Journal of Breast Imaging最新文献_第4页

Evaluation of a Multi-Instant Multimodal Artificial Intelligence System Supporting Interpretive and Noninterpretive Functions. 评估支持释义和非释义功能的多即时多模式人工智能系统。

IF 2

Journal of Breast Imaging Pub Date : 2025-03-18 DOI: 10.1093/jbi/wbae062

Serena Pacilè, Pauline Germaine, Caroline Sclafert, Thomas Bertinotti, Pierre Fillard, Svati Singla Long

{"title":"Evaluation of a Multi-Instant Multimodal Artificial Intelligence System Supporting Interpretive and Noninterpretive Functions.","authors":"Serena Pacilè, Pauline Germaine, Caroline Sclafert, Thomas Bertinotti, Pierre Fillard, Svati Singla Long","doi":"10.1093/jbi/wbae062","DOIUrl":"10.1093/jbi/wbae062","url":null,"abstract":"Objective: Artificial intelligence (AI) has been shown to hold promise for improving breast cancer screening, offering advanced capabilities to enhance diagnostic accuracy and efficiency. This study aimed to evaluate the impact of a multimodal multi-instant AI-based system on the diagnostic performance of radiologists in interpreting mammograms.Methods: We designed a multireader multicase study taking into account the evaluation of both interpretive and noninterpretive tasks. The study was approved by an institutional review board and is compliant with HIPAA. The dataset included 90 cancer-proven and 150 negative cases. The overall diagnostic performance was compared between the unaided vs aided reading condition. Intraclass correlation coefficient (ICC), Fleiss's kappa, and accuracy were used to quantify the agreement and performance on noninterpretive tasks. Reading time and perceived fatigue were used as comprehensive metrics to assess the efficiency of readers.Results: The average area under the receiver operating characteristic curve increased by 7.4% (95% CI, 4.5%-10%) with the concurrent assistance of the AI system (P <.001). On average, readers found 8% more cancers in the assisted reading condition. The ICC went from 0.6 (95% CI, 0.55-0.65) in the unassisted condition to 0.74 (95% CI, 0.70-0.78) for readings done with AI (P <.001). An overall decrease of 24% in reading time and a reduction in perceived fatigue was also found.Conclusion: The incorporation of this AI system, capable of handling multiple image type, prior mammograms, and multiple outputs, improved the diagnostic proficiency of radiologists in identifying breast cancer while also reducing the time required for combined interpretive and noninterpretive tasks.","PeriodicalId":43134,"journal":{"name":"Journal of Breast Imaging","volume":" ","pages":"155-164"},"PeriodicalIF":2.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142740890","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

Time to Enhancement Measured From Ultrafast Dynamic Contrast-Enhanced MRI for Improved Breast Lesion Diagnosis. 从超快动态增强MRI测量增强时间以改善乳腺病变诊断。

IF 2

Journal of Breast Imaging Pub Date : 2025-03-18 DOI: 10.1093/jbi/wbae089

Yun An Chen, Anum S Kazerouni, Matthew D Phelps, Daniel S Hippe, Inyoung Youn, Janie M Lee, Savannah C Partridge, Habib Rahbar

{"title":"Time to Enhancement Measured From Ultrafast Dynamic Contrast-Enhanced MRI for Improved Breast Lesion Diagnosis.","authors":"Yun An Chen, Anum S Kazerouni, Matthew D Phelps, Daniel S Hippe, Inyoung Youn, Janie M Lee, Savannah C Partridge, Habib Rahbar","doi":"10.1093/jbi/wbae089","DOIUrl":"10.1093/jbi/wbae089","url":null,"abstract":"Objective: Breast MRI affords high sensitivity with intermediate specificity for cancer detection. Ultrafast dynamic contrast-enhanced (DCE) MRI assesses early contrast inflow with potential to supplement or replace conventional DCE-MRI kinetic features. We sought to determine whether radiologist's evaluation of ultrafast DCE-MRI can increase specificity of a clinical MRI protocol.Methods: In this IRB-approved, HIPAA-compliant study, breast MRIs from March 2019 to August 2020 with a BI-RADS category 3, 4, or 5 lesion were identified. Ultrafast DCE-MRI was acquired during the first 40 seconds after contrast injection and before conventional DCE-MRI postcontrast acquisitions in the clinical breast MRI protocol. Three radiologists masked to outcomes retrospectively determined lesion time to enhancement (TTE) on ultrafast DCE-MRI. Interreader agreement, differences between benign and malignant lesion TTE, and TTE diagnostic performance were evaluated.Results: Ninety-five lesions (20 malignant, 75 benign) were included. Interreader agreement in TTE was moderate to substantial for both ultrafast source images and subtraction maximum intensity projections (overall κ = 0.63). Time to enhancement was greater across benign lesions compared with malignancies (P <.05), and all lesions demonstrating no enhancement during the ultrafast series were benign. With a threshold TTE ≥40 seconds, ultrafast DCE-MRI yielded an average 40% specificity (95% CI, 30%-48%) and 92% sensitivity (95% CI, 81%-100%), yielding a potential reduction in 31% (95% CI, 23%-39%) of benign follow-ups based on conventional DCE-MRI.Conclusion: Ultrafast imaging can be added to conventional DCE-MRI to increase diagnostic accuracy while adding minimal scan time. Future work to standardize evaluation criteria may improve interreader agreement and allow for more robust ultrafast DCE-MRI assessment.","PeriodicalId":43134,"journal":{"name":"Journal of Breast Imaging","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143657977","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

Improving Wellness Through Reading Room Design and Flexible Scheduling Options. 通过阅览室设计和灵活的时间安排来改善健康。

IF 2

Journal of Breast Imaging Pub Date : 2025-03-18 DOI: 10.1093/jbi/wbae094

Hamad Muhammad, Millie Puglia, Stephanie Colvin, Stefanie Zalasin, Ceren Yalniz, Kathryn W Zamora, Stefanie Woodard

引用次数: 0

Performance of Abbreviated Breast MRI in High-Risk Patients in a Tertiary Care Academic Medical Center. 一家三级医疗学术医学中心对高危患者进行简略乳腺 MRI 检查的效果。

IF 2

Journal of Breast Imaging Pub Date : 2025-03-18 DOI: 10.1093/jbi/wbae071

Tamara Zaza, Kapil Chandora, Ceren Yalniz, Kathryn Watts Zamora, Stefanie Zalasin, Yufeng Li, Stefanie Woodard

{"title":"Performance of Abbreviated Breast MRI in High-Risk Patients in a Tertiary Care Academic Medical Center.","authors":"Tamara Zaza, Kapil Chandora, Ceren Yalniz, Kathryn Watts Zamora, Stefanie Zalasin, Yufeng Li, Stefanie Woodard","doi":"10.1093/jbi/wbae071","DOIUrl":"10.1093/jbi/wbae071","url":null,"abstract":"Introduction: The development of abbreviated breast MRI (AB-MRI) protocols reduce scan times. This paper reports the performance of AB-MRI at a tertiary care public academic medical center in comparison with established literature.Methods: This HIPAA-compliant IRB-approved retrospective study reviewed 413 AB-MRI screenings in high-risk patients from June 2020 to March 2023. Data were collected from 3 databases (MagView, Cerner PowerChart, and Prism Primordial). Demographics and overall BI-RADS assessment were recorded. For all positive (BI-RADS 0, 3, 4, 5) examinations, manual review of each case was performed. Performance metrics (sensitivity, specificity, cancer detection rate [CDR], recall rate, positive predictive value [PPV] 3 and negative predictive value [NPV]) were calculated. PubMed and Google Scholar were used to review similar AB-MRI studies to compare performance metrics.Results: There were 413 AB-MRI examinations from 413 unique patients. The majority of cases were audit-negative BI-RADS 1 or 2 (83.8%, 346/413). There were 67 (16.2%, 67/413) audit-positive cases with 3.6% (15/413) BI-RADS 3, 10.9% (45/413) BI-RADS 4, 0.7% (3/413) BI-RADS 5, and 1.0% (4/413) BI-RADS 0. Performance metrics showed a sensitivity of 100.0% (95% CI, 63.1%-100.0%) and a specificity of 85.7% (95% CI, 81.9%-88.9%). The PPV3 was 14.3% (95% CI, 5.1%-23.5%), and the NPV was 100.0% (95% CI, 99.0%-100.0%). The CDR was 19.4 per 1000 screenings. The results are comparable to prior literature and benchmark data.Conclusion: This study demonstrates high sensitivity (100.0%) and NPV (100.0%) of AB-MRI with comparable specificity (85.7%) and CDR (19.4/1000) to the literature, adding support to the use of AB-MRI. Further research is needed to optimize AB-MRI protocols.","PeriodicalId":43134,"journal":{"name":"Journal of Breast Imaging","volume":" ","pages":"177-186"},"PeriodicalIF":2.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142629794","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

Supplemental Screening With MRI in Women With Dense Breasts: The European Perspective. 致密性乳房女性的MRI辅助筛查：欧洲视角

IF 2

Journal of Breast Imaging Pub Date : 2025-03-18 DOI: 10.1093/jbi/wbae091

Fleur Kilburn-Toppin, Iris Allajbeu, Nuala Healy, Fiona J Gilbert

{"title":"Supplemental Screening With MRI in Women With Dense Breasts: The European Perspective.","authors":"Fleur Kilburn-Toppin, Iris Allajbeu, Nuala Healy, Fiona J Gilbert","doi":"10.1093/jbi/wbae091","DOIUrl":"10.1093/jbi/wbae091","url":null,"abstract":"Breast cancer is the most prevalent cancer in women in Europe, and while all European countries have some form of screening for breast cancer, disparities in organization and implementation exist. Breast density is a well-established risk factor for breast cancer; however, most countries in Europe do not have recommendations in place for notification of breast density or additional supplementary imaging for women with dense breasts. Various supplemental screening modalities have been investigated in Europe, and when comparing modalities, MRI has been shown to be superior in cancer detection rate and in detecting small invasive disease that may impact long-term survival, as demonstrated in the Dense Tissue and Early Breast Neoplasm Screening (DENSE) trial in the Netherlands. Based on convincing evidence, the European Society of Breast Imaging issued recommendations that women with category D density undergo breast MRI from ages 50 to 70 years at least every 4 years and preferably every 2 to 3 years. However, currently no countries in Europe routinely offer women with BI-RADS category D density breasts MRI as supplemental imaging. The reasons for lack of implementation of MRI screening are multifactorial. Concerns regarding increased recalls have been cited, as have cost and lack of resources. However, studies have demonstrated breast MRI in women with BI-RADS category D density breasts to be cost-effective compared with the current breast cancer screening standard of biannual mammography. Furthermore, abbreviated MRI protocols could facilitate more widespread use of affordable MRI screening. Women's perception on breast density notification and supplemental imaging is key to successful implementation.","PeriodicalId":43134,"journal":{"name":"Journal of Breast Imaging","volume":" ","pages":"131-140"},"PeriodicalIF":2.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143013751","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. 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

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

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