Contralateral asymmetry for breast cancer detection: A CADx approach

• Published in: Biocybernetics and biomedical engineering Vol. 38; no. 1; pp. 115 - 125
• Main Authors: Celaya-Padilla, Jose M., Guzmán-Valdivia, Cesar H., Galván-Tejada, Carlos E., Galván-Tejada, Jorge I., Gamboa-Rosales, Hamurabi, Garza-Veloz, Idalia, Martinez-Fierro, Margarita L., Cid-Báez, Miguel A., Martinez-Torteya, Antonio, Martinez-Ruiz, Francisco J., Luna-García, Huizilopoztli, Moreno-Baez, Arturo, Nandal, Amita
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 2018
• Subjects: Asymmetry; Brest cancer; CADx; Contralateral; Detection; Machine learning; Contralateral; Asymmetry; Detection; CADx; Machine learning; Brest cancer
• ISSN: 0208-5216
• DOI: 10.1016/j.bbe.2017.10.005

Early detection is fundamental for the effective treatment of breast cancer and the screening mammography is the most common tool used by the medical community to detect early breast cancer development. Screening mammograms include images of both breasts using two standard views, and the contralateral asymmetry per view is a key feature in detecting breast cancer. However, most automated detection algorithms do not take it into account. In this research, we propose a methodology to incorporate said asymmetry information into a computer-aided diagnosis system that can accurately discern between healthy subjects and subjects at risk of having breast cancer. Furthermore, we generate features that measure not only a view-wise asymmetry, but a subject-wise one. Briefly, the methodology co-registers the left and right mammograms, extracts image characteristics, fuses them into subject-wise features, and classifies subjects. In this study, 152 subjects from two independent databases, one with analog- and one with digital mammograms, were used to validate the methodology. Areas under the receiver operating characteristic curve of 0.738 and 0.767, and diagnostic odds ratios of 23.10 and 9.00 were achieved, respectively. In addition, the proposed method has the potential to rank subjects by their probability of having breast cancer, aiding in the re-scheduling of the radiologists’ image queue, an issue of utmost importance in developing countries.