Journal of Breast Imaging最新文献_第2页

Cystic Neutrophilic Granulomatous Mastitis: Imaging Features With Histopathologic Correlation. 囊性中性肉芽肿性乳腺炎：与组织病理学相关的影像学特征

IF 2

Journal of Breast Imaging Pub Date : 2025-03-18 DOI: 10.1093/jbi/wbae077

Joseph J Villavicencio, Sophia R O'Brien, Tom Hu, Samantha Zuckerman

{"title":"Cystic Neutrophilic Granulomatous Mastitis: Imaging Features With Histopathologic Correlation.","authors":"Joseph J Villavicencio, Sophia R O'Brien, Tom Hu, Samantha Zuckerman","doi":"10.1093/jbi/wbae077","DOIUrl":"10.1093/jbi/wbae077","url":null,"abstract":"Cystic neutrophilic granulomatous mastitis (CNGM) is a rare type of granulomatous lobular mastitis (GLM) with a distinct histologic pattern characterized on histopathology by clear lipid vacuoles lined by peripheral neutrophils (\"suppurative lipogranulomas\"), often containing gram-positive bacilli and strongly associated with Corynebacterial infection (in particular, Corynebacterium kroppenstedtii). Cystic neutrophilic granulomatous mastitis has a distinct histopathologic appearance, but the imaging appearance is less well described and has been limited to case reports and small case series published primarily in pathology literature. Mammographic findings of CNGM include focal asymmetry, skin thickening, and irregular or oval masses. Sonographic findings of CNGM include irregular mass, complex collection/abscess, dilated ducts with intraductal debris, axillary lymphadenopathy, and skin thickening with subcutaneous edema. The imaging features of CNGM are nonspecific, and biopsy is required. Identifying a causative organism, when possible, requires a Gram stain, microbiological culture, and, potentially, molecular analysis. Although therapeutic options exist for CNGM, including antibiotics, steroids, and surgical intervention, there is no current consensus on optimal treatment.","PeriodicalId":43134,"journal":{"name":"Journal of Breast Imaging","volume":" ","pages":"204-213"},"PeriodicalIF":2.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142829613","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

Axillary Lymph Nodes T2 Signal Intensity Characterization in MRI of Patients With Mucinous Breast Cancer: A Pilot Study. 黏液性乳腺癌患者 MRI 中腋窝淋巴结 T2 信号强度特征：一项试点研究

IF 2

Journal of Breast Imaging Pub Date : 2025-03-18 DOI: 10.1093/jbi/wbae078

Noam Nissan, Jill Gluskin, Yuki Arita, R Elena Ochoa-Albiztegui, Hila Fruchtman-Brot, Maxine S Jochelson, Janice S Sung

{"title":"Axillary Lymph Nodes T2 Signal Intensity Characterization in MRI of Patients With Mucinous Breast Cancer: A Pilot Study.","authors":"Noam Nissan, Jill Gluskin, Yuki Arita, R Elena Ochoa-Albiztegui, Hila Fruchtman-Brot, Maxine S Jochelson, Janice S Sung","doi":"10.1093/jbi/wbae078","DOIUrl":"10.1093/jbi/wbae078","url":null,"abstract":"Objective: To evaluate the T2 signal intensity (SI) of axillary lymph nodes as a potential functional imaging marker for metastasis in patients with mucinous breast cancer.Methods: A retrospective review of breast MRIs performed from April 2008 to March 2024 was conducted to identify patients with mucinous breast cancer and adenopathy. Two independent, masked readers qualitatively assessed the T2 SI of tumors and lymph nodes. The T2 SI ratio for adenopathy and contralateral normal lymph nodes was quantitatively measured using the ipsilateral pectoralis muscle as a reference. Comparisons between malignant and nonmalignant lymph nodes were made using the chi-square test for qualitative assessments and the Mann-Whitney U test for quantitative assessments.Results: Of 17 patients (all female; mean age, 48.4 ± 10.7 years; range: 29-80 years), 12 had malignant nodes, while 5 had benign nodes. Qualitative assessment revealed that the primary mucinous breast cancer was T2 hyperintense in most cases (88.2%-94.1%). No significant difference in qualitative T2 hyperintensity was observed between malignant and nonmalignant nodes (P = .51-.84). Quantitative T2 SI ratio parameters, including the ratio of mean and minimal node T2 SI to mean ipsilateral pectoralis muscle T2 SI, were higher in malignant nodes vs benign and contralateral normal nodes (P <.05).Conclusion: Metastatic axillary lymph nodes exhibit high T2 SI, which could serve as a functional biomarker beyond traditional morphological assessment. Future studies should prioritize investigating more precise measurements, such as T2 mapping, and confirm these results in larger groups and across mucinous neoplasms in other organs.","PeriodicalId":43134,"journal":{"name":"Journal of Breast Imaging","volume":" ","pages":"187-195"},"PeriodicalIF":2.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11920617/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142829672","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

Mathematical Modeling to Address Questions in Breast Cancer Screening: An Overview of the Breast Cancer Models of the Cancer Intervention and Surveillance Modeling Network.

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

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

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

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

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.

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".

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

Optimizing Screening Outcomes: A Guide for Breast Imaging Practices.

IF 2

Journal of Breast Imaging Pub Date : 2025-02-20 DOI: 10.1093/jbi/wbae093

Sora C Yoon, Jay A Baker, Lars J Grimm

{"title":"Optimizing Screening Outcomes: A Guide for Breast Imaging Practices.","authors":"Sora C Yoon, Jay A Baker, Lars J Grimm","doi":"10.1093/jbi/wbae093","DOIUrl":"https://doi.org/10.1093/jbi/wbae093","url":null,"abstract":"Radiologists face a range of challenges to maximize the life-saving benefits of screening mammography, including pressure to maintain accuracy, manage heavy workloads, and minimize the risk of fatigue and burnout. This review provides targeted strategies to address these challenges and, ultimately, to improve interpretive performance of screening mammography. Workflow optimizations, including offline vs online and batched vs nonbatched interpretation, interrupted vs uninterrupted reading, and the importance of comparing current mammograms with prior examinations will be explored. Each strategy has strengths, weaknesses, and logistical challenges that must be tailored to the individual practice environment. Moreover, as breast radiologists contend with increasingly busy and hectic working conditions, practical solutions to protect reading environments and minimize distractions, such as the \"sterile cockpit\" approach, will be described. Additionally, breast radiologists are at greater risk for fatigue and burnout due to rising clinic volumes and an inadequate workforce. Optimizing the approach to reading screens is critical to helping breast imaging radiologists maintain and maximize the benefits of screening mammography, ensure the best outcomes for our patients, and maintain radiologist job satisfaction.","PeriodicalId":43134,"journal":{"name":"Journal of Breast Imaging","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143469251","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