Literature DB >> 15890483

Use of prior mammograms in the classification of benign and malignant masses.

Celia Varela1, Nico Karssemeijer, Jan H C L Hendriks, Roland Holland.   

Abstract

The purpose of this study was to determine the importance of using prior mammograms for classification of benign and malignant masses. Five radiologists and one resident classified mass lesions in 198 mammograms obtained from a population-based screening program. Cases were interpreted twice, once without and once with comparison of previous mammograms, in a sequential reading order using soft copy image display. The radiologists' performances in classifying benign and malignant masses without and with previous mammograms were evaluated with receiver operating characteristic (ROC) analysis. The statistical significance of the difference in performances was calculated using analysis of variance. The use of prior mammograms improved the classification performance of all participants in the study. The mean area under the ROC curve of the readers increased from 0.763 to 0.796. This difference in performance was statistically significant (P = 0.008).

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Year:  2005        PMID: 15890483     DOI: 10.1016/j.ejrad.2005.04.007

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  10 in total

1.  Measures of angular spread and entropy for the detection of architectural distortion in prior mammograms.

Authors:  Shantanu Banik; Rangaraj M Rangayyan; J E Leo Desautels
Journal:  Int J Comput Assist Radiol Surg       Date:  2012-03-30       Impact factor: 2.924

2.  Should previous mammograms be digitised in the transition to digital mammography?

Authors:  S Taylor-Phillips; M G Wallis; A G Gale
Journal:  Eur Radiol       Date:  2009-03-18       Impact factor: 5.315

3.  Effect of the Availability of Prior Full-Field Digital Mammography and Digital Breast Tomosynthesis Images on the Interpretation of Mammograms.

Authors:  Christiane M Hakim; Victor J Catullo; Denise M Chough; Marie A Ganott; Amy E Kelly; Dilip D Shinde; Jules H Sumkin; Luisa P Wallace; Andriy I Bandos; David Gur
Journal:  Radiology       Date:  2015-03-13       Impact factor: 11.105

4.  Classifying symmetrical differences and temporal change for the detection of malignant masses in mammography using deep neural networks.

Authors:  Thijs Kooi; Nico Karssemeijer
Journal:  J Med Imaging (Bellingham)       Date:  2017-10-10

5.  Impact of and interaction between the availability of prior examinations and DBT on the interpretation of negative and benign mammograms.

Authors:  Christiane M Hakim; Marie I Anello; Cathy S Cohen; Marie A Ganott; Amy H Lu; Ronald L Perrin; Ratan Shah; Marion Lee Spangler; Andriy I Bandos; David Gur
Journal:  Acad Radiol       Date:  2013-12-05       Impact factor: 3.173

6.  Computer-aided detection of architectural distortion in prior mammograms of interval cancer.

Authors:  Rangaraj M Rangayyan; Shantanu Banik; J E Leo Desautels
Journal:  J Digit Imaging       Date:  2010-02-02       Impact factor: 4.056

7.  Digital subtraction of temporally sequential mammograms for improved detection and classification of microcalcifications.

Authors:  Kosmia Loizidou; Galateia Skouroumouni; Costas Pitris; Christos Nikolaou
Journal:  Eur Radiol Exp       Date:  2021-09-14

8.  Radiomics Based on Digital Mammography Helps to Identify Mammographic Masses Suspicious for Cancer.

Authors:  Guangsong Wang; Dafa Shi; Qiu Guo; Haoran Zhang; Siyuan Wang; Ke Ren
Journal:  Front Oncol       Date:  2022-04-01       Impact factor: 5.738

9.  Cost-Effectiveness of Double Reading versus Single Reading of Mammograms in a Breast Cancer Screening Programme.

Authors:  Margarita Posso; Misericòrdia Carles; Montserrat Rué; Teresa Puig; Xavier Bonfill
Journal:  PLoS One       Date:  2016-07-26       Impact factor: 3.240

10.  Change in Image Quality According to the 3D Locations of a CBCT Phantom.

Authors:  Jae Joon Hwang; Hyok Park; Ho-Gul Jeong; Sang-Sun Han
Journal:  PLoS One       Date:  2016-04-19       Impact factor: 3.240

  10 in total

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