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For predicting cancer among high and low fibroglandular volume, the classifier achieved AUCs of 0.92 and 0.84, respectively, with epithelial organizational features ranking most important. Using extracted features, regression models predicted global ( r = 0.94) and localized ( r = 0.93) fibroglandular volume, with fat and non-fatty stromal content representing the strongest correlates, followed by epithelial organization rather than quantity. benign) performance was assessed using area under receiver-operating characteristics curves (AUC). A second random forest classifier was trained to predict diagnosis (invasive vs. Correlations between predicted and radiologically quantified fibroglandular volume were assessed in 264 independent patients. A random forest regression model was trained to identify correlates most predictive of fibroglandular volume ( n = 588). In total 37 features were extracted from the network output, describing tissue quantities and morphological structure. A convolutional neural network characterized H&E composition. Breast density was assessed as global and localized fibroglandular volume (%). We evaluated hematoxylin and eosin (H&E)-stained digitized images from image-guided breast biopsies ( n = 852 patients). Here we use deep learning approaches to identify histologic correlates in radiologically-guided biopsies that may underlie breast density and distinguish cancer among women with elevated and low density.
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Its histology is incompletely characterized. Breast density, a breast cancer risk factor, is a radiologic feature that reflects fibroglandular tissue content relative to breast area or volume.