Radiomics and deep learning methods in expanding the use of screening breast MRI
Author:
Publisher
Springer Science and Business Media LLC
Subject
Radiology, Nuclear Medicine and imaging,General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s00330-021-08056-9.pdf
Reference11 articles.
1. Mann RM, Cho N, Moy L (2019) Breast MRI: state of the art. Radiology 292:520–536
2. Lehman CD, Lee JM, Demartini WB et al (2016) Screening MRI in women with a personal history of breast cancer. J Natl Cancer Inst 108(3):djv349
3. Kuhl CK, Strobel K, Bieling H et al (2017) Supplemental Breast MR imaging screening of women with average risk of breast cancer. Radiology 283(2):361–370
4. Comstock CE, Gatsonis C, Newstead GM (2020) Comparison of abbreviated breast MRI vs digital breast tomosynthessi for breast cancer detection among women with dense breasts undergoing screening. JAMA 323(8):746–756
5. Bakker MF, de Lange SV, Pijnappel RM et al (2019) Supplemental MRI screening for women with extremely dense breast tissue. N Engl J Med 381:2091–2102
Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. A Novel Artificial Intelligence-Based Hybrid System to Improve Breast Cancer DetectionUsing DCE-MRI;Bulletin of the Polish Academy of Sciences Technical Sciences;2024-01-30
2. False-positive incidental lesions detected on contrast-enhanced breast MRI: clinical and imaging features;Breast Cancer Research and Treatment;2023-02-06
3. Improving breast cancer diagnostics with deep learning for MRI;Science Translational Medicine;2022-09-28
4. Cardiac magnetic resonance T1 mapping for evaluating myocardial fibrosis in patients with type 2 diabetes mellitus: correlation with left ventricular longitudinal diastolic dysfunction;European Radiology;2022-05-14
5. Pretreatment DCE-MRI-Based Deep Learning Outperforms Radiomics Analysis in Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer;Frontiers in Oncology;2022-03-10
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3