Utility of BI-RADS Assessment Category 4 Subdivisions for Screening Breast MRI

被引:43
|
作者
Strigel, Roberta M. [1 ,2 ,3 ]
Burnside, Elizabeth S. [1 ,3 ]
Elezaby, Mai [1 ]
Fowler, Amy M. [1 ,2 ,3 ]
Kelcz, Frederick [1 ]
Salkowski, Lonie R. [1 ]
DeMartini, Wendy B. [1 ]
机构
[1] Univ Wisconsin, Dept Radiol, 600 Highland Ave, Madison, WI 53792 USA
[2] Univ Wisconsin, Dept Med Phys, 1530 Med Sci Ctr, Madison, WI 53706 USA
[3] Univ Wisconsin, Carbone Canc Ctr, Madison, WI USA
基金
美国国家卫生研究院;
关键词
BI-RADS category 4; breast MRI; POSITIVE PREDICTIVE-VALUE; PERSONAL HISTORY; CANCER; MICROCALCIFICATION; MAMMOGRAPHY;
D O I
10.2214/AJR.16.16730
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
OBJECTIVE. BI-RADS for mammography and ultrasound subdivides category 4 assessments by likelihood of malignancy into categories 4A (> 2% to <= 10%), 4B (> 10% to <= 50%), and 4C (> 50% to < 95%). Category 4 is not subdivided for breast MRI because of a paucity of data. The purpose of the present study is to determine the utility of categories 4A, 4B, and 4C for MRI by calculating their positive predictive values (PPVs) and comparing them with BI-RADS-specified rates of malignancy for mammography and ultrasound. MATERIALS AND METHODS. All screening breast MRI examinations performed from July 1, 2010, through June 30, 2013, were included in this study. We identified in medical records prospectively assigned MRI BI-RADS categories, including category 4 subdivisions, which are used routinely in our practice. Benign versus malignant outcomes were determined by pathologic analysis, findings from 12 months or more clinical or imaging follow-up, or a combination of these methods. Distribution of BI-RADS categories and positive predictive value level 2 (PPV2; based on recommendation for tissue diagnosis) for categories 4 (including its subdivisions) and 5 were calculated. RESULTS. Of 860 screening breast MRI examinations performed for 566 women (mean age, 47 years), 82 with a BI-RADS category 4 assessment were identified. A total of 18 malignancies were found among 84 category 4 and 5 assessments, for an overall PPV2 of 21.4% (18/84). For category 4 subdivisions, PPV2s were as follows: for category 4A, 2.5% (1/40); for category 4B, 27.6% (8/29); for category 4C, 83.3% (5/6); and for category 4 (not otherwise specified), 28.6% (2/7). CONCLUSION. Category 4 subdivisions for MRI yielded malignancy rates within BI-RADS-specified ranges, supporting their use for benefits to patient care and more meaningful practice audits.
引用
收藏
页码:1392 / 1399
页数:8
相关论文
共 50 条
  • [31] Positive Predictive Values BI-RADS 4 Subcategories and Category 5 Breast Lesions
    Eradat, J.
    Bassett, L. W.
    AMERICAN JOURNAL OF ROENTGENOLOGY, 2007, 188 (05)
  • [32] Screening Breast MRI Outcomes in Routine Clinical Practice: Comparison to BI-RADS Benchmarks
    Strigel, Roberta M.
    Rollenhagen, Jennifer
    Burnside, Elizabeth S.
    Elezaby, Mai
    Fowler, Amy M.
    Kelcz, Frederick
    Salkowski, Lonie
    DeMartini, Wendy B.
    ACADEMIC RADIOLOGY, 2017, 24 (04) : 411 - 417
  • [33] Clinical impact of BI-RADS classification in Taiwanese breast cancer patients: BI-RADS 5 versus BI-RADS 0-4
    Kuo, Yao-Lung
    Cheng, Lili
    Chang, Tsai-Wang
    EUROPEAN JOURNAL OF RADIOLOGY, 2012, 81 (07) : 1504 - 1507
  • [34] Clinical utility of breast sonography in BI-RADS category 0, manifesting as dense breasts, especially in the detection of breast malignancies
    Ham, SY
    Chung, S
    Yang, I
    Kim, H
    Kim, J
    Kim, SJ
    BREAST CANCER RESEARCH AND TREATMENT, 2003, 82 : S22 - S22
  • [35] BI-RADS 3 Assessment on MRI: A Lesion-Based Review for Breast Radiologists
    Nguyen, Derek L.
    Myers, Kelly S.
    Oluyemi, Eniola
    Mullen, Lisa A.
    Panigrahi, Babita
    Rossi, Joanna
    Ambinder, Emily B.
    JOURNAL OF BREAST IMAGING, 2022, 4 (05) : 460 - 473
  • [36] Changes in the Utilization of the BI-RADS Category 3 Assessment in Recalled Patients Before and After the Implementation of Screening Digital Breast Tomosynthesis
    Stepanek, Tricia
    Constantinou, Niki
    Marshall, Holly
    Pham, Ramya
    Thompson, Cheryl
    Dubchuk, Christina
    Plecha, Donna
    ACADEMIC RADIOLOGY, 2019, 26 (11) : 1515 - 1525
  • [37] A collaborative multi-task learning method for BI-RADS category 4 breast lesion segmentation and classification of MRI images
    Sun, Liang
    Zhang, Yunling
    Liu, Tang
    Ge, Hongwei
    Tian, Juan
    Qi, Xin
    Sun, Jian
    Zhao, Yiping
    COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2023, 240
  • [38] Assessment and Management of Challenging BI-RADS Category 3 Mammographic Lesions
    Michaels, Aya Y.
    Birdwell, Robyn L.
    Chung, Chris SungWon
    Frost, Elisabeth P.
    Giess, Catherine S.
    RADIOGRAPHICS, 2016, 36 (05) : 1261 - 1272
  • [39] Characteristics and Outcomes of BI-RADS 3 Lesions on Breast MRI
    Panigrahi, Babita
    Harvey, Susan C.
    Mullen, Lisa A.
    Falomo, Fniola
    Di Carlo, Philip
    Lee, Bonmyong
    Myers, Kelly S.
    CLINICAL BREAST CANCER, 2019, 19 (01) : E152 - E159
  • [40] Value of the US BI-RADS final assessment following mastectomy: BI-RADS 4 and 5 lesions
    Gweon, Hye Mi
    Son, Eun Ju
    Youk, Ji Hyun
    Kim, Jeong-Ah
    Chung, Jin
    ACTA RADIOLOGICA, 2012, 53 (03) : 255 - 260