Journal of Breast Imaging最新文献_第5页

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

Correction to: Utilization of Texture Analysis in Differentiating Benign and Malignant Breast Masses: Comparison of Grayscale US, Shear Wave Elastography, and Radiomic Features. 更正：利用纹理分析区分良性和恶性乳腺肿块：灰度超声、剪切波弹性成像和放射线学特征的比较。

IF 2

Journal of Breast Imaging Pub Date : 2025-01-25 DOI: 10.1093/jbi/wbae063

引用次数: 0

The Utility of Second-Look US to Evaluate Abnormal Molecular Breast Imaging Findings: A Retrospective Study. 二诊 US 对评估异常分子乳腺成像结果的实用性：回顾性研究。

IF 2

Journal of Breast Imaging Pub Date : 2025-01-25 DOI: 10.1093/jbi/wbae059

Davis C Teichgraeber, Roland L Bassett, Gary J Whitman

{"title":"The Utility of Second-Look US to Evaluate Abnormal Molecular Breast Imaging Findings: A Retrospective Study.","authors":"Davis C Teichgraeber, Roland L Bassett, Gary J Whitman","doi":"10.1093/jbi/wbae059","DOIUrl":"10.1093/jbi/wbae059","url":null,"abstract":"Objective: The purpose of this study was to evaluate the utility of US for identifying and characterizing lesions detected on molecular breast imaging (MBI).Methods: A retrospective single-institution review was performed of patients with MBI studies with subsequent US for abnormal MBI findings between January 1, 2015, and September 30, 2021. Medical records, imaging, and histopathology were reviewed. The reference standard was histopathology and/or imaging follow-up. Associations among MBI findings, the presence of an US correlate, and histopathology were evaluated by Fisher exact tests.Results: The 32 lesions detected on MBI in 25 patients were evaluated by US, and 19 lesions had an US correlate (19/32, 59%). Mass uptake was more likely to have an US correlate (11/13, 85%; P = .02) than nonmass uptake (7/19, 37%), and mass uptake was more likely to be malignant (5/13, 38%; P = .01). Of the 13 lesions without an US correlate, 5 were evaluated and subsequently biopsied by MRI (2 high-risk lesions and 3 benign lesions). Follow-up MBIs demonstrated stability/resolution for 5 lesions in 4 patients at 6 months or longer. Three patients had no further imaging.Conclusion: Mass lesions identified on MBI were more likely to have an US correlate and were more likely to be malignant than nonmass lesions.","PeriodicalId":43134,"journal":{"name":"Journal of Breast Imaging","volume":" ","pages":"27-34"},"PeriodicalIF":2.0,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11770226/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509995","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

Invasive Lobular Carcinoma in the Screening Setting. 浸润性小叶癌的筛查。

IF 2

Journal of Breast Imaging Pub Date : 2025-01-25 DOI: 10.1093/jbi/wbae082

Beatriu Reig, Laura Heacock

引用次数: 0