Studying the relevance of breast imaging features

Pedro Ferreira, Inés Dutra, Nuno A. Fonseca, Ryan Woods, Elizabeth Burnside

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Breast screening is the regular examination of a woman's breasts to find breast cancer in an initial stage. The sole exam approved for this purpose is mammography that, despite the existence of more advanced technologies, is considered the cheapest and most efficient method to detect cancer in a preclinical stage. We investigate, using machine learning techniques, how attributes obtained from mammographies can relate to malignancy. In particular, this study focus is on how mass density can influence malignancy from a data set of 348 patients containing, among other information, results of biopsies. To this end, we applied different learning algorithms on the data set using the WEKA tools, and performed significance tests on the results. The conclusions are threefold: (1) automatic classification of a mammography can reach equal or better results than the ones annotated by specialists, which can help doctors to quickly concentrate on some specific mammogram for a more thorough study; (2) mass density seems to be a good indicator of malignancy, as previous studies suggested; (3) we can obtain classifiers that can predict mass density with a quality as good as the specialist blind to biopsy.

Original languageEnglish (US)
Title of host publicationHEALTHINF 2011 - Proceedings of the International Conference on Health Informatics
Pages337-342
Number of pages6
StatePublished - Jul 15 2011
EventInternational Conference on Health Informatics, HEALTHINF 2011 - Rome, Italy
Duration: Jan 26 2011Jan 29 2011

Publication series

NameHEALTHINF 2011 - Proceedings of the International Conference on Health Informatics

Other

OtherInternational Conference on Health Informatics, HEALTHINF 2011
CountryItaly
CityRome
Period1/26/111/29/11

Keywords

  • Breast cancer
  • Classification methods
  • Data mining
  • Machine learning
  • Mammograms
  • Mass density

ASJC Scopus subject areas

  • Health Informatics
  • Health Information Management

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