Cited by 22Radiomics and Machine Learning in Medical ImagingMaternal and fetal healthcareRadiology practices and education

Placenta Accreta Spectrum Disorders and Radiomics: Systematic review and quality appraisal

Stanzione, Arnaldo, Verde, Francesco, Cuocolo, Renato, Romeo, Valeria, Paolo Mainenti, Pier, Brunetti, Arturo, Maurea, Simone·2022·European Journal of Radiology

Abstract

Purpose Ultrasound and magnetic resonance imaging are the imaging modalities of choice for placenta accrete spectrum (PAS) disorders assessment. Radiomics could further increase the value of medical images and allow to overcome the limitations linked to their visual assessment. Aim of this systematic review was to identify and appraise the methodological quality of radiomics studies focused PAS disorders applications.Method Three online databases (PubMed, Scopus and Web of Science) were searched to identify original research articles on human subjects published in English. For the qualitative synthesis of results, data regarding study design (e.g., retrospective or prospective), purpose, patient population (e.g., sample size), imaging modalities and radiomics pipelines (e.g., segmentation and feature extraction strategy) were collected. The appraisal of methodological quality was performed using the Radiomics Quality Score (RQS).Results 10 articles were finally included and analyzed. All were retrospective and MRI-powered. The majority included more than 100 patients (6/10). Four were prognostic (focused on either the prediction of bleeding volume or the prediction of needed management) while six diagnostic (PAS vs not PAS classification) studies. The median RQS was 8, with maximum and minimum respectively equal to 17/36 and - 6/36. Major methodological concerns were the lack of feature stability to multiple segmentation testing and poor data openness.Conclusions Radiomics studies focused on PAS disorders showed a heterogeneous methodological quality, overall lower than desirable. Furthermore, many relevant research questions remain unexplored. More robust investigations are needed to foster advancements in the field and possibly clinical translation.

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Crossref · 10.1016/j.ejrad.2022.110497

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References 38 · Cited by 22

Authors
Stanzione, A., Verde, F., Cuocolo, R., Romeo, V., Paolo Mainenti, P., Brunetti, A., & Maurea, S.
Affiliations
  • University of Naples Federico II (IT)
  • University of Salerno (IT)
Corresponding author
Verde, Francesco
Venue
European Journal of Radiology
Publisher
Elsevier BV
Published
2022-10
Volume
155
Pages
110497
ISSN
0720-048X (ISSN-L)
OpenAlex
W4292622287
Cited by
22
References
38
Citations by year
2026: 5 · 2025: 3 · 2024: 7 · 2023: 5 · 2022: 2
FWCI
0.72
Type
journal article
Language
en
Open access
closed
Locations
  • European Journal of Radiology (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Maternal and fetal healthcare
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiology practices and education
Keywords
placenta accreta spectrum, radiomics, methodological quality, radiomics quality score, magnetic resonance imaging, ultrasound, feature extraction, diagnostic classification, feature stability
MeSH terms
Female, Humans, Magnetic Resonance Imaging, Placenta Accreta, Pregnancy, Prognosis, Prospective Studies, Retrospective Studies

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