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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
External Evaluation of a Mammography-based Deep Learning Model for Predicting Breast Cancer in an Ethnically Diverse Population
Radiology: Artificial Intelligence, Volume 5, No. 6, Article e220299, Year 2023
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Description
Purpose: To externally evaluate a mammography-based deep learning (DL) model (Mirai) in a high-risk racially diverse population and compare its performance with other mammographic measures. Materials and Methods: A total of 6435 screening mammograms in 2096 female patients (median age, 56.4 years ± 11.2 [SD]) enrolled in a hospital-based case-control study from 2006 to 2020 were retrospectively evaluated. Pathologically confirmed breast cancer was the primary outcome. Mirai scores were the primary predictors. Breast density and Breast Imaging Reporting and Data System (BI-RADS) assessment categories were comparative predictors. Performance was evaluated using area under the receiver operating characteristic curve (AUC) and concordance index analyses. Results: Mirai achieved 1-and 5-year AUCs of 0.71 (95% CI: 0.68, 0.74) and 0.65 (95% CI: 0.64, 0.67), respectively. One-year AUCs for nondense versus dense breasts were 0.72 versus 0.58 (P = .10). There was no evidence of a difference in near-term discrimination performance between BI-RADS and Mirai (1-year AUC, 0.73 vs 0.68; P = .34). For longer-term prediction (2–5 years), Mirai outperformed BI-RADS assessment (5-year AUC, 0.63 vs 0.54; P < .001). Using only images of the unaffected breast reduced the discriminatory performance of the DL model (P < .001 at all time points), suggesting that its predictions are likely dependent on the detection of ipsilateral premalignant patterns. Conclusion: A mammography DL model showed good performance in a high-risk external dataset enriched for African American patients, benign breast disease, and BRCA mutation carriers, and study findings suggest that the model performance is likely driven by the detection of precancerous changes. © RSNA, 2023.
Authors & Co-Authors
Woodard, Anna E.
United States, Chicago
The University of Chicago
Zhao, Fangyuan
United States, Chicago
The University of Chicago
Yoshimatsu, Toshio F.
United States, Chicago
The University of Chicago
Zheng, Yonglan
United States, Chicago
The University of Chicago
Pearson, Alexander T.
United States, Chicago
The University of Chicago
Aribisala, Benjamin Segun
United States, Chicago
The University of Chicago
Nigeria, Lagos
Lagos State University
Olopade, Olufunmilayo Ibironke
United States, Chicago
The University of Chicago
Huo, Dezheng
United States, Chicago
The University of Chicago
Statistics
Citations: 1
Authors: 8
Affiliations: 2
Identifiers
Doi:
10.1148/ryai.220299
ISSN:
26386100
Research Areas
Cancer
Health System And Policy
Study Design
Cross Sectional Study
Case-Control Study
Participants Gender
Female