Journal of Breast Imaging最新文献_第4页

Mathematical Modeling to Address Questions in Breast Cancer Screening: An Overview of the Breast Cancer Models of the Cancer Intervention and Surveillance Modeling Network. 数学建模以解决乳腺癌筛查中的问题：癌症干预和监测建模网络的乳腺癌模型概述。

IF 2

Journal of Breast Imaging Pub Date : 2025-03-18 DOI: 10.1093/jbi/wbaf003

Oguzhan Alagoz, Jennifer L Caswell-Jin, Harry J de Koning, Hui Huang, Xuelin Huang, Sandra J Lee, Yisheng Li, Sylvia K Plevritis, Swarnavo Sarkar, Clyde B Schechter, Natasha K Stout, Amy Trentham-Dietz, Nicolien van Ravesteyn, Kathryn P Lowry

{"title":"Mathematical Modeling to Address Questions in Breast Cancer Screening: An Overview of the Breast Cancer Models of the Cancer Intervention and Surveillance Modeling Network.","authors":"Oguzhan Alagoz, Jennifer L Caswell-Jin, Harry J de Koning, Hui Huang, Xuelin Huang, Sandra J Lee, Yisheng Li, Sylvia K Plevritis, Swarnavo Sarkar, Clyde B Schechter, Natasha K Stout, Amy Trentham-Dietz, Nicolien van Ravesteyn, Kathryn P Lowry","doi":"10.1093/jbi/wbaf003","DOIUrl":"10.1093/jbi/wbaf003","url":null,"abstract":"The National Cancer Institute-funded Cancer Intervention and Surveillance Modeling Network (CISNET) breast cancer mathematical models have been increasingly utilized by policymakers to address breast cancer screening policy decisions and influence clinical practice. These well-established and validated models have a successful track record of use in collaborations spanning over 2 decades. While mathematical modeling is a valuable approach to translate short-term screening performance data into long-term breast cancer outcomes, it is inherently complex and requires numerous inputs to approximate the impacts of breast cancer screening. This review article describes the 6 independently developed CISNET breast cancer models, with a particular focus on how they represent breast cancer screening and estimate the contribution of screening to breast cancer mortality reduction and improvements in life expectancy. We also describe differences in structures and assumptions across the models and how variation in model results can highlight areas of uncertainty. Finally, we offer insight into how the results generated by the models can be used to aid decision-making regarding breast cancer screening policy.","PeriodicalId":43134,"journal":{"name":"Journal of Breast Imaging","volume":" ","pages":"141-154"},"PeriodicalIF":2.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11920616/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143558328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A How-to Guide for Community Breast Imaging Centers: Starting a Breast Imaging Fellowship. 社区乳腺成像中心操作指南》：启动乳腺成像奖学金。

IF 2

Journal of Breast Imaging Pub Date : 2025-03-18 DOI: 10.1093/jbi/wbae069

Randy C Miles, Antonio R Lopez, Nhat-Tuan Tran, Christopher Doyle, Charmi Vijapura, Rifat A Wahab, David M Naeger

引用次数: 0

Artificial Intelligence-based Software for Breast Arterial Calcification Detection on Mammograms. 基于人工智能的乳房 X 光照片乳腺动脉钙化检测软件。

IF 2

Journal of Breast Imaging Pub Date : 2025-03-18 DOI: 10.1093/jbi/wbae064

Alyssa T Watanabe, Valerie Dib, Junhao Wang, Richard Mantey, William Daughton, Chi Yung Chim, Gregory Eckel, Caroline Moss, Vinay Goel, Nitesh Nerlekar

{"title":"Artificial Intelligence-based Software for Breast Arterial Calcification Detection on Mammograms.","authors":"Alyssa T Watanabe, Valerie Dib, Junhao Wang, Richard Mantey, William Daughton, Chi Yung Chim, Gregory Eckel, Caroline Moss, Vinay Goel, Nitesh Nerlekar","doi":"10.1093/jbi/wbae064","DOIUrl":"10.1093/jbi/wbae064","url":null,"abstract":"Objective: The performance of a commercially available artificial intelligence (AI)-based software that detects breast arterial calcifications (BACs) on mammograms is presented.Methods: This retrospective study was exempt from IRB approval and adhered to the HIPAA regulations. Breast arterial calcification detection using AI was assessed in 253 patients who underwent 314 digital mammography (DM) examinations and 143 patients who underwent 277 digital breast tomosynthesis (DBT) examinations between October 2004 and September 2022. Artificial intelligence performance for binary BAC detection was compared with ground truth (GT) determined by the majority consensus of breast imaging radiologists. Area under the receiver operating curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value (NPV), accuracy, and BAC prevalence rates of the AI algorithm were compared.Results: The case-level AUCs of AI were 0.96 (0.93-0.98) for DM and 0.95 (0.92-0.98) for DBT. Sensitivity, specificity, and accuracy were 87% (79%-93%), 92% (88%-96%), and 91% (87%-94%) for DM and 88% (80%-94%), 90% (84%-94%), and 89% (85%-92%) for DBT. Positive predictive value and NPV were 82% (72%-89%) and 95% (92%-97%) for DM and 84% (76%-90%) and 92% (88%-96%) for DBT, respectively. Results are 95% confidence intervals. Breast arterial calcification prevalence was similar for both AI and GT assessments.Conclusion: Breast AI software for detection of BAC presence on mammograms showed promising performance for both DM and DBT examinations. Artificial intelligence has potential to aid radiologists in detection and reporting of BAC on mammograms, which is a known cardiovascular risk marker specific to women.","PeriodicalId":43134,"journal":{"name":"Journal of Breast Imaging","volume":" ","pages":"168-176"},"PeriodicalIF":2.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142548165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unknown Case: Man With a Palpable Retroareolar Mass. 不明病例：可触及乳晕后肿块的男子。

IF 2

Journal of Breast Imaging Pub Date : 2025-03-18 DOI: 10.1093/jbi/wbae003

Hieu Diep, Cherie M Kuzmiak

引用次数: 0

Stop Training Artificial Intelligence Algorithms Now. Start Prospective Trials! 停止训练人工智能算法。开始前瞻性试验！

IF 2

Journal of Breast Imaging Pub Date : 2025-03-18 DOI: 10.1093/jbi/wbae083

Robert M Nishikawa, Alisa Sumkin

引用次数: 0

Recent Trends in Breast Cancer Mortality Rates for U.S. Women by Age and Race/Ethnicity. 按年龄和种族/民族划分的美国妇女乳腺癌死亡率的最新趋势。

IF 2

Journal of Breast Imaging Pub Date : 2025-03-06 DOI: 10.1093/jbi/wbaf007

Debra L Monticciolo, R Edward Hendrick

{"title":"Recent Trends in Breast Cancer Mortality Rates for U.S. Women by Age and Race/Ethnicity.","authors":"Debra L Monticciolo, R Edward Hendrick","doi":"10.1093/jbi/wbaf007","DOIUrl":"https://doi.org/10.1093/jbi/wbaf007","url":null,"abstract":"Objective: To analyze recent trends in U.S. breast cancer mortality rates by age group and race and ethnicity.Methods: This retrospective analysis of female breast cancer mortality rates used National Center for Health Statistics data from 1990 to 2022 for all women, by age group, and by race or ethnicity. Joinpoint analysis assessed trends in breast cancer mortality rates.Results: Breast cancer mortality rates for women 20 to 39 years old decreased 2.8% per year from 1999 to 2010 but showed no decline from 2010 to 2022 (annual percentage change [APC], -0.01; P = .98). For women of ages 40 to 74 years, breast cancer mortality rates decreased 1.7% to 3.9% per year from 1990 to 2022 (P <.001); a decline was found for all cohorts in this age group except Asian women. For women ≥75 years of age, breast cancer mortality rates declined significantly from 1993 to 2013 (APC, -1.26; P = .01) but showed no evidence of decline from 2013 to 2022 (APC, -0.2; P = .24). Across all ages, breast cancer mortality rates declined for White and Black women but not for Asian, Hispanic, and Native American women. Asian women ≥75 years of age had significantly increasing mortality rates (APC, 0.73; P <.001). For 2004 to 2022, breast cancer mortality rates were 39% higher in Black women than White women and varied strongly by age group: 104% for ages 20 to 39 years, 51% for ages 40 to 74 years, and 13% for ages ≥75 years.Conclusion: Female breast cancer mortality rates have stopped declining in women <40 years of age and >74 years of age. The higher mortality rates in Black women compared with White women are age dependent and substantially higher in younger women.","PeriodicalId":43134,"journal":{"name":"Journal of Breast Imaging","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143574308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effective Integration of Feedback in Breast Imaging: A "Guide for the Trainee". 乳腺成像反馈的有效整合：“培训生指南”。

IF 2

Journal of Breast Imaging Pub Date : 2025-02-22 DOI: 10.1093/jbi/wbae095

Joshua A Greenstein, Martha Sevenich, Allison Aripoli

引用次数: 0

Potential Impact of an Artificial Intelligence-based Mammography Triage Algorithm on Performance and Workload in a Population-based Screening Sample. 基于人工智能的乳腺 X 射线照相术分流算法对人群筛查样本的性能和工作量的潜在影响。

IF 2

Journal of Breast Imaging Pub Date : 2025-01-25 DOI: 10.1093/jbi/wbae056

Alyssa T Watanabe, Hoanh Vu, Chi Y Chim, Andrew W Litt, Tara Retson, Ray C Mayo

{"title":"Potential Impact of an Artificial Intelligence-based Mammography Triage Algorithm on Performance and Workload in a Population-based Screening Sample.","authors":"Alyssa T Watanabe, Hoanh Vu, Chi Y Chim, Andrew W Litt, Tara Retson, Ray C Mayo","doi":"10.1093/jbi/wbae056","DOIUrl":"10.1093/jbi/wbae056","url":null,"abstract":"Objective: To evaluate potential screening mammography performance and workload impact using a commercial artificial intelligence (AI)-based triage device in a population-based screening sample.Methods: In this retrospective study, a sample of 2129 women who underwent screening mammograms were evaluated. The performance of a commercial AI-based triage device was compared with radiologists' reports, actual outcomes, and national benchmarks using commonly used mammography metrics. Up to 5 years of follow-up examination results were evaluated in cases to establish benignity. The algorithm sorted cases into groups of \"suspicious\" and \"low suspicion.\" A theoretical workload reduction was calculated by subtracting cases triaged as \"low suspicion\" from the sample.Results: At the default 93% sensitivity setting, there was significant improvement (P <.05) in the following triage simulation mean performance measures compared with actual outcome: 45.5% improvement in recall rate (13.4% to 7.3%; 95% CI, 6.2-8.3), 119% improvement in positive predictive value (PPV) 1 (5.3% to 11.6%; 95% CI, 9.96-13.4), 28.5% improvement in PPV2 (24.6% to 31.6%; 95% CI, 24.8-39.1), 20% improvement in sensitivity (83.3% to 100%; 95% CI, 100-100), and 7.2% improvement in specificity (87.2% to 93.5%; 95% CI, 92.4-94.5). A theoretical 62.5% workload reduction was possible. At the ultrahigh 99% sensitivity setting, a theoretical 27% workload reduction was possible. No cancers were missed by the algorithm at either sensitivity.Conclusion: Artificial intelligence-based triage in this simulation demonstrated potential for significant improvement in mammography performance and predicted substantial theoretical workload reduction without any missed cancers.","PeriodicalId":43134,"journal":{"name":"Journal of Breast Imaging","volume":" ","pages":"45-53"},"PeriodicalIF":2.0,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142156257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

External Validation of a Commercial Artificial Intelligence Algorithm on a Diverse Population for Detection of False Negative Breast Cancers. 在不同人群中对商用人工智能算法进行外部验证，以检测假阴性乳腺癌。

IF 2

Journal of Breast Imaging Pub Date : 2025-01-25 DOI: 10.1093/jbi/wbae058

S Reed Plimpton, Hannah Milch, Christopher Sears, James Chalfant, Anne Hoyt, Cheryce Fischer, William Hsu, Melissa Joines

{"title":"External Validation of a Commercial Artificial Intelligence Algorithm on a Diverse Population for Detection of False Negative Breast Cancers.","authors":"S Reed Plimpton, Hannah Milch, Christopher Sears, James Chalfant, Anne Hoyt, Cheryce Fischer, William Hsu, Melissa Joines","doi":"10.1093/jbi/wbae058","DOIUrl":"10.1093/jbi/wbae058","url":null,"abstract":"Objective: There are limited data on the application of artificial intelligence (AI) on nonenriched, real-world screening mammograms. This work aims to evaluate the ability of AI to detect false negative cancers not detected at the time of screening when reviewed by the radiologist alone.Methods: A commercially available AI algorithm was retrospectively applied to patients undergoing screening full-field digital mammography (FFDM) or digital breast tomosynthesis (DBT) at a single institution from 2010 to 2019. Ground truth was established based on 1-year follow-up data. Descriptive statistics were performed with attention focused on AI detection of false negative cancers within these subsets.Results: A total of 26 694 FFDM and 3183 DBT examinations were analyzed. Artificial intelligence was able to detect 7/13 false negative cancers (54%) in the FFDM cohort and 4/10 (40%) in the DBT cohort on the preceding screening mammogram that was interpreted as negative by the radiologist. Of these, 4 in the FFDM cohort and 4 in the DBT cohort were identified in breast densities of C or greater. False negative cancers detected by AI were predominantly luminal A invasive malignancies (9/11, 82%). Artificial intelligence was able to detect these false negative cancers a median time of 272 days sooner in the FFDM cohort and 248 days sooner in the DBT cohort compared to the radiologist.Conclusion: Artificial intelligence was able to detect cancers at the time of screening that were missed by the radiologist. Prospective studies are needed to evaluate the synergy of AI and the radiologist in real-world settings, especially on DBT examinations.","PeriodicalId":43134,"journal":{"name":"Journal of Breast Imaging","volume":" ","pages":"16-26"},"PeriodicalIF":2.0,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11770225/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142477137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unknown Case: Metastatic Breast Cancer With Abnormal Soft Tissue Mass in the Shoulder. 未知病例：转移性乳腺癌伴肩部异常软组织肿块

IF 2

Journal of Breast Imaging Pub Date : 2025-01-25 DOI: 10.1093/jbi/wbae005

Colin Marshall, Holly Marshall

引用次数: 0