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• Primary Medicine Forum •
Study on diagnostic efficacy of magnetic resonance imaging for BI-RADS category 4 lesions
WANG Fangfang  PENG Qin  XU Bingren  JIN Jun  TANG Xiaoli 

Cite this article as: Wang FF, Peng Q, Xu BR, et al. Study on diagnostic efficacy of magnetic resonance imaging for BI-RADS category 4 lesions[J]. Chin J Magn Reson Imaging, 2022, 13(8): 88-91. DOI:10.12015/issn.1674-8034.2022.08.017.


[Keywords] breast；magnetic resonance imagine；Breast Imaging Reporting and Data System；positive predictive value；diagnostic efficiency

WANG Fangfang   PENG Qin   XU Bingren   JIN Jun   TANG Xiaoli*  

Department of Radiology, Shenzhen Qianhai Shekou Free Trade Zone Hospital, Shenzhen 518067, China

Tang XL, E-mail: 303175614@qq.com

Conflicts of interest   None.

ACKNOWLEDGMENTS Health Science and Technology Project in Nanshan District of Shenzhen (No. 2020106).
Received  2021-05-10
Accepted  2022-08-10
DOI: 10.12015/issn.1674-8034.2022.08.017
Cite this article as: Wang FF, Peng Q, Xu BR, et al. Study on diagnostic efficacy of magnetic resonance imaging for BI-RADS category 4 lesions[J]. Chin J Magn Reson Imaging, 2022, 13(8): 88-91. DOI:10.12015/issn.1674-8034.2022.08.017.

[1]
Elezaby M, Li G, Bhargavan-Chatfield M, et al. ACR BI-RADS assessment category 4 subdivisions in diagnostic mammography: utilization and outcomes in the national mammography database[J]. Radiology, 2018, 287(2): 416-422. DOI: 10.1148/radiol.2017170770.
[2]
Liu BG, Cao MR, Zhang FJ, et al. Positive predictive value and subcategory for breast cancer in BI-RADS-MRI category 4 lesions[J]. J Clin Radiol, 2012, 31(6): 804-808. DOI: 10.13437/j.cnki.jcr.2012.06.024.
[3]
Istomin A, Masarwah A, Okuma H, et al. A multiparametric classification system for lesions detected by breast magnetic resonance imaging[J/OL]. Eur J Radiol, 2020, 132 [2022-03-01]. https://linkinghub.elsevier.com/retrieve/pii/S0720-048X(20)30511-8. DOI: 10.1016/j.ejrad.2020.109322.
[4]
de Almeida JR, Gomes AB, Barros TP, et al. Predictive performance of BI-RADS magnetic resonance imaging descriptors in the context of suspicious (category 4) findings[J]. Radiol Bras, 2016, 49(3): 137-143. DOI: 10.1590/0100-3984.2015.0021.
[5]
Mahoney MC, Gatsonis C, Hanna L, et al. Positive predictive value of BI-RADS MR imaging[J]. Radiology, 2012, 264(1): 51-58. DOI: 10.1148/radiol.12110619.
[6]
Strigel RM, Burnside ES, Elezaby M, et al. Utility of BI-RADS assessment category 4 subdivisions for screening breast MRI[J]. AJR Am J Roentgenol, 2017, 208(6): 1392-1399. DOI: 10.2214/AJR.16.16730.
[7]
Strobel K, Schrading S, Hansen NL, et al. Assessment of BI-RADS category 4 lesions detected with screening mammography and screening US: utility of MR imaging[J]. Radiology, 2015, 274(2): 343-351. DOI: 10.1148/radiol.14140645.
[8]
Avendano D, Marino MA, Onishi N, et al. Can follow-up be avoided for probably benign US masses with No enhancement on MRI?[J]. Eur Radiol, 2021, 31(2): 975-982. DOI: 10.1007/s00330-020-07216-7.
[9]
Honda M, Kataoka M, Kawaguchi K, et al. Subcategory classifications of Breast Imaging and Data System (BI-RADS) category 4 lesions on MRI[J]. Jpn J Radiol, 2021, 39(1): 56-65. DOI: 10.1007/s11604-020-01029-w.
[10]
Eghtedari M, Chong A, Rakow-Penner R, et al. Current status and future of BI-RADS in multimodality imaging, from the AJR special series on radiology reporting and data systems[J]. AJR Am J Roentgenol, 2021, 216(4): 860-873. DOI: 10.2214/AJR.20.24894.
[11]
Flowers CI, O'Donoghue C, Moore D, et al. Reducing false-positive biopsies: a pilot study to reduce benign biopsy rates for BI-RADS 4A/B assessments through testing risk stratification and new thresholds for intervention[J]. Breast Cancer Res Treat, 2013, 139(3): 769-777. DOI: 10.1007/s10549-013-2576-0.
[12]
Bennani-Baiti B, Baltzer PA. MR imaging for diagnosis of malignancy in mammographic microcalcifications: a systematic review and Meta-analysis[J]. Radiology, 2017, 283(3): 692-701. DOI: 10.1148/radiol.2016161106.
[13]
Li JY, Zheng H, Cai WG, et al. Subclassification of BI-RADS 4 magnetic resonance lesions: a systematic review and Meta-analysis[J]. J Comput Assist Tomogr, 2020, 44(6): 914-920. DOI: 10.1097/RCT.0000000000001108.
[14]
Chikarmane SA, Tai R, Meyer JE, et al. Prevalence and predictive value of BI-RADS 3, 4, and 5 lesions detected on breast MRI: correlation with study indication[J]. Acad Radiol, 2017, 24(4): 435-441. DOI: 10.1016/j.acra.2016.11.008.
[15]
Maltez de Almeida JR, Gomes AB, Barros TP, et al. Subcategorization of suspicious breast lesions (BI-RADS category 4) according to MRI criteria: role of dynamic contrast-enhanced and diffusion-weighted imaging[J]. AJR Am J Roentgenol, 2015, 205(1): 222-231. DOI: 10.2214/AJR.14.13834.
[16]
Eun NL, Son EJ, Gweon HM, et al. The value of breast MRI for BI-RADS category 4B mammographic microcalcification: based on the 5th edition of BI-RADS[J]. Clin Radiol, 2018, 73(8): 750-755. DOI: 10.1016/j.crad.2018.04.014.
[17]
Ahn HS, Kim SM, Kim MS, et al. Application of magnetic resonance computer-aided diagnosis for preoperatively determining invasive disease in ultrasonography-guided core needle biopsy-proven ductal carcinoma in situ[J/OL]. Medicine, 2020, 99(31) [2022-03-01]. https://journals.lww.com/md-journal/Fulltext/2020/07310/Application_of_magnetic_resonance_computer_aided.34.aspx. DOI: 10.1097/MD.0000000000021257.
[18]
Ha SM, Cha JH, Shin HJ, et al. Mammography, US, and MRI to assess outcomes of invasive breast cancer with extensive intraductal component: a matched cohort study[J]. Radiology, 2019, 292(2): 299-308. DOI: 10.1148/radiol.2019182762.
[19]
Kim HR, Jung HK, Ko KH, et al. Mammography, US, and MRI for preoperative prediction of extensive intraductal component of invasive breast cancer: interobserver variability and performances[J]. Clin Breast Cancer, 2016, 16(4): 305-311. DOI: 10.1016/j.clbc.2016.02.005.
[20]
Mann RM, Cho N, Moy L. Breast MRI: state of the art[J]. Radiology, 2019, 292(3): 520-536. DOI: 10.1148/radiol.2019182947.
[21]
Milos RI, Pipan F, Kalovidouri A, et al. The Kaiser score reliably excludes malignancy in benign contrast-enhancing lesions classified as BI-RADS 4 on breast MRI high-risk screening exams[J]. Eur Radiol, 2020, 30(11): 6052-6061. DOI: 10.1007/s00330-020-06945-z.
[22]
Pötsch N, Dietzel M, Kapetas P, et al. An A.I. classifier derived from 4D radiomics of dynamic contrast-enhanced breast MRI data: potential to avoid unnecessary breast biopsies[J]. Eur Radiol, 2021, 31(8): 5866-5876. DOI: 10.1007/s00330-021-07787-z.

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