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dc.contributor.authorVidic, Igor
dc.contributor.authorJerome, Neil Peter
dc.contributor.authorBathen, Tone Frost
dc.contributor.authorGoa, Pål Erik
dc.contributor.authorWhile, Peter Thomas
dc.date.accessioned2019-10-14T12:24:54Z
dc.date.available2019-10-14T12:24:54Z
dc.date.created2019-07-25T17:07:56Z
dc.date.issued2019
dc.identifier.issn1053-1807
dc.identifier.urihttp://hdl.handle.net/11250/2622000
dc.description.abstractBackground Diffusion‐weighted MRI (DWI) has potential to noninvasively characterize breast cancer lesions; models such as intravoxel incoherent motion (IVIM) provide pseudodiffusion parameters that reflect tissue perfusion, but are dependent on the details of acquisition and analysis strategy. Purpose To examine the effect of fitting algorithms, including conventional least‐squares (LSQ) and segmented (SEG) methods as well as Bayesian methods with global shrinkage (BSP) and local spatial (FBM) priors, on the power of IVIM parameters to differentiate benign and malignant breast lesions. Study Type Prospective patient study. Subjects 61 patients with confirmed breast lesions. Field Strength/Sequence DWI (bipolar SE‐EPI, 13 b values) was included in a clinical MR protocol including T2‐weighted and dynamic contrast‐enhanced MRI on a 3T scanner. Assessment The IVIM model was fitted voxelwise in lesion regions of interest (ROIs), and derived parameters were compared across methods within benign and malignant subgroups (correlation, coefficients of variation). Area under receiver operator characteristic curves (ROC AUCs) were calculated to determine discriminatory power of parameter combinations from all fitting methods. Statistical Tests Kruskal–Wallis, Mann–Whitney, Pearson correlation. Results All methods provided useful IVIM parameters; D was well‐correlated across all methods (r> 0.8), with a wider range for f and D* (0.3–0.7). Fitting methods gave detectable differences in parameters, but all showed increased f and decreased D in malign lesions. D was the most discriminatory single parameter, with LSQ performing least well (AUC 0.83). In general, ROC AUCs were maximized by the inclusion of pseudodiffusion parameters, and by the use of Bayesian methods incorporating prior information (maximum AUC of 0.92 for BSP). Data Conclusion DWI performs well at classifying breast lesions, but careful consideration of analysis procedure can improve performance. D is the most discriminatory single parameter, but including pseudodiffusion parameters (f and D*) increases ROC AUC. Bayesian methods outperformed conventional least‐squares and segmented fitting methods for breast lesion classification. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019.nb_NO
dc.language.isoengnb_NO
dc.publisherWileynb_NO
dc.titleAccuracy of breast cancer lesion classification using intravoxel incoherent motion diffusion-weighted imaging is improved by the inclusion of global or local prior knowledge with bayesian methodsnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionacceptedVersionnb_NO
dc.source.journalJournal of Magnetic Resonance Imagingnb_NO
dc.identifier.doi10.1002/jmri.26772
dc.identifier.cristin1712760
dc.description.localcodeLocked until 9.5.2020 due to copyright restrictions. This is the peer reviewed version of an article, which has been published in final form at [https://doi.org/10.1002/jmri.26772]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.nb_NO
cristin.unitcode194,66,20,0
cristin.unitcode194,65,25,0
cristin.unitcode1920,4,0,0
cristin.unitnameInstitutt for fysikk
cristin.unitnameInstitutt for sirkulasjon og bildediagnostikk
cristin.unitnameKlinikk for bildediagnostikk
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2


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