Evidence map›Paper›PMID 41682554›Full record

SynthesisSensors (Basel, Switzerland)2026

Image Quality Standardization in Radiomics: A Systematic Review of Artifacts, Variability, and Feature Stability.

Francesco Felicetti, Francesco Lamonaca, Domenico Luca Carnì, Sandra Costanzo

Abstract readSystematic ReviewReview
In one paragraph

Synthesis in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 2 pooled it
–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

5 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Assessing Respiratory Motion Stability of NovelDiagnostics (Basel, Switzerland) · 2026
    Article
  4. Article
  5. Article
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.

Francesco FelicettiDepartment of Computer Engineering, Modeling, Electronics and Systems Engineering, University of Calabria, Ponte P. Bucci, 87036 Rende, Italy.ORCID 0009-0005-0933-9902
Francesco LamonacaDepartment of Computer Engineering, Modeling, Electronics and Systems Engineering, University of Calabria, Ponte P. Bucci, 87036 Rende, Italy.ORCID 0000-0002-6263-8929
Domenico Luca CarnìDepartment of Computer Engineering, Modeling, Electronics and Systems Engineering, University of Calabria, Ponte P. Bucci, 87036 Rende, Italy.ORCID 0000-0002-9619-2742
Sandra CostanzoDepartment of Computer Engineering, Modeling, Electronics and Systems Engineering, University of Calabria, Ponte P. Bucci, 87036 Rende, Italy.ORCID 0000-0002-8028-2268

Funding

Italian Ministry for Research RADIOAMICA- Open Network per la RADIOm- 446 ica/rAdiogenoMIca Cooperativa basata su intelligenza Artificiale
6 · The paper itself

Abstract

This paper explores the role of metrology in the assessment of image quality in the field of radiomics. Image Quality Assessment (IQA) is central to ensuring the reliability and reproducibility of radiomic analyses, as it directly affects the accuracy of feature extraction and segmentation, ultimately impacting diagnostic outcomes. From the analysis of approximately 20,000 papers sourced from three databases (PubMed, Scopus, IEEE Xplore), last searched in December 2025, the need for standardized imaging protocols and quality control measures emerges as a critical theme. Studies were included if they involved radiomic feature extraction and evaluated the impact of image quality variations on feature robustness and no formal risk-of-bias assessment was performed. A total of 105 studies were included, covering different medical imaging modalities. Across the included studies, noise, motion, acquisition and reconstruction parameters, and other artifacts consistently emerged as major sources of radiomic feature instability. Indeed, in most papers, IQA is neglected, while the effect of poor-quality images is reported. This research identifies and discusses the relevant issues reported in clinical practice, as well as the main metrics adopted for image quality evaluation. Through a comprehensive review of current literature and an analysis of emerging trends, this paper highlights the urgent need for innovative solutions in image quality metrics tailored to radiomics applications.

Indexed as

Diagnostic ImagingImage Processing, Computer-AssistedRadiomicsArtifactsHumansQuality ControlReproducibility of Resultsartifactsfeature extractionimage quality assessmentmeasurementspersonalized medicineradiomicsstandardization

Identifiers

PMID41682554
PMCPMC12900098

What OpenQuestion holds

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Registered trials

None linked

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.