Predicting malignancy from mammography findings and image-guided core biopsies.
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 dataset consisting of 348 consecutive breast masses that underwent image guided core biopsy performed between October 2005 and December 2007 on 328 female subjects. We applied various algorithms with parameter variation to learn from the data. The tasks were to predict mass density and to predict malignancy. The best classifier that predicts mass density is based on a support vector machine and has accuracy of 81.3%. The expert correctly annotated 70% of the mass densities. The best classifier that predicts malignancy is also based on a support vector machine and has accuracy of 85.6%, with a positive predictive value of 85%. One important contribution of this work is that 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.
Pedro
P
Ferreira
Nuno A
NA
Fonseca
Inês
I
Dutra
Ryan
R
Woods
Elizabeth
E
Burnside
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001706:Biopsy / Q000379:methods; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D003628:Database Management Systems; D016208:Databases, Factual; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D008875:Middle Aged; D011237:Predictive Value of Tests; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D060388:Support Vector Machine