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For each patient, a prior normal (i.e., with negative or benign findings) digital mammogram examination [including mediolateral oblique (MLO) view and craniocaudal (CC) view two images] was collected. Thus, a total of 452 normal images (226 MLO view images and 226 CC view images) of this case\u2010control cohort were analyzed to predict the outcome, i.e., developing breast cancer (cancer cases) or remaining breast cancer\u2010free (controls) within the follow\u2010up period. We implemented an end\u2010to\u2010end deep learning model and a GoogLeNet\u2010LDA model and compared their effects in several experimental settings using two mammographic view images and inputting two different subregions of the images to the models. The proposed models were also compared to logistic regression modeling of mammographic breast density. Area under the receiver operating characteristic curve (AUC) was used as the model performance metric.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The highest AUC was 0.73 [95% Confidence Interval (CI): 0.68\u20130.78; GoogLeNet\u2010LDA model on CC view] when using the whole\u2010breast and was 0.72 (95% CI: 0.67\u20130.76; GoogLeNet\u2010LDA model on MLO\u00a0+\u00a0CC view) when using the dense tissue, respectively, as the model input. The GoogleNet\u2010LDA model significantly (all <jats:italic>P<\/jats:italic>\u00a0&lt;\u00a00.05) outperformed the end\u2010to\u2010end GoogLeNet model in all experiments. CC view was consistently more predictive than MLO view in both deep learning models, regardless of the input subregions. Both models exhibited superior performance than the percent breast density (AUC\u00a0=\u00a00.54; 95% CI: 0.49\u20130.59).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The proposed deep learning modeling approach can predict short\u2010term breast cancer risk using normal screening mammogram images. Larger studies are needed to further reveal the promise of deep learning in enhancing imaging\u2010based breast cancer risk assessment.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.13886","type":"journal-article","created":{"date-parts":[[2019,10,31]],"date-time":"2019-10-31T01:18:43Z","timestamp":1572484723000},"page":"110-118","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":112,"title":["Deep learning modeling using normal mammograms for predicting breast cancer risk"],"prefix":"10.1002","volume":"47","author":[{"given":"Dooman","family":"Arefan","sequence":"first","affiliation":[{"name":"Department of Radiology University of Pittsburgh School of Medicine 4200 Fifth Ave Pittsburgh PA 15260 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aly A.","family":"Mohamed","sequence":"additional","affiliation":[{"name":"Department of Radiology University of Pittsburgh School of Medicine 4200 Fifth Ave Pittsburgh PA 15260 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wendie A.","family":"Berg","sequence":"additional","affiliation":[{"name":"Department of Radiology University of Pittsburgh School of Medicine 4200 Fifth Ave Pittsburgh PA 15260 USA"},{"name":"Magee\u2010Womens Hospital of University of Pittsburgh Medical Center 300 Halket St Pittsburgh PA 15213 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Margarita L.","family":"Zuley","sequence":"additional","affiliation":[{"name":"Department of Radiology University of Pittsburgh School of Medicine 4200 Fifth Ave Pittsburgh PA 15260 USA"},{"name":"Magee\u2010Womens Hospital of University of Pittsburgh Medical Center 300 Halket St Pittsburgh PA 15213 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jules H.","family":"Sumkin","sequence":"additional","affiliation":[{"name":"Department of Radiology University of Pittsburgh School of Medicine 4200 Fifth Ave Pittsburgh PA 15260 USA"},{"name":"Magee\u2010Womens Hospital of University of Pittsburgh Medical Center 300 Halket St Pittsburgh PA 15213 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shandong","family":"Wu","sequence":"additional","affiliation":[{"name":"Departments of Radiology, Biomedical Informatics, Bioengineering, and Intelligent Systems Program University of Pittsburgh 4200 Fifth Ave Pittsburgh PA 15260 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2019,11,19]]},"reference":[{"key":"e_1_2_8_2_1","volume-title":"Breast imaging reporting and data system (BI\u2010RADS)","author":"D'orsi C","year":"1998"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4736530"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.breast.2012.01.005"},{"key":"e_1_2_8_5_1","unstructured":"AlainG BengioY.Understanding intermediate layers using linear classifier probes arXiv preprint arXiv:1610.01644 (2016)."},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa062790"},{"key":"e_1_2_8_7_1","first-page":"1159","article-title":"Breast density and parenchymal patterns as markers of breast cancer risk: a meta\u2010analysis, cancer epidemiology and prevention","volume":"15","author":"McCormack VA","year":"2006","journal-title":"Biomarkers"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics7020030"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamaoncol.2016.6326"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13058-016-0755-8"},{"key":"e_1_2_8_11_1","doi-asserted-by":"crossref","unstructured":"KellerBM ChenJ ConantEF KontosD. 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