Evidence map›Paper›PMID 38411750›Full record

ArticleInsights into imaging2024

CT and MRI radiomics of bone and soft-tissue sarcomas: an updated systematic review of reproducibility and validation strategies.

Salvatore Gitto, Renato Cuocolo, Merel Huisman, Carmelo Messina, Domenico Albano, Patrick Omoumi, Elmar Kotter, Mario Maas, Peter Van Ooijen, Luca Maria Sconfienza

Abstract read
In one paragraph

Article in Insights into imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing 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

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

  1. Pooled it
  2. Pooled it
  3. Review
  4. Article
  5. Article
  6. Diagnosis and Management of Soft-Tissue Sarcomas: Current Insights.Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2026
    Review
  7. Review
  8. Review
  9. Article
  10. Review
  11. Article
  12. Article
  13. Review
  14. Review
  15. Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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.

Salvatore GittoDipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milan, Italy.
Renato CuocoloDepartment of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy.
Merel HuismanRadboud University Medical Center, Department of Radiology and Nuclear Medicine, Nijmegen, The Netherlands.
Carmelo MessinaDipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milan, Italy.
Domenico AlbanoIRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
Patrick OmoumiDepartment of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Elmar KotterDepartment of Radiology, Freiburg University Medical Center, Freiburg, Germany.
Mario MaasDepartment of Radiology and Nuclear Medicine, Amsterdam UMC Location University of Amsterdam, Amsterdam, The Netherlands.
Peter Van OoijenDepartment of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
Luca Maria SconfienzaDipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milan, Italy. io@lucasconfienza.it.ORCID http://orcid.org/0000-0003-0759-8431

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo systematically review radiomic feature reproducibility and model validation strategies in recent studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas, thus updating a previous version of this review which included studies published up to 2020.

methodsA literature search was conducted on EMBASE and PubMed databases for papers published between January 2021 and March 2023. Data regarding radiomic feature reproducibility and model validation strategies were extracted and analyzed.

resultsOut of 201 identified papers, 55 were included. They dealt with radiomics of bone (n = 23) or soft-tissue (n = 32) tumors. Thirty-two (out of 54 employing manual or semiautomatic segmentation, 59%) studies included a feature reproducibility analysis. Reproducibility was assessed based on intra/interobserver segmentation variability in 30 (55%) and geometrical transformations of the region of interest in 2 (4%) studies. At least one machine learning validation technique was used for model development in 34 (62%) papers, and K-fold cross-validation was employed most frequently. A clinical validation of the model was reported in 38 (69%) papers. It was performed using a separate dataset from the primary institution (internal test) in 22 (40%), an independent dataset from another institution (external test) in 14 (25%) and both in 2 (4%) studies.

conclusionsCompared to papers published up to 2020, a clear improvement was noted with almost double publications reporting methodological aspects related to reproducibility and validation. Larger multicenter investigations including external clinical validation and the publication of databases in open-access repositories could further improve methodology and bring radiomics from a research area to the clinical stage. CRITICAL RELEVANCE STATEMENT: An improvement in feature reproducibility and model validation strategies has been shown in this updated systematic review on radiomics of bone and soft-tissue sarcomas, highlighting efforts to enhance methodology and bring radiomics from a research area to the clinical stage. KEY POINTS: • 2021-2023 radiomic studies on CT and MRI of musculoskeletal sarcomas were reviewed. • Feature reproducibility was assessed in more than half (59%) of the studies. • Model clinical validation was performed in 69% of the studies. • Internal (44%) and/or external (29%) test datasets were employed for clinical validation.

Indexed as

Artificial intelligenceRadiomicsSarcomaTexture analysis

Identifiers

PMID38411750
PMCPMC10899555

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.