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dc.contributor.authorSavaridas, Sarah
dc.contributor.authorAgrawal, Utkarsh
dc.contributor.authorFagbamigbe, Adeniyi
dc.contributor.authorTennant, Sarah L
dc.contributor.authorMcCowan, Colin
dc.date.accessioned2023-05-15T10:30:03Z
dc.date.available2023-05-15T10:30:03Z
dc.date.issued2023-04-01
dc.identifier283587823
dc.identifieraf5f1301-e049-44ee-9c9c-1d6c63d74e68
dc.identifier85153544557
dc.identifier.citationSavaridas , S , Agrawal , U , Fagbamigbe , A , Tennant , S L & McCowan , C 2023 , ' Radiomic analysis in contrast-enhanced mammography using a multi-vendor data set : accuracy of models according to segmentation techniques ' , The British Journal of Radiology , vol. 96 , no. 1145 , 20220980 . https://doi.org/10.1259/bjr.20220980en
dc.identifier.issn0007-1285
dc.identifier.otherRIS: urn:131452B2F982197A6984271AD9A1B1B0
dc.identifier.otherORCID: /0000-0002-9466-833X/work/130203721
dc.identifier.urihttps://hdl.handle.net/10023/27613
dc.descriptionFunding: British Society of Breast Radiology grant, TENOVUS Scotland.en
dc.description.abstractObjective Radiomic analysis of contrast enhanced mammographic (CEM) images is an emerging field. The aims of this study were to build classification models to distinguish benign and malignant lesions using a multi vendor dataset and compare segmentation techniques. Methods CEM images were acquired using Hologic and GE equipment. Textural features were extracted using MaZda analysis software. Lesions were segmented with freehand region of interest (ROI) and ellipsoid_ROI. Benign/Malignant classification models were built using extracted textural features. Subset analysis according to ROI and mammographic view was performed. Results 269 enhancing mass lesions (238 patients) were included. Oversampling mitigated benign/malignant imbalance. Diagnostic accuracy of all models was high (>0.9). Segmentation with ellipsoid_ROI produced a more accurate model than with FH_ROI, accuracy:0.947 vs 0.914, AUC:0.974 vs 0.86, p < 0.05. Regarding mammographic view all models were highly accurate (0.947–0.955) with no difference in AUC (0.985–0.987). The CC-view model had the greatest specificity:0.962, the MLO-view and CC + MLO view models had higher sensitivity:0.954, p < 0.05. Conclusions Accurate radiomics models can be built using a real-life multivendor dataset segmentation with ellipsoid-ROI produces the highest level of accuracy. The marginal increase in accuracy using both mammographic views, may not justify the increased workload. Advances in knowledge Radiomic modelling can be successfully applied to a multivendor CEM dataset, ellipsoid_ROI is an accurate segmentation technique and it may be unnecessary to segment both CEM views. These results will help further developments aimed at producing a widely accessible radiomics model for clinical use.
dc.format.extent8
dc.format.extent597105
dc.language.isoeng
dc.relation.ispartofThe British Journal of Radiologyen
dc.subjectContrast-enhanced mammographyen
dc.subjectMammographyen
dc.subjectRadiomicsen
dc.subjectBreast canceren
dc.subjectRC0254 Neoplasms. Tumors. Oncology (including Cancer)en
dc.subjectE-NDASen
dc.subjectSDG 3 - Good Health and Well-beingen
dc.subjectMCCen
dc.subject.lccRC0254en
dc.titleRadiomic analysis in contrast-enhanced mammography using a multi-vendor data set : accuracy of models according to segmentation techniquesen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews. School of Medicineen
dc.contributor.institutionUniversity of St Andrews. Population and Behavioural Science Divisionen
dc.contributor.institutionUniversity of St Andrews. Sir James Mackenzie Institute for Early Diagnosisen
dc.identifier.doi10.1259/bjr.20220980
dc.description.statusPeer revieweden


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