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Dynamic contrast-enhanced and diffusion-weighted MRI of invasive breast cancer for the prediction of sentinel lymph node status

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dc.contributor.author육지현-
dc.date.accessioned2020-02-26T06:38:51Z-
dc.date.available2020-02-26T06:38:51Z-
dc.date.issued2020-
dc.identifier.issn1053-1807-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/175235-
dc.description.abstractBACKGROUND: Although sentinel lymph node biopsy (SLNB) is the current standard for identifying lymph metastasis in breast cancer patients, there are complications of SLNB. PURPOSE: To evaluate preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) of invasive breast cancer for predicting sentinel lymph node metastasis. STUDY TYPE: Retrospective. POPULATION: In all, 309 patients who underwent clinically node-negative invasive breast cancer surgery FIELD STRENGTH/SEQUENCE: 3.0T, DCE-MRI, DWI. ASSESSMENT: We collected clinicopathologic variables (age, histologic and nuclear grade, extensive intraductal carcinoma component, lymphovascular invasion, and immunohistochemical profiles) and preoperative MRI features (tumor size, background parenchymal enhancement, internal enhancement, adjacent vessel sign, whole-breast vascularity, initial enhancement pattern, kinetic curve types, quantitative kinetic parameters, tumoral apparent diffusion coefficient [ADC], peritumoral maximal ADC, and peritumoral-tumoral ADC ratio). STATISTICAL TESTS: Multivariate logistic regressions were performed to determine independent variables associated with SLN metastasis, and the area under the receiver operating characteristic curve (AUC) was analyzed for those variables. RESULTS: 41 (13.3%) of the patients showed SLN metastasis. With MRI, tumor size (odds ratio [OR], 1.11; 95% confidence interval [CI], 1.06-1.17), heterogeneous (OR, 5.33; 95% CI, 1.71-16.58), and rim (OR, 15.54; 95% CI, 2.12-113.72) enhancement and peritumoral-tumoral ADC ratio (OR, 72.79; 95% CI, 7.15-740.82) were independently associated with SLN metastasis. Clinicopathologic variables independently associated with SLN metastasis included age (OR, 0.96; 95% CI, 0.92-0.99) and CD31 (OR, 2.90; 95% CI, 1.04-8.92). The area under the curve (AUC) of MRI features (0.80; 95% CI, 0.73-0.87) was significantly higher than for clinicopathologic variables (0.68; 95% CI, 0.60-0.77; P = 0.048) and was barely below statistical significance for combined MRI features with clinicopathologic variables (0.84; 95% CI 0.78-0.90, P = 0.057). DATA CONCLUSION: Preoperative internal enhancement on DCE-MRI and peritumoral-tumoral ADC ratio on DWI might be useful for predicting SLN metastasis in patients with invasive breast cancer. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:615-626.-
dc.description.statementOfResponsibilityrestriction-
dc.languageEnglish-
dc.publisherWiley-Liss-
dc.relation.isPartOfJOURNAL OF MAGNETIC RESONANCE IMAGING-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleDynamic contrast-enhanced and diffusion-weighted MRI of invasive breast cancer for the prediction of sentinel lymph node status-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Radiology (영상의학교실)-
dc.contributor.googleauthorEun Jung Choi-
dc.contributor.googleauthorJi Hyun Youk-
dc.contributor.googleauthorHyemi Choi-
dc.contributor.googleauthorJi Soo Song-
dc.identifier.doi10.1002/jmri.26865-
dc.contributor.localIdA02537-
dc.relation.journalcodeJ01567-
dc.identifier.eissn1522-2586-
dc.identifier.pmid31313393-
dc.identifier.urlhttps://onlinelibrary.wiley.com/doi/full/10.1002/jmri.26865-
dc.subject.keywordbreast neoplasm-
dc.subject.keyworddiffusion magnetic resonance imaging-
dc.subject.keywordmagnetic resonance imaging-
dc.subject.keywordsentinel lymph node-
dc.contributor.alternativeNameYouk, Ji Hyun-
dc.contributor.affiliatedAuthor육지현-
dc.citation.volume51-
dc.citation.number2-
dc.citation.startPage615-
dc.citation.endPage626-
dc.identifier.bibliographicCitationJOURNAL OF MAGNETIC RESONANCE IMAGING, Vol.51(2) : 615-626, 2020-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers

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