0006-3339-1126
B van LeerDepartment of Critical Care, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
M H RenesDepartment of Critical Care, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
J PillayDepartment of Critical Care, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
J KoezeDepartment of Critical Care, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundKidney function is associated with kidney volume. This study aims to explore automated segmentation for measuring total kidney volume (TKV) and to analyse the association between (changes in) TKV and acute kidney injury (AKI) incidence and/or severity in Intensive Care Unit (ICU) patients.

methodsPatients were included in this retrospective pilot cohort study when at least two abdominal Computed Tomography (CT) scans were performed during ICU admission. If available, CT scans made before the ICU admission were included as a baseline scan. TKV was measured by automated segmentation of both kidneys using Data Analysis Facilitation Suite (DAFS, Voronoi Analytics Incorporated). All segmentations were visually checked and manually adjusted when necessary. ΔTKV was calculated between baseline CT and CT1 (ΔTKVCT1–baseline) and CT1 and CT2 (ΔTKVCT2–CT1). Primary outcomes were differences in kidney volume before and after manual correction and AKI incidence and severity, per the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines, on the day of scanning.

resultsTwenty-six patients were included, of whom eighteen developed AKI during ICU admission. Analysis showed no significant differences in volumes before and after manual correction of the automated segmentations. TKV was not associated with AKI incidence or severity. Longitudinal intraindividual changes in TKV were observed. Median ΔTKVCT1–baseline was statistically significantly different for AKI versus non-AKI patients (−22 cm3 (−49–9) versus 42 cm3 (23–43), p = 0.03) and for different KDIGO stages.

conclusionThis study demonstrates the possibility of measuring TKV on CT in ICU patients using automated segmentation. Longitudinal intraindividual changes in TKV were observed, however, no clear association between TKV and AKI was found. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Acute Kidney InjuryIntensive Care UnitsKidneyTomography, X-Ray ComputedAgedCohort StudiesFemaleHumansMaleMiddle AgedOrgan SizePilot ProjectsRetrospective StudiesAcute kidney injuryAutomated segmentationComputed tomographyIntensive careTotal kidney volume

Identifiers

PMID41882619
PMCPMC13023156

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