Probability of malignancy for lesions detected on breast MRI: a predictive model incorporating BI-RADS imaging features and patient characteristics.
Objectives: To predict the probability of malignancy for MRI-detected breast lesions with a multivariate model incorporating patient and lesion characteristics.Methods: Retrospective review of 2565 breast MR examinations from 1/03-11/06. BI-RADS 3, 4 and 5 lesions initially detected on MRI for new c...
| Publicado en: | European Radiology Vol. 21; no. 8; pp. 1609 - 1618 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Aug2011
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104572878&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104572878 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Aug2011 vid: 21 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104572878 62001652 NLM21359910 2011194063 10.1007/s00330-011-2094-6 NLM21359910 104572878 ppf: 1609 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Probability of malignancy for lesions detected on breast MRI: a predictive model incorporating BI-RADS imaging features and patient characteristics. aug: au: Demartini WB Kurland BF Gutierrez RL Blackmore CC Peacock S Lehman CD Demartini, Wendy B Kurland, Brenda F Gutierrez, Robert L Blackmore, C Craig Peacock, Sue Lehman, Constance D affil: Department of Radiology, University of Washington Medical Center, 1959 NE Pacific, Seattle, WA 98195, USA sug: subj: Breast Neoplasms Diagnosis Magnetic Resonance Imaging Methods Adult Aged Aged, 80 and Over Contrast Media Diagnostic Use Female Image Interpretation, Computer Assisted Logistic Regression Middle Age Predictive Value of Tests Probability ROC Curve Retrospective Design Funding Source Human Adult: 19-44 years Aged: 65+ years Aged, 80 & over Middle Aged: 45-64 years Female ab: Objectives: To predict the probability of malignancy for MRI-detected breast lesions with a multivariate model incorporating patient and lesion characteristics.Methods: Retrospective review of 2565 breast MR examinations from 1/03-11/06. BI-RADS 3, 4 and 5 lesions initially detected on MRI for new cancer or high-risk screening were included and outcomes determined by imaging, biopsy or tumor registry linkage. Variables were indication for MRI, age, lesion size, BI-RADS lesion type and kinetics. Associations with malignancy were assessed using generalized estimating equations and lesion probabilities of malignancy were calculated.Results: 855 lesions (155 malignant, 700 benign) were included. Strongest associations with malignancy were for kinetics (washout versus persistent; OR 4.2, 95% CI 2.5-7.1) and clinical indication (new cancer versus high-risk screening; OR 3.0, 95% CI 1.7-5.1). Also significant were age > = 50 years, size > = 10 mm and lesion-type mass. The most predictive model (AUC 0.70) incorporated indication, size and kinetics. The highest probability of malignancy (41.1%) was for lesions on MRI for new cancer, > = 10 mm with washout. The lowest (1.2%) was for lesions on high-risk screening, <10 mm with persistent kinetics.Conclusions: A multivariate model shows promise as a decision support tool in predicting malignancy for MRI-detected breast lesions. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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