Predicting the gist of breast cancer on a screening mammogram using global radiomic features

This study explored if using a set of global radiomic (i.e., computer-extracted) features derived from mammograms could predict the gist of breast cancer (holistic perceptual information provided from radiologists' first impression about the presence of an image abnormality after a brief sight of the image). A retrospective de-identified dataset was used to collect the gist of breast cancer (i.e., gist scores) from 13 readers interpreting 1100 screening craniocaudal mammograms (659 current "normal" cancer-free images, and 441 "prior" no visible signs of cancer images acquired two years before current cancer mammograms). The collected gist scores from all readers were averaged to eliminate the noise of the gist signal, giving one gist score per image. The images were grouped into high- and low-gist based on the 75th and 25th percentiles of the images containing the highest and lowest gist scores, respectively. A set of 130 handcrafted global radiomic features per image were extracted and used to construct two machine learning random forest classifiers: 1). Normal and 2). Prior based on the corresponding features computed from the "normal" and "prior" images, for distinguishing high- from low-gist images. The classifiers were trained and validated using the 10-fold cross-validation approach and their performances were measured by the area under the receiver operating characteristic curve (AUC). The Normal and Prior classifiers resulted in AUCs of 0.83 (95% CI: 0.77-0.85) and 0.84 (95% CI: 0.80-0.87) respectively, suggesting that the global mammographic radiomic features can predict the gist of breast cancer on a screening mammogram.