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dc.contributor.authorRomeo, V
dc.contributor.authorClauser, P
dc.contributor.authorRasul, S
dc.contributor.authorKapetas, P
dc.contributor.authorGibbs, P
dc.contributor.authorBaltzer, PAT
dc.contributor.authorHacker, M
dc.contributor.authorWoitek, R
dc.contributor.authorHelbich, TH
dc.contributor.authorPinker, K
dc.date.accessioned2022-01-31T16:21:17Z
dc.date.available2022-01-31T16:21:17Z
dc.date.issued2022-01
dc.date.submitted2021-04-10
dc.identifier.issn1619-7070
dc.identifier.others00259-021-05492-z
dc.identifier.other5492
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/333473
dc.descriptionFunder: Horizon 2020 Framework Programme; doi: http://dx.doi.org/10.13039/100010661
dc.descriptionFunder: Medical University of Vienna
dc.description.abstractPURPOSE: To assess whether a radiomics and machine learning (ML) model combining quantitative parameters and radiomics features extracted from simultaneous multiparametric 18F-FDG PET/MRI can discriminate between benign and malignant breast lesions. METHODS: A population of 102 patients with 120 breast lesions (101 malignant and 19 benign) detected on ultrasound and/or mammography was prospectively enrolled. All patients underwent hybrid 18F-FDG PET/MRI for diagnostic purposes. Quantitative parameters were extracted from DCE (MTT, VD, PF), DW (mean ADC of breast lesions and contralateral breast parenchyma), PET (SUVmax, SUVmean, and SUVminimum of breast lesions, as well as SUVmean of the contralateral breast parenchyma), and T2-weighted images. Radiomics features were extracted from DCE, T2-weighted, ADC, and PET images. Different diagnostic models were developed using a fine Gaussian support vector machine algorithm which explored different combinations of quantitative parameters and radiomics features to obtain the highest accuracy in discriminating between benign and malignant breast lesions using fivefold cross-validation. The performance of the best radiomics and ML model was compared with that of expert reader review using McNemar's test. RESULTS: Eight radiomics models were developed. The integrated model combining MTT and ADC with radiomics features extracted from PET and ADC images obtained the highest accuracy for breast cancer diagnosis (AUC 0.983), although its accuracy was not significantly higher than that of expert reader review (AUC 0.868) (p = 0.508). CONCLUSION: A radiomics and ML model combining quantitative parameters and radiomics features extracted from simultaneous multiparametric 18F-FDG PET/MRI images can accurately discriminate between benign and malignant breast lesions.
dc.languageen
dc.publisherSpringer Science and Business Media LLC
dc.subjectOriginal Article
dc.subjectAdvanced Image Analyses (Radiomics and Artificial Intelligence)
dc.subject18F-FDG PET/MRI
dc.subjectBreast cancer
dc.subjectArtificial intelligence
dc.subjectRadiomics
dc.titleAI-enhanced simultaneous multiparametric 18F-FDG PET/MRI for accurate breast cancer diagnosis.
dc.typeArticle
dc.date.updated2022-01-31T16:21:16Z
prism.endingPage608
prism.issueIdentifier2
prism.publicationNameEur J Nucl Med Mol Imaging
prism.startingPage596
prism.volume49
dc.identifier.doi10.17863/CAM.80894
dcterms.dateAccepted2021-07-06
rioxxterms.versionofrecord10.1007/s00259-021-05492-z
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidWoitek, Ramona [0000-0002-9146-9159]
dc.identifier.eissn1619-7089
cam.issuedOnline2021-08-10


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