Evidence map›Paper›PMID 38156331›Full record

ArticleJournal of medical imaging (Bellingham, Wash.)2023

Magnetic resonance imaging preprocessing and radiomic features for classification of autosomal dominant polycystic kidney disease genotype.

Linnea E Kremer, Arlene B Chapman, Samuel G Armato

Open access · greenAbstract read
In one paragraph

Article in Journal of medical imaging (Bellingham, Wash.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.7field-weighted citation impact, top 27% of its field
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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
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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

3 authors at 1 institution in 1 country.

Linnea E KremerThe University of Chicago, Committee on Medical Physics, Department of Radiology, Chicago, Illinois, United States.ORCID https://orcid.org/0000-0003-1076-5544
Arlene B ChapmanThe University of Chicago, Department of Medicine, Chicago, Illinois, United States.
Samuel G ArmatoThe University of Chicago, Committee on Medical Physics, Department of Radiology, Chicago, Illinois, United States.ORCID https://orcid.org/0000-0002-5428-7997
University of Chicago · US

Funding

Training Core for Chicago KUH FORWARDTL1DK132769 · NIDDK · NORTHWESTERN UNIVERSITY AT CHICAGO · PI Tamara Isakova · 2021 to 2026
$4.0M
Professional Development Core for Chicago KUH FORWARDU2CDK129917 · NIDDK · NORTHWESTERN UNIVERSITY AT CHICAGO · PI SUSAN E. QUAGGIN · 2021 to 2026
$2.9M
NIDDK NIH HHS TL1 DK132769NIDDK NIH HHS U2C DK129917
6 · The paper itself

Abstract

Purpose: Our study aims to investigate the impact of preprocessing on magnetic resonance imaging (MRI) radiomic features extracted from the noncystic kidney parenchyma of patients with autosomal dominant polycystic kidney disease (ADPKD) in the task of classifying PKD1 versus PKD2 genotypes, which differ with regard to cyst burden and disease outcome. Approach: The effect of preprocessing on radiomic features was investigated using a single T2-weighted fat saturated (T2W-FS) MRI scan from PKD1 and PKD2 subjects (29 kidneys in total) from the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease study. Radiomic feature reproducibility using the intraclass correlation coefficient (ICC) was computed across MRI normalizations ( Results: Radiomic features extracted from the noncystic kidney parenchyma were sensitive to preprocessing parameters, with varying reproducibility depending on the parameter. The percentage of features with good-to-excellent ICC scores ranged from 14% to 58%. AUC values ranged between 0.47 to 0.68 and 0.56 to 0.73 for the noncystic kidney parenchyma and entire kidney, respectively. Conclusions: Reproducibility of radiomic features extracted from the noncystic kidney parenchyma was dependent on the preprocessing parameters used, and the effect on genotype classification was sensitive to preprocessing parameters. The results suggest that texture features may be indicative of genotype expression in ADPKD.

Indexed as

autosomal dominant polycystic kidney diseasecomputer-aided diagnosismagnetic resonance imagingpreprocessingradiomics

Identifiers

PMID38156331
PMCPMC10752557
OpenAlexW4390263916

What OpenQuestion holds

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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 OpenQuestion graph.