Literature DB >> 21177388

The mammographic density of a mass is a significant predictor of breast cancer.

Ryan W Woods1, Gale S Sisney, Lonie R Salkowski, Kazuhiko Shinki, Yunzhi Lin, Elizabeth S Burnside.   

Abstract

PURPOSE: To determine whether the mammographic density of noncalcified solid breast masses is associated with malignancy and to measure the agreement between prospective and retrospective assessment.
MATERIALS AND METHODS: The institutional review board approved this study and waived informed consent. Three hundred forty-eight consecutive breast masses in 328 women who underwent image-guided or surgical biopsy between October 2005 and December 2007 were included. All 348 biopsy-proved masses were randomized and assigned to a radiologist who was blinded to biopsy results for retrospective assessment by using the Breast Imaging Reporting and Data System (retrospectively assessed data set). Clinical radiologists prospectively assessed the density of 180 of these masses (prospectively assessed data set). Pathologic result at biopsy was the reference standard. Benign masses were followed for at least 1 year by linking each patient to a cancer registry. Univariate analyses were performed on the retrospectively assessed data set. The association of mass density and malignancy was examined by creating a logistic model for the prospectively assessed data set. Agreement between prospective and retrospective assessments was calculated by using the κ statistic.
RESULTS: In the retrospectively assessed data set, 70.2% of high-density masses were malignant, and 22.3% of the isodense or low-density masses were malignant (P < .0001). In the prospective logistic model, high density (odds ratio, 6.6), irregular shape (odds ratio, 9.9), spiculated margin (odds ratio, 20.3), and age (β = 0.09, P < .0001) were significantly associated with the probability of malignancy. The κ value for prospective-retrospective agreement of mass density was 0.53.
CONCLUSION: High mass density is significantly associated with malignancy in both retrospectively and prospectively assessed data sets, with moderate prospective-retrospective agreement. Radiologists should consider mass density as a valuable descriptor that can stratify risk. SUPPLEMENTAL MATERIAL: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.10100328/-/DC1. © RSNA, 2010

Entities:  

Mesh:

Year:  2010        PMID: 21177388      PMCID: PMC3029888          DOI: 10.1148/radiol.10100328

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  23 in total

1.  Breast Imaging Reporting and Data System: inter- and intraobserver variability in feature analysis and final assessment.

Authors:  W A Berg; C Campassi; P Langenberg; M J Sexton
Journal:  AJR Am J Roentgenol       Date:  2000-06       Impact factor: 3.959

2.  The positive predictive value of the breast imaging reporting and data system (BI-RADS) as a method of quality assessment in breast imaging in a hospital population.

Authors:  Harmine M Zonderland; Thomas L Pope; Arend J Nieborg
Journal:  Eur Radiol       Date:  2004-07-09       Impact factor: 5.315

3.  Diagnostic importance of the radiographic density of noncalcified breast masses: analysis of 91 lesions.

Authors:  V P Jackson; K A Dines; L W Bassett; R H Gold; H E Reynolds
Journal:  AJR Am J Roentgenol       Date:  1991-07       Impact factor: 3.959

4.  Concordance of breast imaging reporting and data system assessments and management recommendations in screening mammography.

Authors:  Stephen H Taplin; Laura E Ichikawa; Karla Kerlikowske; Virginia L Ernster; Robert D Rosenberg; Bonnie C Yankaskas; Patricia A Carney; Berta M Geller; Nicole Urban; Mark B Dignan; William E Barlow; Rachel Ballard-Barbash; Edward A Sickles
Journal:  Radiology       Date:  2002-02       Impact factor: 11.105

5.  Nonpalpable lesions detected with mammography: review of 512 consecutive cases.

Authors:  S Ciatto; L Cataliotti; V Distante
Journal:  Radiology       Date:  1987-10       Impact factor: 11.105

6.  The measurement of observer agreement for categorical data.

Authors:  J R Landis; G G Koch
Journal:  Biometrics       Date:  1977-03       Impact factor: 2.571

7.  Lesion and patient characteristics associated with malignancy after a probably benign finding on community practice mammography.

Authors:  Constance D Lehman; Carolyn M Rutter; Peter R Eby; Emily White; Diana S M Buist; Stephen H Taplin
Journal:  AJR Am J Roentgenol       Date:  2008-02       Impact factor: 3.959

8.  Nonpalpable breast lesions: recommendations for biopsy based on suspicion of carcinoma at mammography.

Authors:  F M Hall; J M Storella; D Z Silverstone; G Wyshak
Journal:  Radiology       Date:  1988-05       Impact factor: 11.105

9.  Mammographic follow-up of low-suspicion lesions: compliance rate and diagnostic yield.

Authors:  M A Helvie; D R Pennes; M Rebner; D D Adler
Journal:  Radiology       Date:  1991-01       Impact factor: 11.105

10.  Revisiting the mammographic follow-up of BI-RADS category 3 lesions.

Authors:  Ximena Varas; José H Leborgne; Francisco Leborgne; Julieta Mezzera; Sylvia Jaumandreu; Felix Leborgne
Journal:  AJR Am J Roentgenol       Date:  2002-09       Impact factor: 3.959

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Authors:  Yirong Wu; Oguzhan Alagoz; Mehmet U S Ayvaci; Alejandro Munoz Del Rio; David J Vanness; Ryan Woods; Elizabeth S Burnside
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4.  Breast tissue decomposition with spectral distortion correction: a postmortem study.

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6.  Dramatic increase in volume versus length of invasive ductal carcinoma mimicking intramammary lymph node in a small nodular lesion.

Authors:  Seda Aladag Kurt; Varol Celik
Journal:  Bull Natl Res Cent       Date:  2022-05-12

7.  Breast tissue characterization with photon-counting spectral CT imaging: a postmortem breast study.

Authors:  Huanjun Ding; Michael J Klopfer; Justin L Ducote; Fumitaro Masaki; Sabee Molloi
Journal:  Radiology       Date:  2014-05-07       Impact factor: 11.105

8.  Predicting Malignancy from Mammography Findings and Surgical Biopsies.

Authors:  Pedro Ferreira; Nuno A Fonseca; Inês Dutra; Ryan Woods; Elizabeth Burnside
Journal:  Proceedings (IEEE Int Conf Bioinformatics Biomed)       Date:  2011-11

9.  Predicting malignancy from mammography findings and image-guided core biopsies.

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Journal:  Int J Data Min Bioinform       Date:  2015       Impact factor: 0.667

10.  Patchless Multi-Stage Transfer Learning for Improved Mammographic Breast Mass Classification.

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  10 in total

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