Journal of Breast Imaging最新文献_第6页

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

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

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

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

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

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

Patient- vs Technologist-Controlled Mammography Compression: A Prospective Comparative Study of Patient Discomfort and Breast Compression Thickness. 患者与技术人员控制的乳腺 X 射线压迫：患者不适感与乳房压迫厚度的前瞻性比较研究。

IF 2

Journal of Breast Imaging Pub Date : 2025-01-25 DOI: 10.1093/jbi/wbae052

Joshua Gaudette, Sai Kilaru, Alexis Davenport, Sushil Hanumolu, David Pinkney, Sabala Mandava, Amy Williams, Xiaoqin Amy Tang

{"title":"Patient- vs Technologist-Controlled Mammography Compression: A Prospective Comparative Study of Patient Discomfort and Breast Compression Thickness.","authors":"Joshua Gaudette, Sai Kilaru, Alexis Davenport, Sushil Hanumolu, David Pinkney, Sabala Mandava, Amy Williams, Xiaoqin Amy Tang","doi":"10.1093/jbi/wbae052","DOIUrl":"10.1093/jbi/wbae052","url":null,"abstract":"Objective: We assess whether mammographic patient-assisted compression (PAC) has an impact on breast compression thickness and patient discomfort compared with technologist-assisted compression (TAC).Methods: A total of 382 female patients between ages 40 and 90 years undergoing screening mammography from February 2020 to June 2021 were recruited via informational pamphlet to participate in this IRB-approved study. Patients without prior baseline mammograms were excluded. The participating patients were randomly assigned to the PAC or TAC study group. Pre- and postmammogram surveys assessed expected pain and experienced pain, respectively, using a 100-mm visual analogue scale and the State-Trait Anxiety Inventory. Breast compression thickness values from the most recent mammogram were compared with the patient's recent prior mammogram.Results: Between the 2 groups, there was no significant difference between the expected level of pain prior to the mammogram (P = .97). While both study groups reported a lower level of experienced pain than was expected, the difference was greater for the PAC group (P <.0001). Additionally, the PAC group reported significantly lower experienced pain during mammography compared with the TAC group (P = .014). The correlation of trait/state anxiety scores with pre- and postmammogram pain scores was weak among the groups. Lastly, the mean breast compression thickness values for standard screening mammographic views showed no significant difference in the PAC group when compared with the patient's prior mammogram.Conclusion: Involving patients in compression reduces their pain independent of the patient's state anxiety during mammography while having no effect on breast compression thickness. Implementing PAC could improve the mammography experience.","PeriodicalId":43134,"journal":{"name":"Journal of Breast Imaging","volume":" ","pages":"54-62"},"PeriodicalIF":2.0,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142141339","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