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Detection of Breast Cancer with a Computer-aided Detection Applied to Full-field Digital Mammography

Abstract

A study was conducted to evaluate the sensitivity of computer-aided detection (CAD) with full-field digital mammography in detection of breast cancer, based on mammographic appearance and histopathology. Retrospectively, CAD sensitivity was assessed in total group of 152 cases for subgroups based on breast density, mammographic presentation, lesion size, and results of histopathological examination. The overall sensitivity of CAD was 91 % (139 of 152 cases). CAD detected 100 % (47/47) of cancers manifested as microcalcifications; 98 % (62/63) of those manifested as non-calcified masses; 100 % (15/15) of those manifested as mixed masses and microcalcifications; 75 % (12/16) of those manifested as architectural distortions, and 69 % (18/26) of those manifested as focal asymmetry. CAD sensitivity was 83 % (10/12) for cancers measuring 1-10 mm, 92 % (37/40) for those measuring 11-20 mm, and 92 % (92/100) for those measuring >20 mm. There was no significant difference in CAD detection efficiency between cancers in dense breasts (88 %; 69/78) and those in non-dense breasts (95 %; 70/74). CAD showed a high sensitivity of 91 % (139/152) for the mammographic appearance of cancer and 100 % sensitivity for identifying cancers manifested as microcalcifications. Sensitivity was not influenced by breast density or lesion size. CAD should be effective for helping radiologists detect breast cancer at an earlier stage.

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References
1.
Warren Burhenne L, Wood S, DOrsi C, Feig S, Kopans D, OShaughnessy K . Potential contribution of computer-aided detection to the sensitivity of screening mammography. Radiology. 2000; 215(2):554-62. DOI: 10.1148/radiology.215.2.r00ma15554. View

2.
Bolivar A, Sanchez Gomez S, Merino P, Alonso-Bartolome P, Garcia E, Munoz Cacho P . Computer-aided detection system applied to full-field digital mammograms. Acta Radiol. 2010; 51(10):1086-92. DOI: 10.3109/02841851.2010.520024. View

3.
Sickles E . Mammographic features of 300 consecutive nonpalpable breast cancers. AJR Am J Roentgenol. 1986; 146(4):661-3. DOI: 10.2214/ajr.146.4.661. View

4.
Evans W, Warren Burhenne L, Laurie L, OShaughnessy K, Castellino R . Invasive lobular carcinoma of the breast: mammographic characteristics and computer-aided detection. Radiology. 2002; 225(1):182-9. DOI: 10.1148/radiol.2251011029. View

5.
Brem R, Rapelyea J, Zisman G, Hoffmeister J, Desimio M . Evaluation of breast cancer with a computer-aided detection system by mammographic appearance and histopathology. Cancer. 2005; 104(5):931-5. DOI: 10.1002/cncr.21255. View