Evidence map›Paper›PMID 42662913›Full record

ArticleKidney international reports2026

Diffusion MRI Radiomics Predict Renal Function Decline in ADPKD.

Matteo Bargagli, Luisa Altabella, Marina Fedon Vocaturo, Riccardo Negrelli, Maria Chiara Ambrosetti, Giovanni Puppini, Carlo Cavedon, Martin Scoglio, Daniel G Fuster, Pietro Manuel Ferraro

Abstract read
In one paragraph

Article in Kidney international reports, 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

What it found

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Matteo BargagliDepartment of Nephrology and Hypertension, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
Luisa AltabellaMedical Physics Unit, Azienda Ospedaliera Universitaria Integrata, Verona, Italy.
Marina Fedon VocaturoMedical Physics Unit, Azienda Ospedaliera Universitaria Integrata, Verona, Italy.
Riccardo NegrelliRadiology Unit, Azienda Ospedaliera Universitaria Integrata, Verona, Italy.
Maria Chiara AmbrosettiRadiology Unit, Azienda Ospedaliera Universitaria Integrata, Verona, Italy.
Giovanni PuppiniRadiology Unit, Azienda Ospedaliera Universitaria Integrata, Verona, Italy.
Carlo CavedonMedical Physics Unit, Azienda Ospedaliera Universitaria Integrata, Verona, Italy.
Martin ScoglioDepartment of Nephrology and Hypertension, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
Daniel G FusterDepartment of Nephrology and Hypertension, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
Pietro Manuel FerraroSection of Nephrology, Department of Medicine, Università degli Studi di Verona; Nephrology Unit, Azienda Ospedaliera Universitaria Integrata Verona, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary kidney disorder and a leading cause of end-stage kidney disease (ESKD). Current risk stratification relies on height-adjusted total kidney volume (ht-TKV), which captures cyst burden but not alterations in the noncystic parenchyma. Radiomics enables extraction of quantitative features reflecting tissue microstructure across both cystic and noncystic kidney compartments. We investigated whether radiomic features from apparent diffusion coefficient (ADC) maps improve prediction of renal function decline in ADPKD. Methods: We analyzed 112 patients with ADPKD from the Bern ADPKD Registry with baseline magnetic resonance imaging (MRI, T1-, T2-, and diffusion-weighted). Kidneys were manually segmented, and radiomic features were extracted using PyRadiomics. Features were selected using least absolute shrinkage and selection operator, and models were developed using support vector machines with 20-fold cross-validation. The outcome was rapid renal function decline (estimated glomerular filtration rate [eGFR] ≤ -3 ml/min per 1.73 m Results: ADC-based radiomics provided the highest area under the curve (AUC: 0.82), compared with clinical (0.77) and T1/T2w models (0.77). Ensemble models integrating ADC, T1/T2w, and clinical data achieved the best AUC (0.85) with improved calibration. Net reclassification improvement (NRI) analyses demonstrated improved risk reclassification when ADC-derived information was incorporated into clinical and conventional MRI-based models. Conclusion: ADC-derived radiomic features showed promising prognostic value for predicting renal function decline in ADPKD and provided incremental information beyond ht-TKV and current clinical standards when integrated with clinical and conventional MRI-based models, capturing both cystic and noncystic tissue alterations. If confirmed in independent cohorts, diffusion MRI-based radiomics may support earlier risk stratification and treatment decision-making in ADPKD.

Indexed as

ADPKDdiffusionmachine learningMRIradiomics

Identifiers

PMID42662913
PMCPMC13521158

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