Evidence map›Paper›PMID 41535705›Full record

ArticleCommunications medicine2026

Natural history of simple and complex cysts in autosomal dominant polycystic kidney disease on MRI.

Zhongxiu Hu, Caden Li, Jon D Blumenfeld, Martin R Prince

Abstract read
In one paragraph

Article in Communications medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Zhongxiu HuWeill Cornell Medicine, Department of Radiology, New York, NY, USA.ORCID http://orcid.org/0009-0009-9258-5459
Caden LiWeill Cornell Medicine, Department of Radiology, New York, NY, USA.
Jon D BlumenfeldThe Rogosin Institute, New York, NY, USA.
Martin R PrinceWeill Cornell Medicine, Department of Radiology, New York, NY, USA. map2008@med.cornell.edu.ORCID http://orcid.org/0000-0002-9883-0584

Funding

AI-Based Image Phenotyping of Early ADPKDU54DK144866 · NIDDK · WEILL MEDICAL COLL OF CORNELL UNIV · PI MARTIN R PRINCE, Mert Rory Sabuncu · 2025 to 2026
$918k
NIDDK NIH HHS U54 DK144866
6 · The paper itself

Abstract

backgroundKidney volume, reflecting cumulative effects of many cysts, is an important prognostic biomarker for autosomal dominant polycystic kidney disease (ADPKD) but fails in many patients. Tracking individual cysts may more directly assess disease progression.

methodsIndividual cysts (n = 299) from 37 subjects were evaluated retrospectively over ≥ 8 years by serial MRI (mean follow-up = 11 years). Cysts were labeled on every available MRI scan, totaling 1654 contours (median timepoints per cyst = 5). Effects of cyst location, morphology, and growth pattern on kidney function decline were evaluated by univariate and multivariate analyses.

resultsSimple, T2-bright cysts follow logistic growth (median cyst growth rate = 11%/year). A subset (94/222, 42%) transitions over time to shrinking, to complex solid-fluid/fluid-fluid cysts, then to homogeneously T1-bright cysts and finally disappearing. By contrast, T1-bright complex cysts have no volume change (median cyst growth rate = 0%/year; p < 0.001). On multivariate analysis, faster kidney function decline is associated with simple cyst diameter > 2 cm on index scan (p = 0.007) and simple cyst transitions (p = 0.02). There is a trend towards faster kidney function decline with higher simple cyst growth rate (p = 0.16).

conclusionsProfiling individual cysts on serial MRI to identify transitions as well as size and growth rate may improve predictions of ADPKD progression and treatment response.

Identifiers

PMID41535705
PMCPMC12852659

What Socratic holds

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LicenceCC BY-NC-ND
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Registered trials

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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 Socratic graph.