Ovarian imaging radiomics quality score assessment: an EuSoMII radiomics auditing group initiative

Andrea Ponsiglione; Arnaldo Stanzione; Gaia Spadarella; Agah Baran; Luca Alessandro Cappellini; Kevin Groot Lipman; Peter Van Ooijen; Renato Cuocolo

doi:10.1007/s00330-022-09180-w

Ovarian imaging radiomics quality score assessment: an EuSoMII radiomics auditing group initiative

Eur Radiol. 2023 Mar;33(3):2239-2247. doi: 10.1007/s00330-022-09180-w. Epub 2022 Oct 27.

Authors

Andrea Ponsiglione¹, Arnaldo Stanzione², Gaia Spadarella¹, Agah Baran³, Luca Alessandro Cappellini⁴, Kevin Groot Lipman⁵, Peter Van Ooijen^{6

7}, Renato Cuocolo^{8

9}

Affiliations

¹ Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy.
² Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy. arnaldo.stanzione@unina.it.
³ Department of Diagnostic and Interventional Radiology, University of Cologne, Cologne, Germany.
⁴ Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.
⁵ Department of Radiology, Netherlands Cancer Institute, Amsterdam, Netherlands.
⁶ Department of Radiation Oncology, University Medical Center Groningen, Groningen, The Netherlands.
⁷ Machine Learning Lab, Data Science Center in Health, University Medical Center Groningen, Groningen, the Netherlands.
⁸ Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy.
⁹ Augmented Reality for Health Monitoring Laboratory (ARHeMLab), Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Naples, Italy.

Abstract

Objective: To evaluate the methodological rigor of radiomics-based studies using noninvasive imaging in ovarian setting.

Methods: Multiple medical literature archives (PubMed, Web of Science, and Scopus) were searched to retrieve original studies focused on computed tomography (CT), magnetic resonance imaging (MRI), ultrasound (US), or positron emission tomography (PET) radiomics for ovarian disorders' assessment. Two researchers in consensus evaluated each investigation using the radiomics quality score (RQS). Subgroup analyses were performed to assess whether the total RQS varied according to first author category, study aim and topic, imaging modality, and journal quartile.

Results: From a total of 531 items, 63 investigations were finally included in the analysis. The studies were greatly focused (94%) on the field of oncology, with CT representing the most used imaging technique (41%). Overall, the papers achieved a median total RQS 6 (IQR, -0.5 to 11), corresponding to a percentage of 16.7% of the maximum score (IQR, 0-30.6%). The scoring was low especially due to the lack of prospective design and formal validation of the results. At subgroup analysis, the 4 studies not focused on oncological topic showed significantly lower quality scores than the others.

Conclusions: The overall methodological rigor of radiomics studies in the ovarian field is still not ideal, limiting the reproducibility of results and potential translation to clinical setting. More efforts towards a standardized methodology in the workflow are needed to allow radiomics to become a viable tool for clinical decision-making.

Key points: • The 63 included studies using noninvasive imaging for ovarian applications were mostly focused on oncologic topic (94%). • The included investigations achieved a median total RQS 6 (IQR, -0.5 to 11), indicating poor methodological rigor. • The RQS was low especially due to the lack of prospective design and formal validation of the results.

Keywords: Computed tomography; Machine learning; Magnetic resonance imaging; Ovary; Positron emission tomography.

MeSH terms

Humans
Magnetic Resonance Imaging* / methods
Positron-Emission Tomography
Reproducibility of Results
Tomography, X-Ray Computed* / methods
Ultrasonography