Breast density is a radiologic feature that reflects fibroglandular tissue content relative to breast area or volume, and it is a breast cancer risk factor. This study employed 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.
Data access: Datasets supporting figure 2, tables 2 and 3 and supplementary table 2 of the published article are publicly available in the figshare repository, as part of this data record (https://doi.org/10.6084/m9.figshare.9786152). These datasets are contained in the zip file NPJ FigShare.zip. Datasets supporting figure 3, table 1 and supplementary table 1 of the published article are not publicly available to protect patient privacy, but can be made available on request from Dr. Gretchen L. Gierach, Senior Investigator, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA, email address: gierachg@mail.nih.gov.
Study description and aims: The study aimed to identify tissue correlates of breast density that may be important for distinguishing malignant from benign biopsy diagnoses separately among women with high and low breast density, to help inform cancer risk stratification among women undergoing a biopsy following an abnormal mammogram. Haematoxylin and eosin (H&E)-stained digitized images from image-guided breast biopsies (n=852 patients) were evaluated. Breast density was assessed as global and localized fibroglandular volume (%). A convolutional neural network characterized H&E composition. 37 features were extracted from the network output, describing tissue quantities and morphological structure. A random forest regression model was trained to identify correlates most predictive of fibroglandular volume (n=588). Correlations between predicted and radiologically quantified fibroglandular volume were assessed in 264 independent patients. A second random forest classifier was trained to predict diagnosis (invasive vs. benign); performance was assessed using area under receiver-operating characteristics curves (AUC). For more details on the methodology please see the published article.
Study approval: The Institutional Review Boards at the NCI and the University of Vermont approved the protocol for this project for either active consenting or a waiver of consent to enrol participants, link data and ...
Data access: Datasets supporting figure 2, tables 2 and 3 and supplementary table 2 of the published article are publicly available in the figshare repository, as part of this data record (https://doi.org/10.6084/m9.figshare.9786152). These datasets are contained in the zip file NPJ FigShare.zip. Datasets supporting figure 3, table 1 and supplementary table 1 of the published article are not publicly available to protect patient privacy, but can be made available on request from Dr. Gretchen L. Gierach, Senior Investigator, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA, email address: gierachg@mail.nih.gov.
Study description and aims: The study aimed to identify tissue correlates of breast density that may be important for distinguishing malignant from benign biopsy diagnoses separately among women with high and low breast density, to help inform cancer risk stratification among women undergoing a biopsy following an abnormal mammogram. Haematoxylin and eosin (H&E)-stained digitized images from image-guided breast biopsies (n=852 patients) were evaluated. Breast density was assessed as global and localized fibroglandular volume (%). A convolutional neural network characterized H&E composition. 37 features were extracted from the network output, describing tissue quantities and morphological structure. A random forest regression model was trained to identify correlates most predictive of fibroglandular volume (n=588). Correlations between predicted and radiologically quantified fibroglandular volume were assessed in 264 independent patients. A second random forest classifier was trained to predict diagnosis (invasive vs. benign); performance was assessed using area under receiver-operating characteristics curves (AUC). For more details on the methodology please see the published article.
Study approval: The Institutional Review Boards at the NCI and the University of Vermont approved the protocol for this project for either active consenting or a waiver of consent to enrol participants, link data and ...