Literature DB >> 1646563

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

V P Jackson1, K A Dines, L W Bassett, R H Gold, H E Reynolds.   

Abstract

Radiographic density is considered an important feature in the evaluation of noncalcified breast masses, yet no studies assessing its value have been published. The radiographic densities of 91 biopsy-proved, nonfatty, noncalcified breast masses were evaluated by three mammographers. The density determinations made by each observer were compared with the histologic outcome for the 51 benign and 40 malignant lesions. With the kappa statistic, interobserver agreement was relatively poor (0.22 to 0.49), and intraobserver agreement for one expert mammographer was 0.50. When the majority opinion of the mammographers was used, sensitivity was 48%, specificity was 80%, and both positive and negative predictive values were 66%. As a solitary feature in lesion analysis, mammographic density is difficult to assess and is of limited value for the prediction of the benign or malignant nature of noncalcified breast masses.

Mesh:

Year:  1991        PMID: 1646563     DOI: 10.2214/ajr.157.1.1646563

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  4 in total

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

Authors:  Ryan W Woods; Gale S Sisney; Lonie R Salkowski; Kazuhiko Shinki; Yunzhi Lin; Elizabeth S Burnside
Journal:  Radiology       Date:  2010-12-21       Impact factor: 11.105

2.  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

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

Authors:  Pedro Ferreira; Nuno A Fonseca; Inês Dutra; Ryan Woods; Elizabeth Burnside
Journal:  Int J Data Min Bioinform       Date:  2015       Impact factor: 0.667

4.  Validation of results from knowledge discovery: mass density as a predictor of breast cancer.

Authors:  Ryan W Woods; Louis Oliphant; Kazuhiko Shinki; David Page; Jude Shavlik; Elizabeth Burnside
Journal:  J Digit Imaging       Date:  2009-09-16       Impact factor: 4.056

  4 in total

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