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Multiparametric MRI model with dynamic contrast-enhanced and diffusion-weighted imaging enables breast cancer diagnosis with high accuracy
Verfasser / VerfasserinHelbich, Thomas H. ; Zhang, Michelle ; Horvat, Joao V. ; Bernard-Davila, Blanca ; Marino, Maria Adele ; Leithner, Doris ; Ochoa-Albiztegui, R. Elena ; Morris, Elizabeth A. ; Thakur, Sunitha ; Pinker, Katja
Erschienen in
Journal of Magnetic Resonance Imaging, 2019, Jg. 49, H. 3, S. 864-874
ErschienenWiley-Blackwell, 2019
SpracheEnglisch
DokumenttypAufsatz in einer Zeitschrift
Schlagwörter (EN)breast cancer / dynamic contrastenhanced MRI / diffusionweighted imaging / T2weighted imaging / BIRADS
URNurn:nbn:at:at-ubmuw:3-872 Persistent Identifier (URN)
DOI10.1002/jmri.26285 
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 Das Werk ist frei verfügbar
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Multiparametric MRI model with dynamic contrast-enhanced and diffusion-weighted imaging enables breast cancer diagnosis with high accuracy [3.76 mb]
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Zusammenfassung (Englisch)

Background

The MRI Breast ImagingReporting and Data System (BIRADS) lexicon recommends that a breast MRI protocol contain T2weighted and dynamic contrastenhanced (DCE) MRI sequences. The addition of diffusionweighted imaging (DWI) significantly improves diagnostic accuracy. This study aims to clarify which descriptors from DCEMRI, DWI, and T2weighted imaging are most strongly associated with a breast cancer diagnosis.

Purpose/Hypothesis

To develop a multiparametric MRI (mpMRI) model for breast cancer diagnosis incorporating American College of Radiology (ACR) BIRADS recommended descriptors for breast MRI with DCE, T2weighted imaging, and DWI with apparent diffusion coefficient (ADC) mapping.

Study Type

Retrospective.

Subjects

In all, 188 patients (mean 51.6 years) with 210 breast tumors (136 malignant and 74 benign) who underwent mpMRI from December 2010 to September 2014.

Field Strength/Sequence

IR inversion recovert DCEMRI dynamic contrastenhanced magnetic resonance imaging VIBE VolumeInterpolatedBreathholdExamination FLASH turbo fastlowangleshot TWIST Timeresolved angiography with stochastic Trajectories.

Assessment

Two radiologists in consensus and another radiologist independently evaluated the mpMRI data. Characteristics for mass (n = 182) and nonmass (n = 28) lesions were recorded on DCE and T2weighted imaging according to BIRADS, as well as DWI descriptors. Two separate models were analyzed, using DCEMRI BIRADS descriptors, T2weighted imagines, and ADCmean as either a continuous or binary form using a previously published ADC cutoff value of 1.25 103 mm2/sec for differentiation between benign and malignant lesions. Histopathology was the standard of reference.

Statistical Tests

2 test, Fisher's exact test, KruskalWallis test, Pearson correlation coefficient, multivariate logistic regression analysis, HosmerLemeshow test of goodnessoffit, receiver operating characteristics analysis.

Results

In Model 1, ADCmean (P = 0.0031), mass margins with DCE (P = 0.0016), and delayed enhancement with DCE (P = 0.0016) were significantly and independently associated with breast cancer diagnosis; Model 2 identified ADCmean (P = 0.0031), mass margins with DCE (P = 0.0012), initial enhancement (P = 0.0422), and delayed enhancement with DCE (P = 0.0065) to be significantly independently associated with breast cancer diagnosis. T2weighted imaging variables were not included in the final models.

Data Conclusion

mpMRI with DCEMRI and DWI with ADC mapping enables accurate breast cancer diagnosis. A model using quantitative and qualitative descriptors from DCEMRI and DWI identifies breast cancer with a high diagnostic accuracy. T2weighted imaging does not significantly contribute to breast cancer diagnosis.

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