ArticleRadiology. Artificial intelligence2023
External Evaluation of a Mammography-based Deep Learning Model for Predicting Breast Cancer in an Ethnically Diverse Population.
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Article in Radiology. Artificial intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
0numbers the graph read from it
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14citing papers in PubMed, 2 pooled it
3.7field-weighted citation impact, top 6% of its field
What it found
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 21 citations in OpenAlex.
- Artificial intelligence for risk-stratified breast cancer screening: a systematic review of evidence, clinical integration, and ethical implications in risk assessment tools.La Radiologia medica · 2026Pooled it
- Current state of mammography-based artificial intelligence for future breast cancer risk prediction: a systematic review.Journal of the National Cancer Institute · 2026Pooled it
- Integrating multi-omics data for next-generation cancer research and precision medicine.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Explainable Artificial Intelligence in Mammography: A Systematic Review of Methods, Evaluation Practices, and Clinical Readiness.Diagnostics (Basel, Switzerland) · 2026Review
- Preliminary exploration of radiomic mammographic analysis in triple negative breast cancer related to BRCA profile.Scientific reports · 2026Article
- Artificial Intelligence-Driven Personalization in Breast Cancer Screening: From Population Models to Individualized Protocols.Cancers · 2025Review
- Innovative Multi-View Strategies for AI-Assisted Breast Cancer Detection in Mammography.Journal of imaging · 2025Article
- Hierarchical diagnosis of breast phyllodes tumors enabled by deep learning of ultrasound images: a retrospective multi-center study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Artificial Intelligence and Cancer Health Equity: Bridging the Divide or Widening the Gap.Current oncology reports · 2025Review
- Deep learning in breast cancer risk prediction: a review of recent applications in full-field digital mammography.Frontiers in oncology · 2025Review
- Validation of the Mirai model for predicting breast cancer risk in Mexican women.Insights into imaging · 2024Article
- Radiofrequency ablation for capsular-located versus noncapsular-located papillary thyroid microcarcinoma: a propensity score matching study of 1095 patients.European radiology · 2024Article
- A multi-centre, randomised trial for diagnostic efficacy of the automatic breast volume scanner ultrasound for breast cancer screening in China.Frontiers in oncology · 2024Article
- Breast Cancer Risk Assessment in the AI Era: The Importance of Model Validation in Ethnically Diverse Cohorts.Radiology. Artificial intelligence · 2023Article
Corrections and comments
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Authors and funding
14 authors at 2 institutions in 1 country.
Olasubomi J OmoleyeFrom the Center for Clinical Cancer Genetics and Global Health, Department of Medicine (O.J.O., A.E.W., T.F.Y., Y.Z., B.S.A., O.I.O.), Data Science Institute (A.E.W.), Division of Hematology/Oncology, Department of Medicine (F.M.H., A.T.P.), Department of Public Health Sciences (F.Z., D.H.), Department of Computer Science (M.L.), and Department of Radiology (K.K., G.S.K., H.A.), The University of Chicago, 5841 S Maryland Ave, MC 2000, Chicago, IL 60637; Department of Computer Science, Lagos State University, Lagos, Nigeria (B.S.A.).ORCID https://orcid.org/0000-0002-6922-9298
Anna E WoodardFrom the Center for Clinical Cancer Genetics and Global Health, Department of Medicine (O.J.O., A.E.W., T.F.Y., Y.Z., B.S.A., O.I.O.), Data Science Institute (A.E.W.), Division of Hematology/Oncology, Department of Medicine (F.M.H., A.T.P.), Department of Public Health Sciences (F.Z., D.H.), Department of Computer Science (M.L.), and Department of Radiology (K.K., G.S.K., H.A.), The University of Chicago, 5841 S Maryland Ave, MC 2000, Chicago, IL 60637; Department of Computer Science, Lagos State University, Lagos, Nigeria (B.S.A.).ORCID 0000-0002-8640-5417
Frederick M HowardFrom the Center for Clinical Cancer Genetics and Global Health, Department of Medicine (O.J.O., A.E.W., T.F.Y., Y.Z., B.S.A., O.I.O.), Data Science Institute (A.E.W.), Division of Hematology/Oncology, Department of Medicine (F.M.H., A.T.P.), Department of Public Health Sciences (F.Z., D.H.), Department of Computer Science (M.L.), and Department of Radiology (K.K., G.S.K., H.A.), The University of Chicago, 5841 S Maryland Ave, MC 2000, Chicago, IL 60637; Department of Computer Science, Lagos State University, Lagos, Nigeria (B.S.A.).
Fangyuan ZhaoFrom the Center for Clinical Cancer Genetics and Global Health, Department of Medicine (O.J.O., A.E.W., T.F.Y., Y.Z., B.S.A., O.I.O.), Data Science Institute (A.E.W.), Division of Hematology/Oncology, Department of Medicine (F.M.H., A.T.P.), Department of Public Health Sciences (F.Z., D.H.), Department of Computer Science (M.L.), and Department of Radiology (K.K., G.S.K., H.A.), The University of Chicago, 5841 S Maryland Ave, MC 2000, Chicago, IL 60637; Department of Computer Science, Lagos State University, Lagos, Nigeria (B.S.A.).
Toshio F YoshimatsuFrom the Center for Clinical Cancer Genetics and Global Health, Department of Medicine (O.J.O., A.E.W., T.F.Y., Y.Z., B.S.A., O.I.O.), Data Science Institute (A.E.W.), Division of Hematology/Oncology, Department of Medicine (F.M.H., A.T.P.), Department of Public Health Sciences (F.Z., D.H.), Department of Computer Science (M.L.), and Department of Radiology (K.K., G.S.K., H.A.), The University of Chicago, 5841 S Maryland Ave, MC 2000, Chicago, IL 60637; Department of Computer Science, Lagos State University, Lagos, Nigeria (B.S.A.).ORCID 0000-0003-2674-8159
Yonglan ZhengFrom the Center for Clinical Cancer Genetics and Global Health, Department of Medicine (O.J.O., A.E.W., T.F.Y., Y.Z., B.S.A., O.I.O.), Data Science Institute (A.E.W.), Division of Hematology/Oncology, Department of Medicine (F.M.H., A.T.P.), Department of Public Health Sciences (F.Z., D.H.), Department of Computer Science (M.L.), and Department of Radiology (K.K., G.S.K., H.A.), The University of Chicago, 5841 S Maryland Ave, MC 2000, Chicago, IL 60637; Department of Computer Science, Lagos State University, Lagos, Nigeria (B.S.A.).ORCID 0000-0001-6597-7072
Alexander T PearsonFrom the Center for Clinical Cancer Genetics and Global Health, Department of Medicine (O.J.O., A.E.W., T.F.Y., Y.Z., B.S.A., O.I.O.), Data Science Institute (A.E.W.), Division of Hematology/Oncology, Department of Medicine (F.M.H., A.T.P.), Department of Public Health Sciences (F.Z., D.H.), Department of Computer Science (M.L.), and Department of Radiology (K.K., G.S.K., H.A.), The University of Chicago, 5841 S Maryland Ave, MC 2000, Chicago, IL 60637; Department of Computer Science, Lagos State University, Lagos, Nigeria (B.S.A.).
Maksim LeventalFrom the Center for Clinical Cancer Genetics and Global Health, Department of Medicine (O.J.O., A.E.W., T.F.Y., Y.Z., B.S.A., O.I.O.), Data Science Institute (A.E.W.), Division of Hematology/Oncology, Department of Medicine (F.M.H., A.T.P.), Department of Public Health Sciences (F.Z., D.H.), Department of Computer Science (M.L.), and Department of Radiology (K.K., G.S.K., H.A.), The University of Chicago, 5841 S Maryland Ave, MC 2000, Chicago, IL 60637; Department of Computer Science, Lagos State University, Lagos, Nigeria (B.S.A.).
Benjamin S AribisalaFrom the Center for Clinical Cancer Genetics and Global Health, Department of Medicine (O.J.O., A.E.W., T.F.Y., Y.Z., B.S.A., O.I.O.), Data Science Institute (A.E.W.), Division of Hematology/Oncology, Department of Medicine (F.M.H., A.T.P.), Department of Public Health Sciences (F.Z., D.H.), Department of Computer Science (M.L.), and Department of Radiology (K.K., G.S.K., H.A.), The University of Chicago, 5841 S Maryland Ave, MC 2000, Chicago, IL 60637; Department of Computer Science, Lagos State University, Lagos, Nigeria (B.S.A.).
Kirti KulkarniFrom the Center for Clinical Cancer Genetics and Global Health, Department of Medicine (O.J.O., A.E.W., T.F.Y., Y.Z., B.S.A., O.I.O.), Data Science Institute (A.E.W.), Division of Hematology/Oncology, Department of Medicine (F.M.H., A.T.P.), Department of Public Health Sciences (F.Z., D.H.), Department of Computer Science (M.L.), and Department of Radiology (K.K., G.S.K., H.A.), The University of Chicago, 5841 S Maryland Ave, MC 2000, Chicago, IL 60637; Department of Computer Science, Lagos State University, Lagos, Nigeria (B.S.A.).
Gregory S KarczmarFrom the Center for Clinical Cancer Genetics and Global Health, Department of Medicine (O.J.O., A.E.W., T.F.Y., Y.Z., B.S.A., O.I.O.), Data Science Institute (A.E.W.), Division of Hematology/Oncology, Department of Medicine (F.M.H., A.T.P.), Department of Public Health Sciences (F.Z., D.H.), Department of Computer Science (M.L.), and Department of Radiology (K.K., G.S.K., H.A.), The University of Chicago, 5841 S Maryland Ave, MC 2000, Chicago, IL 60637; Department of Computer Science, Lagos State University, Lagos, Nigeria (B.S.A.).ORCID 0000-0002-2455-5858
Olufunmilayo I OlopadeFrom the Center for Clinical Cancer Genetics and Global Health, Department of Medicine (O.J.O., A.E.W., T.F.Y., Y.Z., B.S.A., O.I.O.), Data Science Institute (A.E.W.), Division of Hematology/Oncology, Department of Medicine (F.M.H., A.T.P.), Department of Public Health Sciences (F.Z., D.H.), Department of Computer Science (M.L.), and Department of Radiology (K.K., G.S.K., H.A.), The University of Chicago, 5841 S Maryland Ave, MC 2000, Chicago, IL 60637; Department of Computer Science, Lagos State University, Lagos, Nigeria (B.S.A.).ORCID 0000-0002-9936-1599
Hiroyuki Abe *From the Center for Clinical Cancer Genetics and Global Health, Department of Medicine (O.J.O., A.E.W., T.F.Y., Y.Z., B.S.A., O.I.O.), Data Science Institute (A.E.W.), Division of Hematology/Oncology, Department of Medicine (F.M.H., A.T.P.), Department of Public Health Sciences (F.Z., D.H.), Department of Computer Science (M.L.), and Department of Radiology (K.K., G.S.K., H.A.), The University of Chicago, 5841 S Maryland Ave, MC 2000, Chicago, IL 60637; Department of Computer Science, Lagos State University, Lagos, Nigeria (B.S.A.).
Dezheng Huo *From the Center for Clinical Cancer Genetics and Global Health, Department of Medicine (O.J.O., A.E.W., T.F.Y., Y.Z., B.S.A., O.I.O.), Data Science Institute (A.E.W.), Division of Hematology/Oncology, Department of Medicine (F.M.H., A.T.P.), Department of Public Health Sciences (F.Z., D.H.), Department of Computer Science (M.L.), and Department of Radiology (K.K., G.S.K., H.A.), The University of Chicago, 5841 S Maryland Ave, MC 2000, Chicago, IL 60637; Department of Computer Science, Lagos State University, Lagos, Nigeria (B.S.A.).ORCID 0000-0002-4041-1678
Oregon Health & Science University · USMcCrone Research Institute · US
Funding
Developing an Oncology Workforce for the 21st Century - Renewal 01K12CA139160 · NCI · UNIVERSITY OF CHICAGO · PI OLOPADE, OLUFUNMILAYO F., SMITH, SONALI · 2010 to 2024
$10.6MUChicago Interdisciplinary Cancer Health Disparities SPOREP20CA233307 · NCI · UNIVERSITY OF CHICAGO · PI HUO, DEZHENG, NANDA, RITA · 2018 to 2020
$3.4MMultiscale Computational Models Guided By Emerging Cellular Dynamics Quantification For Predicting Optimum Immune Checkpoint And Targeted Therapy SchedulesU01CA243075 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI JACKSON, TRACHETTE · 2020 to 2024
$2.6MDeep learning for oral premalignancy evaluationR56DE030958 · NIDCR · UNIVERSITY OF CHICAGO · PI PEARSON, ALEXANDER THOMAS · 2021 to 2021
$331kNCI NIH HHS K12 CA139160NCI NIH HHS P20 CA233307NCI NIH HHS U01 CA243075NIDCR NIH HHS R56 DE030958
Abstract
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 ( Conclusion: A mammography DL model showed good performance in a high-risk external dataset enriched for African American patients, benign breast disease, and
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BreastCancerComputer ApplicationsConvolutional Neural NetworkDeep Learning AlgorithmsEpidemiologyInformaticsMachine LearningMammographyOncologyRadiomics
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