Literature DB >> 28961568

MRI in the Assessment of BI-RADS® 4 lesions.

Doris Leithner1, Georg Wengert, Thomas Helbich, Elizabeth Morris, Katja Pinker.   

Abstract

The American College of Radiology (ACR) Breast Imaging-Reporting and Data System (BI-RADS) lexicon, which is used ubiquitously to standardize reporting of breast magnetic resonance imaging (MRI), provides 7 BI-RADS assessment categories to indicate the level of suspicion of malignancy and guide further management. A BI-RADS category 4 assessment is assigned when an imaging abnormality does not fulfill the typical criteria for malignancy, but is suspicious enough to warrant a recommendation for biopsy. The BI-RADS category 4 assessment covers a wide range of probability of malignancy, from >2 to <95%. MRI is an essential noninvasive technique in breast imaging and the role of MRI in the assessment of ACR BI-RADS 4 lesions is manifold. In lesions classified as suspicious on imaging with mammography, digital breast tomosynthesis, and sonography, MRI can aid in the noninvasive differentiation of benign and malignant lesions and obviate unnecessary breast biopsies. When the suspicion of cancer is confirmed with MRI, concurrent staging of disease for treatment planning can be accomplished. This article will provide a comprehensive overview of the role of breast MRI in the assessment of ACR BI-RADS 4 lesions. In addition, we will discuss strategies to decrease false positives and avoid false negative results when reporting MRI of the breast.

Entities:  

Mesh:

Year:  2017        PMID: 28961568     DOI: 10.1097/RMR.0000000000000138

Source DB:  PubMed          Journal:  Top Magn Reson Imaging        ISSN: 0899-3459


  7 in total

1.  Combined diagnosis of multiparametric MRI-based deep learning models facilitates differentiating triple-negative breast cancer from fibroadenoma magnetic resonance BI-RADS 4 lesions.

Authors:  Hao-Lin Yin; Yu Jiang; Zihan Xu; Hui-Hui Jia; Guang-Wu Lin
Journal:  J Cancer Res Clin Oncol       Date:  2022-06-30       Impact factor: 4.553

2.  Extraosseous osteoblastoma: A rare cause of breast mass in a prepubertal girl.

Authors:  Sabine Danzinger; Leo Kager; Maria Bernathova; Susanna Lang; Werner Haslik; Christian F Singer
Journal:  Clin Case Rep       Date:  2021-03-31

3.  A Comparative Assessment of MR BI-RADS 4 Breast Lesions With Kaiser Score and Apparent Diffusion Coefficient Value.

Authors:  Lingsong Meng; Xin Zhao; Lin Lu; Qingna Xing; Kaiyu Wang; Yafei Guo; Honglei Shang; Yan Chen; Mengyue Huang; Yongbing Sun; Xiaoan Zhang
Journal:  Front Oncol       Date:  2021-12-02       Impact factor: 6.244

4.  Value of breast MRI omics features and clinical characteristics in Breast Imaging Reporting and Data System (BI-RADS) category 4 breast lesions: an analysis of radiomics-based diagnosis.

Authors:  Dongxue Qin; Yiping Zhao; Qian Cui; Liang Sun; Yu Zhang; Zimu Zhao; Shuo Li; Yajie Liu; Hongwei Ge
Journal:  Ann Transl Med       Date:  2021-11

5.  XGboost Prediction Model Based on 3.0T Diffusion Kurtosis Imaging Improves the Diagnostic Accuracy of MRI BiRADS 4 Masses.

Authors:  Wan Tang; Han Zhou; Tianhong Quan; Xiaoyan Chen; Huanian Zhang; Yan Lin; Renhua Wu
Journal:  Front Oncol       Date:  2022-03-17       Impact factor: 6.244

6.  Development and validation of a deep learning model for breast lesion segmentation and characterization in multiparametric MRI.

Authors:  Jingjin Zhu; Jiahui Geng; Wei Shan; Boya Zhang; Huaqing Shen; Xiaohan Dong; Mei Liu; Xiru Li; Liuquan Cheng
Journal:  Front Oncol       Date:  2022-08-11       Impact factor: 5.738

7.  Application of MRI Radiomics-Based Machine Learning Model to Improve Contralateral BI-RADS 4 Lesion Assessment.

Authors:  Wen Hao; Jing Gong; Shengping Wang; Hui Zhu; Bin Zhao; Weijun Peng
Journal:  Front Oncol       Date:  2020-10-29       Impact factor: 6.244

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.