Evidence map›Paper›PMID 40360663›Full record

ArticleScientific reports2025

Reliability of radiomic analysis on multiparametric MRI for patients affected by autosomal dominant polycystic kidney disease.

Francesca Lussana, Ettore Lanzarone, Giulia Villa, Alfonso Mastropietro, Anna Caroli, Elisa Scalco

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

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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

6 authors.

Francesca LussanaDepartment of Management, Information and Production Engineering, University of Bergamo, 24044, Dalmine, BG, Italy.
Ettore LanzaroneDepartment of Management, Information and Production Engineering, University of Bergamo, 24044, Dalmine, BG, Italy.
Giulia VillaBioengineering Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, 24020, Ranica, BG, Italy.
Alfonso MastropietroInstitute of Intelligent Industrial Technologies and Systems, Italian National Research Council (STIIMA-CNR), 20133, Milan, Italy.
Anna CaroliBioengineering Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, 24020, Ranica, BG, Italy.
Elisa ScalcoInstitute of Biomedical Technologies, Italian National Research Council (ITB-CNR), 20054, Segrate, MI, Italy. elisa.scalco@cnr.it.

Funding

Ministero dell'Università e della Ricerca 2022B23JT5
6 · The paper itself

Abstract

Autosomal dominant polycystic kidney disease (ADPKD) is a prevalent hereditary disorder characterized by the development and growth of fluid-filled cysts, resulting in a decline in kidney function. Beyond total kidney and cyst volume quantification, non-cystic tissue characterization by multi-parametric MRI (mp-MRI) and radiomics holds promise. We conducted a radiomic analysis based on reproducible and informative features extracted from non-cystic tissue on mp-MRI in ADPKD patients. T2-weighted (T2-w), T1-weighted MRI (T1-w), and IntraVoxel Incoherent Motion (IVIM) maps from Diffusion Weighted Imaging (DWI) were considered. The reliability of radiomic features was evaluated using five different segmentation methods. The impact of segmentation variability on radiomic reproducibility was assessed through Intraclass Correlation Coefficients (ICC), and a preliminary correlation analysis with relevant clinical parameters, such as age and eGFR, was also performed. The results from 14 patients indicate that radiomic features derived from IVIM maps exhibit greater reliability compared to features from T1-w and T2-w for characterizing non-cystic tissue in ADPKD patients, also showing a moderate correlation with age and eGFR. Additionally, lower-order features, including those computed from histograms and co-occurrence matrices, demonstrate higher reproducibility than other texture features.

Indexed as

Magnetic Resonance ImagingMultiparametric Magnetic Resonance ImagingPolycystic Kidney, Autosomal DominantAdultAgedDiffusion Magnetic Resonance ImagingFemaleHumansImage Processing, Computer-AssistedKidneyMaleMiddle AgedRadiomicsReproducibility of ResultsADPKDmp-MRIRadiomics

Identifiers

PMID40360663
PMCPMC12075844

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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.