Predicting Malignancy from Mammography Findings and Surgical Biopsies

• Type: Article in International Conference Proceedings Book
• Date: 2011

Authors

Ferreira, P (Author) Other; Fonseca, NA (Author) FCUP; Dutra, I (Author) FCUP; Woods, R (Author) Other; Burnside, E (Author) Other

Conference proceedings International

Title: Proceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011

Pages: 339-344

2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011

Atlanta, GA, 12 November 2011 through 15 November 2011

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 2 Citations

Other information

Authenticus ID: P-008-21K

DOI: 10.1109/bibm.2011.71

Abstract (EN): Breast screening is the regular examination of a woman's breasts to find breast cancer earlier. The sole exam approved for this purpose is mammography. Usually, findings are annotated through the Breast Imaging Reporting and Data System (BIRADS) created by the American College of Radiology. The BIRADS system determines a standard lexicon to be used by radiologists when studying each finding. Although the lexicon is standard, the annotation accuracy of the findings depends on the experience of the radiologist. Moreover, the accuracy of the classification of a mammography is also highly dependent on the expertise of the radiologist. A correct classification is paramount due to economical and humanitarian reasons. The main goal of this work is to produce machine learning models that predict the outcome of a mammography from a reduced set of annotated mammography findings. In the study we used a data set consisting of 348 consecutive breast masses that underwent image guided or surgical biopsy performed between October 2005 and December 2007 on 328 female subjects. The main conclusions are threefold: (1) automatic classification of a mammography, independent on information about mass density, can reach equal or better results than the classification performed by a physician; (2) mass density seems to be a good indicator of malignancy, as previous studies suggested; (3) a machine learning model can predict mass density with a quality as good as the specialist blind to biopsy, which is one of our main contributions. Our model can predict malignancy in the absence of the mass density attribute, since we can fill up this attribute using our mass density predictor.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6