MRI in the Assessment of BI-RADS® 4 lesions-Reference-Cited by-同舟云学术

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MRI in the Assessment of BI-RADS® 4 lesions

Published:2017-10 Issue:5 Volume:26 Page:191-199
ISSN:0899-3459
Container-title:Topics in Magnetic Resonance Imaging
language:en
Short-container-title:

Author:

Leithner Doris,Wengert Georg,Helbich Thomas,Morris Elizabeth,Pinker Katja

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Radiology, Nuclear Medicine and imaging

Reference64 articles.

1. Magnetic resonance imaging of the breast: recommendations from the EUSOMA working group;Sardanelli;Eur J Cancer,2010

2. Breast MRI: EUSOBI recommendations for women's information;Mann;Eur Radiol,2015

3. High resolution MRI of the breast at 3 T: which BI-RADS(R) descriptors are most strongly associated with the diagnosis of breast cancer?;Pinker-Domenig;Eur Radiol,2012

4. Hallmarks of cancer: the next generation;Hanahan;Cell,2011

5. Contrast-enhanced magnetic resonance imaging of the breast;Helbich;Eur J Radiol,2000

Cited by 11 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. Combination of ultrafast dynamic contrast-enhanced MRI-based radiomics and artificial neural network in assessing BI-RADS 4 breast lesions: Potential to avoid unnecessary biopsies;Frontiers in Oncology;2023-02-01

2. Evaluation of the differentiation of benign and malignant breast lesions using synthetic relaxometry and the Kaiser score;Frontiers in Oncology;2022-10-11

3. Development and validation of a deep learning model for breast lesion segmentation and characterization in multiparametric MRI;Frontiers in Oncology;2022-08-11

4. Combined diagnosis of multiparametric MRI-based deep learning models facilitates differentiating triple-negative breast cancer from fibroadenoma magnetic resonance BI-RADS 4 lesions;Journal of Cancer Research and Clinical Oncology;2022-06-30

5. XGboost Prediction Model Based on 3.0T Diffusion Kurtosis Imaging Improves the Diagnostic Accuracy of MRI BiRADS 4 Masses;Frontiers in Oncology;2022-03-17

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