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Open Access 01-12-2023 | Computed Tomography | Original Article

Radiomics in the evaluation of ovarian masses — a systematic review

Authors: Pratik Adusumilli, Nishant Ravikumar, Geoff Hall, Sarah Swift, Nicolas Orsi, Andrew Scarsbrook

Published in: Insights into Imaging | Issue 1/2023

Abstract

Objectives

The study aim was to conduct a systematic review of the literature reporting the application of radiomics to imaging techniques in patients with ovarian lesions.

Methods

MEDLINE/PubMed, Web of Science, Scopus, EMBASE, Ovid and ClinicalTrials.gov were searched for relevant articles. Using PRISMA criteria, data were extracted from short-listed studies. Validity and bias were assessed independently by 2 researchers in consensus using the Quality in Prognosis Studies (QUIPS) tool. Radiomic Quality Score (RQS) was utilised to assess radiomic methodology.

Results

After duplicate removal, 63 articles were identified, of which 33 were eligible. Fifteen assessed lesion classifications, 10 treatment outcomes, 5 outcome predictions, 2 metastatic disease predictions and 1 classification/outcome prediction. The sample size ranged from 28 to 501 patients. Twelve studies investigated CT, 11 MRI, 4 ultrasound and 1 FDG PET-CT. Twenty-three studies (70%) incorporated 3D segmentation. Various modelling methods were used, most commonly LASSO (least absolute shrinkage and selection operator) (10/33). Five studies (15%) compared radiomic models to radiologist interpretation, all demonstrating superior performance. Only 6 studies (18%) included external validation. Five studies (15%) had a low overall risk of bias, 9 (27%) moderate, and 19 (58%) high risk of bias. The highest RQS achieved was 61.1%, and the lowest was − 16.7%.

Conclusion

Radiomics has the potential as a clinical diagnostic tool in patients with ovarian masses and may allow better lesion stratification, guiding more personalised patient care in the future. Standardisation of the feature extraction methodology, larger and more diverse patient cohorts and real-world evaluation is required before clinical translation.

Clinical relevance statement

Radiomics shows promising results in improving lesion stratification, treatment selection and outcome prediction. Modelling with larger cohorts and real-world evaluation is required before clinical translation.

Key points

• Radiomics is emerging as a tool for enhancing clinical decisions in patients with ovarian masses.

• Radiomics shows promising results in improving lesion stratification, treatment selection and outcome prediction.

• Modelling with larger cohorts and real-world evaluation is required before clinical translation.

Graphical Abstract

Available only for authorised users

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Title: Radiomics in the evaluation of ovarian masses — a systematic review
Authors: Pratik Adusumilli
Nishant Ravikumar
Geoff Hall
Sarah Swift
Nicolas Orsi
Andrew Scarsbrook
Publication date: 01-12-2023
Publisher: Springer Vienna
Keywords: Computed Tomography
Computed Tomography
Ovarian Cancer
Ovarian Cancer
Magnetic Resonance Imaging
Magnetic Resonance Imaging
Ultrasound
Ultrasound
Published in: Insights into Imaging / Issue 1/2023
Electronic ISSN: 1869-4101
DOI: https://doi.org/10.1186/s13244-023-01500-y

At a glance: The STEP trials

Springer Medicine

Radiomics in the evaluation of ovarian masses — a systematic review

Abstract

Objectives

Methods

Results

Conclusion

Clinical relevance statement

Key points

Graphical Abstract

At a glance: The STEP trials

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusion

Clinical relevance statement

Key points

Graphical Abstract

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