Literature DB >> 17114620

Computer-aided detection in digital mammography: comparison of craniocaudal, mediolateral oblique, and mediolateral views.

Seung Ja Kim1, Woo Kyung Moon, Nariya Cho, Joo Hee Cha, Sun Mi Kim, Jung-Gi Im.   

Abstract

PURPOSE: To retrospectively compare the sensitivity of a computer-aided detection (CAD) system for depicting breast cancer in three digital mammographic views.
MATERIALS AND METHODS: This study was conducted with institutional review board approval; informed consent was waived. A commercially available CAD system was applied to the craniocaudal, mediolateral oblique, and mediolateral digital mammographic views of 83 women (mean age, 48 years; range, 30-66 years) with 83 histologically proved breast cancers. Findings were 59 masses and 41 microcalcifications (17 lesions showed both findings; 42 lesions, mass only; and 24 lesions, microcalcification only). The paired t test was used to analyze sensitivity of the CAD system for the detection of cancer in these three mammographic views and in combinations of the views.
RESULTS: The sensitivities of the CAD system were 92% (76 of 83) in the craniocaudal view, 83% (69 of 83) in the mediolateral oblique view, and 86% (71 of 83) in the mediolateral view; the differences were not significant (P = .07-.62). Sensitivity increased to 96% (80 of 83) in the craniocaudal plus mediolateral oblique views and to 99% (82 of 83) in the craniocaudal plus mediolateral oblique plus mediolateral views. For masses, the sensitivity of the CAD system was 76% (45 of 59) in the craniocaudal view and 75% (44 of 59) in the mediolateral oblique view and increased to 93% (55 of 59) when mediolateral oblique and craniocaudal views were combined (P < .001). For microcalcifications, sensitivity was 98% (40 of 41) in the craniocaudal view and 95% (39 of 41) in the mediolateral oblique view, and this increased to 100% (41 of 41) when the mediolateral oblique and craniocaudal views were combined (P = .31).
CONCLUSION: The sensitivities of the CAD system were not significantly different among these three digital mammographic views. Sensitivity for depicting masses was significantly increased (P < .001) when the craniocaudal view was added to the mediolateral oblique view. (c) RSNA, 2006.

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Year:  2006        PMID: 17114620     DOI: 10.1148/radiol.2413051145

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


  6 in total

1.  Evaluation of breast amorphous calcifications by a computer-aided detection system in full-field digital mammography.

Authors:  A M Scaranelo; R Eiada; K Bukhanov; P Crystal
Journal:  Br J Radiol       Date:  2012-05       Impact factor: 3.039

2.  Genetic variation in the estrogen metabolic pathway and mammographic density as an intermediate phenotype of breast cancer.

Authors:  Jingmei Li; Louise Eriksson; Keith Humphreys; Kamila Czene; Jianjun Liu; Rulla M Tamimi; Sara Lindström; David J Hunter; Celine M Vachon; Fergus J Couch; Christopher G Scott; Pagona Lagiou; Per Hall
Journal:  Breast Cancer Res       Date:  2010-03-09       Impact factor: 6.466

3.  Detection of breast cancer with a computer-aided detection applied to full-field digital mammography.

Authors:  Ryusuke Murakami; Shinichiro Kumita; Hitomi Tani; Tamiko Yoshida; Kenichi Sugizaki; Tomoyuki Kuwako; Tomonari Kiriyama; Kenta Hakozaki; Emi Okazaki; Keiko Yanagihara; Shinya Iida; Shunsuke Haga; Shinichi Tsuchiya
Journal:  J Digit Imaging       Date:  2013-08       Impact factor: 4.056

4.  Computer-aided detection of breast carcinoma in standard mammographic projections with digital mammography.

Authors:  Stamatia Destounis; Sarah Hanson; Renee Morgan; Philip Murphy; Patricia Somerville; Posy Seifert; Valerie Andolina; Andrea Arieno; Melissa Skolny; Wende Logan-Young
Journal:  Int J Comput Assist Radiol Surg       Date:  2009-04-15       Impact factor: 2.924

5.  A curated mammography data set for use in computer-aided detection and diagnosis research.

Authors:  Rebecca Sawyer Lee; Francisco Gimenez; Assaf Hoogi; Kanae Kawai Miyake; Mia Gorovoy; Daniel L Rubin
Journal:  Sci Data       Date:  2017-12-19       Impact factor: 6.444

6.  Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening: Preliminary Study.

Authors:  Eun-Kyung Kim; Hyo-Eun Kim; Kyunghwa Han; Bong Joo Kang; Yu-Mee Sohn; Ok Hee Woo; Chan Wha Lee
Journal:  Sci Rep       Date:  2018-02-09       Impact factor: 4.379

  6 in total

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