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Journal articles on the topic 'Breast Imaging and Reporting Data System'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Berg, Wendie A., Cristina Campassi, Patricia Langenberg, and Mary J. Sexton. "Breast Imaging Reporting and Data System." American Journal of Roentgenology 174, no. 6 (2000): 1769–77. http://dx.doi.org/10.2214/ajr.174.6.1741769.

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2

Om, Prakash Rathore, Solanki Dinesh, Choudhary Manish, Sharma Tarun, and P. Toshniwal H. "Ultrasonographic Breast Imaging-Reporting and Data System (BIRADS): Radiologic-Pathological Correlation." International Journal of Pharmaceutical and Clinical Research 15, no. 5 (2023): 229–35. https://doi.org/10.5281/zenodo.12552116.

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<strong>Background:</strong> Breast cancer is the leading cause of cancer deaths among women worldwide. Breast cancer clinically presents as breast lump. Biopsy/Fine needle aspiration cytology (FNAC) and breast ultrasound is pivotal in characterization of a breast lump as benign and malignant lesion. Despite advances in modern investigations, accurate diagnosis is challenge. Delay in diagnosis may leads to Morbidity and mortality. Early diagnosis of breast lump reducing health care costs and also morbidity and mortality of patients. BI-RADS (Breast Imaging-Reporting and Data System) are a

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3

Liberman, Laura, and Jennifer H. Menell. "Breast imaging reporting and data system (BI-RADS)." Radiologic Clinics of North America 40, no. 3 (2002): 409–30. http://dx.doi.org/10.1016/s0033-8389(01)00017-3.

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4

Tardivon, Anne A., Alexandra Athanasiou, Fabienne Thibault, and Carl El Khoury. "Breast imaging and reporting data system (BIRADS): Magnetic resonance imaging." European Journal of Radiology 61, no. 2 (2007): 212–15. http://dx.doi.org/10.1016/j.ejrad.2006.08.036.

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5

Evans, A. "Breast Imaging Reporting and Data Systems." Breast 3, no. 2 (1994): 132. http://dx.doi.org/10.1016/0960-9776(94)90019-1.

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6

Humayun, Saba, Muhammad Asif, Muhammad Tahir Khadim, Hafeez Ud Din, Madeeha Anwar, and Farhat Rashid. "Comparison of Breast Biopsy Pathology Reporting with Breast Imaging Reporting and Data System (Bi-Rads) Categories-An Institutional Study." Pakistan Armed Forces Medical Journal 72, SUPPL-2 (2022): S186–90. http://dx.doi.org/10.51253/pafmj.v72isuppl-2.3496.

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Objective: To determine concordance and discordance between radiological and pathological findings of palpable breast lesions. Study Design: Comparative cross-sectional study. Place and Duration of Study: Department of Histopathology, Armed Forces Institute of Pathology, Rawalpindi Pakistan, from Oct 2018 to Mar 2019. Methodology: Imaging and histopathologic reports of a total of 170 female patients with breast lumps were analyzed. Concordance and discordance rates were estimated by comparing histopathology and imaging findings. All the reports were divided into four categori

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7

Aziz, Suraya, Muhammad Afiq Mohamad, and Reena Rahayu Md Zin. "Histopathological Correlation of Breast Carcinoma with Breast Imaging-Reporting and Data System." Malaysian Journal of Medical Sciences 29, no. 4 (2022): 65–74. http://dx.doi.org/10.21315/mjms2022.29.4.7.

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Background: Breast cancer is one of the commonest malignancy cancer worldwide and the Breast Imaging-Reporting and Data System (BI-RADS) classification has been extensively utilised as an adjunct to histopathological examination for malignant breast diseases. This study aims to analyse the concordance between radiological and histopathological findings, demonstrate the high predictive value in the BI-RADS category and evaluate the impact of these findings on surgical intervention and treatment outcome. Methods: This is a single-centre retrospective study, analysing patients who underwent radio

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8

Sedgwick, Emily. "The Breast Ultrasound Lexicon: Breast Imaging Reporting and Data System (BI-RADS)." Seminars in Roentgenology 46, no. 4 (2011): 245–51. http://dx.doi.org/10.1053/j.ro.2011.04.001.

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9

Tardivon, Anne A., Alexandra Athanasiou, Fabienne Thibault, and Carl El Khoury. "Breast imaging and reporting data system (BIRADS) magnetic resonance imaging illustrated cases." European Journal of Radiology 61, no. 2 (2007): 216–23. http://dx.doi.org/10.1016/j.ejrad.2006.08.037.

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10

Lee, Karen A. "Breast Imaging Reporting and Data System Category 3 for Magnetic Resonance Imaging." Topics in Magnetic Resonance Imaging 23, no. 6 (2014): 337–44. http://dx.doi.org/10.1097/rmr.0000000000000037.

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11

Subramaniam, Prema, Rupa Renganathan, Parameswaran Prathiba Rajalakshmi, and Vinita Thakur. "Contrast Enhanced Mammography Descriptors using Breast Imaging and Reporting and Data System." Indographics 01, no. 02 (2022): 161–70. http://dx.doi.org/10.1055/s-0042-1760321.

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AbstractContrast-enhanced mammography (CEM) is an emerging breast imaging technique with a potential to develop into a one-stop imaging solution for several conditions which require imaging, especially its role in cancer detection and local staging when combined with ultrasound. In 2022, CEM lexicon was published as a supplement to the American College of Radiology Breast Imaging Reporting and Data System (ACR BIRADS) fifth edition. This pictorial review illustrates the findings that are seen on CEM using ACR BIRADS CEM lexicon to make the reader familiar with the terminologies. In our experie

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12

Antonio, Anna Liza M., and Catherine M. Crespi. "Predictors of interobserver agreement in breast imaging using the Breast Imaging Reporting and Data System." Breast Cancer Research and Treatment 120, no. 3 (2010): 539–46. http://dx.doi.org/10.1007/s10549-010-0770-x.

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13

Qiao, Mengyun, Yuzhou Hu, Yi Guo, Yuanyuan Wang, and Jinhua Yu. "Breast Tumor Classification Based on a Computerized Breast Imaging Reporting and Data System Feature System." Journal of Ultrasound in Medicine 37, no. 2 (2017): 403–15. http://dx.doi.org/10.1002/jum.14350.

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14

Choi, Ji Soo. "Breast Imaging Reporting and Data System (BI-RADS): Advantages and Limitations." Journal of the Korean Society of Radiology 84, no. 1 (2023): 3. http://dx.doi.org/10.3348/jksr.2022.0142.

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15

Arora, Jyoti, and Richa Gauba. "Unusual Breast Malignancies—Going A Step Ahead of Breast Imaging Reporting and Data System." Indographics 02, no. 01 (2023): 39–52. http://dx.doi.org/10.1055/s-0043-1761448.

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AbstractWith the advent of the Breast Imaging Reporting and Data System (BIRADS), the categorization of the entire gamut of breast lesions has been simplified. However, there are many unusual breast lesions, both benign and malignant, which cannot be categorized accurately using the BIRADS descriptors due to the overlap of features. Carcinomas such as medullary, papillary, and mucinous (colloid) types, may be difficult to recognize as malignant because they may have relatively benign-appearing morphologic features. Well-circumscribed, small, and hyperechoic lesions may not be benign in all cas

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16

Menezes, Gisela LG, Gonneke AO Winter-Warnars, Eva L. Koekenbier, Emma J. Groen, Helena M. Verkooijen, and Ruud M. Pijnappel. "Simplifying Breast Imaging Reporting and Data System classification of mammograms with pure suspicious calcifications." Journal of Medical Screening 25, no. 2 (2017): 82–87. http://dx.doi.org/10.1177/0969141317715281.

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Objectives To investigate the risk of malignancy following stereotactic breast biopsy of calcifications classified as Breast Imaging Reporting and Data System (BI-RADS) 3, 4, and 5. Methods The study included women with pure calcifications (not associated with masses or architectural distortions) who underwent stereotactic breast biopsy at the Dutch Cancer Institute between January 2011 and October 2013. Suspicious calcifications (Breast Imaging Reporting and Data System 3, 4, or 5) detected on mammography were biopsied. All lesions were assessed by breast radiologists and classified according

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17

Lee, Hye-Jeong, Eun-Kyung Kim, Min Jung Kim, et al. "Observer variability of Breast Imaging Reporting and Data System (BI-RADS) for breast ultrasound." European Journal of Radiology 65, no. 2 (2008): 293–98. http://dx.doi.org/10.1016/j.ejrad.2007.04.008.

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18

Dilhuydy, Marie Hélène. "Breast imaging reporting and data system (BI-RADS) or French “classification ACR”." European Journal of Radiology 61, no. 2 (2007): 187–91. http://dx.doi.org/10.1016/j.ejrad.2006.08.032.

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19

Shen, Wei-Chih, Ruey-Feng Chang, and Woo Kyung Moon. "Computer Aided Classification System for Breast Ultrasound Based on Breast Imaging Reporting and Data System (BI-RADS)." Ultrasound in Medicine & Biology 33, no. 11 (2007): 1688–98. http://dx.doi.org/10.1016/j.ultrasmedbio.2007.05.016.

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20

Gard, Charlotte C., Erin J. Aiello Bowles, Diana L. Miglioretti, Stephen H. Taplin, and Carolyn M. Rutter. "Misclassification of Breast Imaging Reporting and Data System (BI-RADS) Mammographic Density and Implications for Breast Density Reporting Legislation." Breast Journal 21, no. 5 (2015): 481–89. http://dx.doi.org/10.1111/tbj.12443.

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21

Nam, Sang Yu, Eun Young Ko, Boo-Kyung Han, Jung Hee Shin, Eun Sook Ko, and Soo Yeon Hahn. "Breast Imaging Reporting and Data System Category 3 Lesions Detected on Whole-Breast Screening Ultrasound." Journal of Breast Cancer 19, no. 3 (2016): 301. http://dx.doi.org/10.4048/jbc.2016.19.3.301.

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22

Diab, Khaled, MohamedF Zaidan, Sameh Samir, ObaidaA Derballa, MahmoudI Aboelnor, and HanyF Habashy. "Evaluation of core-needle biopsy in Breast Imaging Reporting and Data System 3 breast mass." Journal of The Arab Society for Medical Research 18, no. 1 (2023): 88. http://dx.doi.org/10.4103/jasmr.jasmr_32_22.

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23

Koning, Jeffrey L., Katherine P. Davenport, Patricia S. Poole, Peter G. Kruk, and Julia E. Grabowski. "Breast Imaging-Reporting and Data System (BIRADS) Classification in 51 Excised Palpable Pediatric Breast Masses." Journal of the American College of Surgeons 219, no. 3 (2014): S78—S79. http://dx.doi.org/10.1016/j.jamcollsurg.2014.07.186.

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24

Scheel, John R., Sue Peacock, Jackson Orem, et al. "Improving Breast Ultrasound Interpretation in Uganda Using a Condensed Breast Imaging Reporting and Data System." Academic Radiology 23, no. 10 (2016): 1271–77. http://dx.doi.org/10.1016/j.acra.2016.05.018.

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25

Li, Xiao-Long, Feng Lu, An-Qi Zhu, et al. "Multimodal Ultrasound Imaging in Breast Imaging-Reporting and Data System 4 Breast Lesions: A Prediction Model for Malignancy." Ultrasound in Medicine & Biology 46, no. 12 (2020): 3188–99. http://dx.doi.org/10.1016/j.ultrasmedbio.2020.08.003.

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26

Uysal, Emine, Ömer Faruk Topaloğlu, Ayşe Arı, Halil Özer, and Mustafa Koplay. "Can magnetic resonance imaging texture analysis change the breast imaging reporting and data system category of breast lesions?" Clinical Imaging 97 (May 2023): 44–49. http://dx.doi.org/10.1016/j.clinimag.2023.02.016.

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27

Godinho, Eduardo Rodrigues, and Hilton Augusto Koch. "Breast imaging reporting and data system (BI-RADS™): como tem sido utilizado?" Radiologia Brasileira 37, no. 6 (2004): 413–17. http://dx.doi.org/10.1590/s0100-39842004000600006.

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O Breast Imaging Reporting and Data System (BI-RADS™), do American College of hRadiology, foi concebido para padronizar o laudo mamográfico e reduzir os fatores de confusão na descrição e interpretação das imagens, além de facilitar o monitoramento do resultado final. OBJETIVO: Identificar a maneira como vem sendo utilizado o BI-RADS™, gerando informações que possam auxiliar o Colégio Brasileiro de Radiologia a desenvolver estratégias para aperfeiçoar o seu uso. MATERIAIS E MÉTODOS: Os dados foram coletados na cidade de Goiânia, GO. Foram solicitados os exames de mamografia anteriores a todas

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28

Abbasi, Mehrshad, AliReza Azarpeikan, Ramesh Omranipour, et al. "Application of breast scintigraphy for patients with suspicious (breast imaging-reporting and data system IV) breast lesions." Advanced Biomedical Research 12, no. 1 (2023): 86. http://dx.doi.org/10.4103/abr.abr_347_21.

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29

Prvulovic-Bunovic, Natasa, Milica Mastilovic, Maja Stankov, and Borislava Nikolin. "Breast imaging reporting and data system (BI RADS): Why don’t we use it correctly in the region?" Archive of Oncology, no. 00 (2024): 8. http://dx.doi.org/10.2298/aoo240606008p.

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Breast Imaging Reporting and Data System (BI RADS) is used for the standardization of radiological reports in breast imaging, created by the American college of radiology (ACR). Standardized reports allow breast lesion characterization, malignancy risk assessment, and facilitate communication between doctors. The system is made for everyday clinical practice with the aim of improving the quality of diagnostics and anticipate the comparisons of results with colleagues from other health institutions, while in Serbia to assist in communication with colleagues from the region. The BI RADS system i

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30

Kim, Min Jung, Dokyung Kim, WooHee Jung, and Ja Seung Koo. "Histological Analysis of Benign Breast Imaging Reporting and Data System Categories 4c and 5 Breast Lesions in Imaging Study." Yonsei Medical Journal 53, no. 6 (2012): 1203. http://dx.doi.org/10.3349/ymj.2012.53.6.1203.

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31

Yadav, Alka, Aparna Singh, Sonali Madaan, et al. "International Academy of Cytology Yokohama System for Reporting Breast Cytology and the ACR Breast Imaging Reporting and Data System (BIRADS): Are they Concordant?" Iranian Journal of Pathology 19, no. 4 (2024): 400–407. https://doi.org/10.30699/ijp.2024.2028955.3300.

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32

Grande-Barreto, Jonas, Gabriela C. Lopez-Armas, Jose Antonio Sanchez-Tiro, and Hayde Peregrina-Barreto. "A Short Breast Imaging Reporting and Data System-Based Description for Classification of Breast Mass Grade." Life 14, no. 12 (2024): 1634. https://doi.org/10.3390/life14121634.

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Identifying breast masses is relevant in early cancer detection. Automatic identification using computational methods helps assist medical experts with this task. Although high values have been reported in breast mass classification from digital mammograms, most results have focused on a general benign/malignant classification. According to the BI-RADS standard, masses are associated with cancer risk by grade depending on their specific shape, margin, and density characteristics. This work presents a methodology of testing several descriptors on the INbreast dataset, finding those better relat

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33

Birdwell, R. L. "Breast Imaging Reporting and Data System Lexicon for US: Interobserver Agreement for Assessment of Breast Masses." Yearbook of Diagnostic Radiology 2010 (January 2010): 57–58. http://dx.doi.org/10.1016/s0098-1672(10)79140-2.

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34

Abdullah, Nouf, Benoît Mesurolle, Mona El-Khoury, and Ellen Kao. "Breast Imaging Reporting and Data System Lexicon for US: Interobserver Agreement for Assessment of Breast Masses." Radiology 252, no. 3 (2009): 665–72. http://dx.doi.org/10.1148/radiol.2523080670.

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35

Koning, Jeffrey L., Katherine P. Davenport, Patricia S. Poole, Peter G. Kruk, and Julia E. Grabowski. "Breast Imaging-Reporting and Data System (BI-RADS) classification in 51 excised palpable pediatric breast masses." Journal of Pediatric Surgery 50, no. 10 (2015): 1746–50. http://dx.doi.org/10.1016/j.jpedsurg.2015.02.062.

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36

Jin, Zhan-Qiang, Xiu-Ru Li, Hong-Lian Zhou, et al. "Acoustic Radiation Force Impulse Elastography of Breast Imaging Reporting and Data System Category 4 Breast Lesions." Clinical Breast Cancer 12, no. 6 (2012): 420–27. http://dx.doi.org/10.1016/j.clbc.2012.07.007.

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37

Zhou, Peng, Chunchun Jin, Jianghao Lu, et al. "Modified Model for Diagnosing Breast Imaging Reporting and Data System Category 3 to 5 Breast Lesions." Journal of Ultrasound in Medicine 40, no. 1 (2020): 151–61. http://dx.doi.org/10.1002/jum.15385.

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38

Zahra, Syeda Beenish, Ayesha Isani Majeed, and Nouman Malik. "Positive Predictive Rate of Breast Imaging-Reporting and Data System (BI-RADS) Category 4 Lesions For Breast Cancer Taking Histopathology as Gold Standard." Pakistan Armed Forces Medical Journal 75, SUPPL-2 (2025): S202—S205. https://doi.org/10.51253/pafmj.v75isuppl-2.5857.

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Objective: To determine positive predictive rate of Breast imaging-reporting and data system (BI-RADS) category 4 and its sub-categories for breast cancer taking histopathology as gold standard. Study Design: Cross sectional validation study. Place and Duration of Study: Department of Radiology, Pakistan Institute of Medical Sciences, Islamabad Pakistan, from Sep to Nov 2020. Methodology: 60 female patients who were diagnosed as Breast imaging-reporting and data system (BI-RADS) category 4 lesions on mammography were included in the study and afterwards followed for biopsy report. Data was col

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39

Cho, Min Jeng, Jung-Hyun Yang, Yeong Beom Yu, et al. "Validity of breast-specific gamma imaging for Breast Imaging Reporting and Data System 4 lesions on mammography and/or ultrasound." Annals of Surgical Treatment and Research 90, no. 4 (2016): 194. http://dx.doi.org/10.4174/astr.2016.90.4.194.

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40

Wang, Yanfang, Xing Liao, Feng Xiao, Hanfei Zhang, Jianyu Li, and Meiyan Liao. "Magnetic Resonance Imaging Texture Analysis in Differentiating Benign and Malignant Breast Lesions of Breast Imaging Reporting and Data System 4." Journal of Computer Assisted Tomography 44, no. 1 (2020): 83–89. http://dx.doi.org/10.1097/rct.0000000000000969.

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41

Lévy, L., M. Suissa, J. Bokobsa, et al. "Présentation de la traduction française du BI-RADS® (Breast Imaging Reporting System and Data System)." Gynécologie Obstétrique & Fertilité 33, no. 5 (2005): 338–47. http://dx.doi.org/10.1016/j.gyobfe.2005.04.006.

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42

Ghunaim, Hadeel A. "Effect of patient age and breast parenchymal density on Breast Imaging-Reporting and Data System (BIRADS-4) Subcategorization." Pakistan Journal of Medical Sciences 40, no. 10 (2024): 2356–62. http://dx.doi.org/10.12669/pjms.40.10.9552.

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Background & Objectives: BI-RADS (Breast Imaging-Reporting and Data System) is a standard radiological risk assessment for breast lesions, including six categories, of which category four is the widest range of likelihood cancer risk (2% to 95%). This study aimed to evaluate the effect of patient age and ACR breast density on the positive predictive value (PPV) of BI-RADS 4 subcategorization (4A, 4B, and 4C). Methods: A retrospective study was conducted at King Fahed Hospital (KFH) between September 1, 2021, and June 30, 2022. PPV was calculated based on a histopathological report for all

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43

Baker, J. A., P. J. Kornguth, and C. E. Floyd. "Breast imaging reporting and data system standardized mammography lexicon: observer variability in lesion description." American Journal of Roentgenology 166, no. 4 (1996): 773–78. http://dx.doi.org/10.2214/ajr.166.4.8610547.

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44

Luo, Jun, Ji-Dong Chen, Qing Chen, et al. "Contrast-enhanced ultrasound improved performance of breast imaging reporting and data system evaluation of critical breast lesions." World Journal of Radiology 8, no. 6 (2016): 610. http://dx.doi.org/10.4329/wjr.v8.i6.610.

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45

M, Gülsün, Demirkazik FB, and Ariyūrek. "Evaluation of breast microcalcifications according to breast imaging reporting and data system criteria and Le Gal's classification." Clinical Imaging 28, no. 2 (2004): 153–54. http://dx.doi.org/10.1016/s0899-7071(03)00298-5.

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46

Gülsün, Meltem, Figen Başaran Demirkazık, and Macit Arıyürek. "Evaluation of breast microcalcifications according to breast imaging reporting and data system criteria and Le Gal's classification." European Journal of Radiology 47, no. 3 (2003): 227–31. http://dx.doi.org/10.1016/s0720-048x(02)00181-x.

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47

Kincaid, Jennifer A., Chifundo Kajombo, Tamiwe Tomoka, et al. "Correlation of Surgeon-Determined Breast Imaging Reporting and Data System Assessment and Breast Pathological Results in Malawi." Journal of the American College of Surgeons 229, no. 4 (2019): e134. http://dx.doi.org/10.1016/j.jamcollsurg.2019.08.1089.

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48

Scott, Anthony M., Madison G. Lashley, Nicholas B. Drury, and Paul S. Dale. "Comparison of Call-Back Rates between Digital Mammography and Digital Breast Tomosynthesis." American Surgeon 85, no. 8 (2019): 855–57. http://dx.doi.org/10.1177/000313481908500837.

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The effect of mammographic screening on the natural history and evolution of breast cancer treatment cannot be overstated; however, despite intensive and resource consuming screening, advanced breast cancer is still diagnosed frequently. The development of three-dimensional mammography or digital breast tomosynthesis (DBT) has already demonstrated greater sensitivity in the diagnosis of breast pathology and effectiveness in identifying early breast cancers. In addition to being a more sensitive screening tool, other studies indicate DBT has a lower call-back rate when compared with traditional

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49

Sahu, Indira, Suman Ruhela, and Laghuta Verma. "RADIOLOGICAL TESTS VERSUS PATHOLOGICAL DIAGNOSTICS: COMPLIMENTARY OR ANTAGONISTIC RELATIONSHIP- AN INSTITUTIONAL EXPERIENCE." International Journal of Advanced Research 9, no. 11 (2021): 1077–83. http://dx.doi.org/10.21474/ijar01/13843.

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Aims & Objective: To determine the yield, reliability and diagnostic accuracy of Breast Imaging Reporting and Data System in evaluation of breast lesions taking histopathology as gold standard. Material and Methods: This cross-sectional, analytical study was conducted at Pathology dept. Of SMSMC Jaipur from June 2019 to June 2020. The data of the core needle biopsies of breast lesions received at the Pathology department which had been also categorized on mammogram, according to the Breast Imaging Reporting and Data System (BI-RADS), were selected for the study. All clinical parameters, th

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50

Lu, Xin-Hua. "Diagnostic Values of Breast Imaging Reporting and Data System and ultrasound Elastography in Benign and Malignant Breast Tumor." Journal of Computational and Theoretical Nanoscience 13, no. 10 (2016): 6509–13. http://dx.doi.org/10.1166/jctn.2016.5594.

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Objective: To evaluate the diagnostic values of Breast Imaging Reporting and Data System (BI-RADS), ultrasound elastography (UE) and the combination in differentiating benign and malignant breast tumor. Methods: The BI-RADS and UE image features of 248 breast cancer patients (a total of 260 lesions) proved by surgery and pathology from February 2013 to March 2015 were retrospectively analyzed. With the pathologic results as the gold standard, the sensitivity, specificity, positive and negative predictive values, and accuracy were calculated for BI-RADS, UE and the combination. On the basis of

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