Evidence map›Paper›PMID 38332405›Full record

ArticleJournal of imaging informatics in medicine2024

Effects of Interobserver Segmentation Variability and Intensity Discretization on MRI-Based Radiomic Feature Reproducibility of Lipoma and Atypical Lipomatous Tumor.

Salvatore Gitto, Renato Cuocolo, Vincenzo Giannetta, Julietta Badalyan, Filippo Di Luca, Stefano Fusco, Giulia Zantonelli, Domenico Albano, Carmelo Messina, Luca Maria Sconfienza

Open access · hybridAbstract read
In one paragraph

Article in Journal of imaging informatics in medicine, 2024. 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
1.6field-weighted citation impact, top 18% 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, 4 citations in OpenAlex.

  1. Article
  2. 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

10 authors at 4 institutions in 1 country.

Salvatore GittoIRCCS Istituto Ortopedico Galeazzi, Via Cristina Belgioioso 173, 20157, Milan, Italy.
Renato CuocoloDepartment of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy.
Vincenzo GiannettaDiagnostic and Interventional Radiology Department, IRCCS Ospedale San Raffaele-Turro, Università Vita-Salute San Raffaele, Milan, Italy.
Julietta BadalyanScuola Di Specializzazione in Statistica Sanitaria E Biometria, Università Degli Studi Di Milano, Milan, Italy.
Filippo Di LucaScuola Di Specializzazione in Radiodiagnostica, Università Degli Studi Di Milano, Milan, Italy.
Stefano FuscoDipartimento Di Scienze Biomediche Per La Salute, Università Degli Studi Di Milano, Milan, Italy.
Giulia ZantonelliDipartimento Di Scienze Biomediche Per La Salute, Università Degli Studi Di Milano, Milan, Italy.
Domenico AlbanoIRCCS Istituto Ortopedico Galeazzi, Via Cristina Belgioioso 173, 20157, Milan, Italy.
Carmelo MessinaIRCCS Istituto Ortopedico Galeazzi, Via Cristina Belgioioso 173, 20157, Milan, Italy.
Luca Maria SconfienzaIRCCS Istituto Ortopedico Galeazzi, Via Cristina Belgioioso 173, 20157, Milan, Italy. io@lucasconfienza.it.
University of Milan · ITIstituto Ortopedico Galeazzi · ITIRCCS Ospedale San Raffaele · ITUniversity of Salerno · IT

Funding

Fondazione AIRC per la Ricerca sul Cancro Investigator GrantInternational Skeletal Society (ISS) Early Career Grant
6 · The paper itself

Abstract

Segmentation and image intensity discretization impact on radiomics workflow. The aim of this study is to investigate the influence of interobserver segmentation variability and intensity discretization methods on the reproducibility of MRI-based radiomic features in lipoma and atypical lipomatous tumor (ALT). Thirty patients with lipoma or ALT were retrospectively included. Three readers independently performed manual contour-focused segmentation on T1-weighted and T2-weighted sequences, including the whole tumor volume. Additionally, a marginal erosion was applied to segmentations to evaluate its influence on feature reproducibility. After image pre-processing, with included intensity discretization employing both fixed bin number and width approaches, 1106 radiomic features were extracted from each sequence. Intraclass correlation coefficient (ICC) 95% confidence interval lower bound ≥ 0.75 defined feature stability. In contour-focused vs. margin shrinkage segmentation, the rates of stable features extracted from T1-weighted and T2-weighted images ranged from 92.68 to 95.21% vs. 90.69 to 95.66% after fixed bin number discretization and from 95.75 to 97.65% vs. 95.39 to 96.47% after fixed bin width discretization, respectively, with no difference between the two segmentation approaches (p ≥ 0.175). Higher stable feature rates and higher feature ICC values were found when implementing discretization with fixed bin width compared to fixed bin number, regardless of the segmentation approach (p < 0.001). In conclusion, MRI radiomic features of lipoma and ALT are reproducible regardless of the segmentation approach and intensity discretization method, although a certain degree of interobserver variability highlights the need for a preliminary reliability analysis in future studies.

Indexed as

LipomaMagnetic Resonance ImagingObserver VariationAdultAgedFemaleHumansImage Processing, Computer-AssistedMaleMiddle AgedRadiomicsReproducibility of ResultsRetrospective StudiesArtificial intelligenceAtypical lipomatous tumorLipomaRadiomicsTexture analysis

Identifiers

PMID38332405
PMCPMC11169199
OpenAlexW4391659079

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.