Literature DB >> 26102403

Subcategorization of Suspicious Breast Lesions (BI-RADS Category 4) According to MRI Criteria: Role of Dynamic Contrast-Enhanced and Diffusion-Weighted Imaging.

João Ricardo Maltez de Almeida1, André Boechat Gomes1, Thomas Pitangueira Barros2, Paulo Eduardo Fahel3, Mário de Seixas Rocha4.   

Abstract

OBJECTIVE: The purposes of this study were to investigate whether dynamic contrast-enhanced MRI is adequate for subcategorization of suspicious lesions (BI-RADS category 4) and to evaluate whether use of DWI improves diagnostic performance.
MATERIALS AND METHODS: The study group was composed of 103 suspicious lesions found in 83 subjects. Patient ages and lesion sizes were compiled, and two radiologists reanalyzed the images; subcategorized the findings as BI-RADS 4A, 4B, or 4C; and calculated apparent diffusion coefficient (ADC) values. The stratified variables were tested by univariate analysis and inserted in two multivariate predictive models, which were used to generate ROC curves and compare AUCs. Positive predictive values (PPVs) for each subcategory and ADC level were calculated, and interobserver agreement was tested.
RESULTS: Forty-four (42.7%) suspicious findings proved malignant. Except for age (p = 0.08), all stratified predictor variables were significant in univariate analyses (p < 0.01). Logistic regression models did not differ substantially after comparison of the ROC curves (p = 0.09), but the one including ADC values was slightly better: AUC of 0.89 (95% CI, 0.82-0.95) against AUC of 0.85 (95% CI, 0.78-0.93). PPV increased progressively in each BI-RADS 4 subcategory (4A, 0.15; 4B, 0.37; 4C, 0.84). ADC values of 1.10 × 10(-3) mm(2)/s or less had the second highest PPV (0.77). Interobserver agreement was substantial at a kappa value of 0.80 (95% CI, 0.70-0.90; p < 0.01).
CONCLUSION: Risk stratification of suspicious lesions (BI-RADS category 4) can be satisfactorily performed with DCE-MRI and slightly improved when DWI is introduced.

Entities:  

Keywords:  BI-RADS; DWI; breast MRI; breast cancer; suspicious breast lesions

Mesh:

Substances:

Year:  2015        PMID: 26102403     DOI: 10.2214/AJR.14.13834

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  11 in total

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Authors:  Irmak Durur-Subasi; Afak Durur-Karakaya; Adem Karaman; Mehmet Seker; Elif Demirci; Fatih Alper
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2.  Utility of BI-RADS Assessment Category 4 Subdivisions for Screening Breast MRI.

Authors:  Roberta M Strigel; Elizabeth S Burnside; Mai Elezaby; Amy M Fowler; Frederick Kelcz; Lonie R Salkowski; Wendy B DeMartini
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4.  Subdividing BI-RADS category 4 breast lesions observed on magnetic resonance imaging: Is it feasible?

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Journal:  Radiol Bras       Date:  2016 May-Jun

5.  A radiomic nomogram based on an apparent diffusion coefficient map for differential diagnosis of suspicious breast findings.

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Journal:  Chin J Cancer Res       Date:  2018-08       Impact factor: 5.087

6.  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

7.  Multiparameter MRI Model With DCE-MRI, DWI, and Synthetic MRI Improves the Diagnostic Performance of BI-RADS 4 Lesions.

Authors:  Shi Yun Sun; Yingying Ding; Zhuolin Li; Lisha Nie; Chengde Liao; Yifan Liu; Jia Zhang; Dongxue Zhang
Journal:  Front Oncol       Date:  2021-10-15       Impact factor: 6.244

8.  Diffusion-weighted imaging of suspicious (BI-RADS 4) breast lesions: stratification based on histopathology.

Authors:  João Ricardo Maltez de Almeida; André Boechat Gomes; Thomas Pitangueira Barros; Paulo Eduardo Fahel; Mario de Souza Rocha
Journal:  Radiol Bras       Date:  2017 May-Jun

9.  The Value of Lesion Size as an Adjunct to the BI-RADS-MRI 2013 Descriptors in the Diagnosis of Solitary Breast Masses.

Authors:  Makiko Kawai; Masako Kataoka; Shotaro Kanao; Mami Iima; Natsuko Onishi; Akane Ohashi; Rena Sakaguchi; Masakazu Toi; Kaori Togashi
Journal:  Magn Reson Med Sci       Date:  2017-12-07       Impact factor: 2.471

10.  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

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