{"doi":"10.1200/jco.2015.60.8869","title":"Breast Density and Benign Breast Disease: Risk Assessment to Identify Women at High Risk of Breast Cancer","abstract":"<jats:sec><jats:title>Purpose</jats:title><jats:p>Women with proliferative breast lesions are candidates for primary prevention, but few risk models incorporate benign findings to assess breast cancer risk. We incorporated benign breast disease (BBD) diagnoses into the Breast Cancer Surveillance Consortium (BCSC) risk model, the only breast cancer risk assessment tool that uses breast density.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>We developed and validated a competing-risk model using 2000 to 2010 SEER data for breast cancer incidence and 2010 vital statistics to adjust for the competing risk of death. We used Cox proportional hazards regression to estimate the relative hazards for age, race/ethnicity, family history of breast cancer, history of breast biopsy, BBD diagnoses, and breast density in the BCSC.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>We included 1,135,977 women age 35 to 74 years undergoing mammography with no history of breast cancer; 17% of the women had a prior breast biopsy. During a mean follow-up of 6.9 years, 17,908 women were diagnosed with invasive breast cancer. The BCSC BBD model slightly overpredicted risk (expected-to-observed ratio, 1.04; 95% CI, 1.03 to 1.06) and had modest discriminatory accuracy (area under the receiver operator characteristic curve, 0.665). Among women with proliferative findings, adding BBD to the model increased the proportion of women with an estimated 5-year risk of 3% or higher from 9.3% to 27.8% (P &lt; .001).</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>The BCSC BBD model accurately estimates women's risk for breast cancer using breast density and BBD diagnoses. Greater numbers of high-risk women eligible for primary prevention after BBD diagnosis are identified using the BCSC BBD model.</jats:p></jats:sec>","journal":"Journal of Clinical Oncology","year":2015,"id":626098,"datarank":0.8233406589235032,"base_score":5.488937726156687,"endowment":5.488937726156687,"self_citation_contribution":0.8233406589235032,"citation_network_contribution":0.0,"self_endowment_contribution":0.8233406589235032,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":241,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":335039,"name":"Diana L. Miglioretti","orcid":"0000-0002-5547-1833","position":1,"is_corresponding":false},{"id":130371,"name":"Chin-Shang Li","orcid":"0000-0002-0054-4476","position":2,"is_corresponding":false},{"id":328451,"name":"Celine M. Vachon","orcid":"0000-0002-1962-9322","position":3,"is_corresponding":false},{"id":612054,"name":"Charlotte C. Gard","orcid":"0000-0001-7201-4450","position":4,"is_corresponding":false},{"id":328450,"name":"Karla Kerlikowske","orcid":"0000-0001-8793-8779","position":5,"is_corresponding":false},{"id":161116,"name":"Jeffrey A. Tice","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Breast Density and Benign Breast Disease: Risk Assessment to Identify Women at High Risk of Breast Cancer","abstract":"PURPOSE: Women with proliferative breast lesions are candidates for primary prevention, but few risk models incorporate benign findings to assess breast cancer risk. We incorporated benign breast disease (BBD) diagnoses into the Breast Cancer Surveillance Consortium (BCSC) risk model, the only breast cancer risk assessment tool that uses breast density. METHODS: We developed and validated a competing-risk model using 2000 to 2010 SEER data for breast cancer incidence and 2010 vital statistics to adjust for the competing risk of death. We used Cox proportional hazards regression to estimate the relative hazards for age, race/ethnicity, family history of breast cancer, history of breast biopsy, BBD diagnoses, and breast density in the BCSC. RESULTS: We included 1,135,977 women age 35 to 74 years undergoing mammography with no history of breast cancer; 17% of the women had a prior breast biopsy. During a mean follow-up of 6.9 years, 17,908 women were diagnosed with invasive breast cancer. The BCSC BBD model slightly overpredicted risk (expected-to-observed ratio, 1.04; 95% CI, 1.03 to 1.06) and had modest discriminatory accuracy (area under the receiver operator characteristic curve, 0.665). Among women with proliferative findings, adding BBD to the model increased the proportion of women with an estimated 5-year risk of 3% or higher from 9.3% to 27.8% (P<.001). CONCLUSION: The BCSC BBD model accurately estimates women's risk for breast cancer using breast density and BBD diagnoses. Greater numbers of high-risk women eligible for primary prevention after BBD diagnosis are identified using the BCSC BBD model.","is_dataset_classified":null,"base_score":5.488937726156687,"endowment":5.488937726156687,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"26282663","pmcid":"PMC4582144","openalex_id":"https://openalex.org/W2111784232","authors":[],"funders":[{"funder_name":"PHS HHS","grant_id":"HHSN261201100031C","title":null},{"funder_name":"NCI NIH HHS","grant_id":"U54 CA163303","title":null},{"funder_name":"NCI NIH HHS","grant_id":"P01 CA154292","title":null}],"total_grants":3,"fwci":13.1309,"citation_percentile":0.99224308,"influential_citations":0,"citation_trend":[{"year":2015,"count":4},{"year":2016,"count":21},{"year":2017,"count":12},{"year":2018,"count":23},{"year":2019,"count":21},{"year":2020,"count":27},{"year":2021,"count":32},{"year":2022,"count":27},{"year":2023,"count":18},{"year":2024,"count":25},{"year":2025,"count":21},{"year":2026,"count":9}],"oa_status":"closed","license":null,"oa_locations":[{"url":"https://doi.org/10.1200/jco.2015.60.8869","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/26282663","host_type":"repository"},{"url":"https://escholarship.org/uc/item/3qg8h7fk","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/4582144","host_type":"repository"}],"fields_of_study":["Digital Radiography and Breast Imaging","Breast Lesions and Carcinomas","Breast Cancer Treatment Studies"],"mesh_terms":["Adult","Aged","Biopsy","Breast","Breast Diseases","Breast Neoplasms","Decision Support Techniques","Female","Humans","Mammography","Middle Aged","Predictive Value of Tests","Prognosis","Registries","Risk Factors","ROC Curve","Time Factors","United States","Reproducibility of Results","Incidence","Multivariate Analysis","Proportional Hazards Models","Risk Assessment","Area Under Curve","Cell Proliferation","Kaplan-Meier Estimate"],"keywords":["Breast cancer","Medicine","Breast biopsy","Breast disease","Family history","Proportional hazards model","Gynecology","Mammography","Obstetrics","Relative risk","Risk assessment","Breast cancer screening","Oncology","Cancer","Internal medicine","Confidence interval"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Reduced inequalities"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-04T12:25:01.438203Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}