Deep learning modeling using normal mammograms for predicting breast cancer risk. Issue 1 (19th November 2019)

• Record Type: Journal Article
• Authors: Arefan, Dooman; Mohamed, Aly A.; Berg, Wendie A.; Zuley, Margarita L.; Sumkin, Jules H.; Wu, Shandong
• Is Part Of: Medical physics. Volume 47:Issue 1(2020)
• Page Start: 110
• Page End: 118
• Publication Date: 2019-11-19
• Subjects: breast cancer -- breast density -- deep learning -- digital mammography -- risk biomarkers; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.13886
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: To investigate two deep learning‐based modeling schemes for predicting short‐term risk of developing breast cancer using prior normal screening digital mammograms in a case‐control setting. Methods: We conducted a retrospective Institutional Review Board‐approved study on a case‐control cohort of 226 patients (including 113 women diagnosed with breast cancer and 113 controls) who underwent general population breast cancer screening. 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‐control cohort were analyzed to predict the outcome, i.e., developing breast cancer (cancer cases) or remaining breast cancer‐free (controls) within the follow‐up period. We implemented an end‐to‐end deep learning model and a GoogLeNet‐LDA 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. Results: The highest AUC was 0.73 [95% Confidence Interval (CI): 0.68–0.78; GoogLeNet‐LDA model on CC view] when using the whole‐breast and was 0.72 (95% CI: 0.67–0.76; GoogLeNet‐LDA model on MLO + CC view) when using the dense tissue, respectively, as the model input. The GoogleNet‐LDA model significantly (all P < 0.05) outperformed the end‐to‐end 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 = 0.54; 95% CI: 0.49–0.59). Conclusions: The proposed deep learning modeling approach can predict short‐term breast cancer risk using normal screening mammogram images. Larger studies are needed to further reveal the promise of deep learning in enhancing imaging‐based breast cancer risk assessment.