Observer variability of Breast Imaging Reporting and Data System (BI-RADS) for breast ultrasound

This chapter, devoted to the Breast Imaging Reporting and Data System (BI-RADS), describes the standardized language applied to findings in mammography, breast ultrasound, and breast MRI. BI-RADS terms most frequently used are described, and most are illustrated by figures. In addition, the rules for a facility and radiologist audit are described, with definitions of true positive (TP), false positive (FP), true negative (TN), and false negative (FN) findings. Sensitivity (Se), specificity (Sp), positive predictive values 1, 2, and 3 (PPV1, 2, 3), and cancer detection rate are defined. An example of an audit is provided to clarify the use of these metrics.

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Observer Variability of the Breast Imaging Reporting and Data System (BI-RADS) Lexicon for Mammography

Breast Care ◽

10.1159/000272277 ◽

2010 ◽

Vol 5 (1) ◽

pp. 3-3 ◽

Cited By ~ 8

Author(s):

Zehra H. Adibelli ◽

Ruken Ergenc ◽

Ozgur Oztekin ◽

Suheyla Ecevit ◽

Gokhan Unal ◽

...

Keyword(s):

Breast Imaging ◽

Data System ◽

Observer Variability

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Comparison of inter- and intra-observer variability of breast density assessments using the fourth and fifth editions of Breast Imaging Reporting and Data System

European Journal of Radiology Open ◽

10.1016/j.ejro.2018.04.002 ◽

2018 ◽

Vol 5 ◽

pp. 67-72 ◽

Cited By ~ 7

Author(s):

Afsaneh Alikhassi ◽

Hamed Esmaili Gourabi ◽

Masoud Baikpour

Keyword(s):

Breast Density ◽

Breast Imaging ◽

Data System ◽

Observer Variability

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Incorporating the Breast Imaging Reporting and Data System Lexicon with a Fully Convolutional Network for Malignancy Detection on Breast Ultrasound

Diagnostics ◽

10.3390/diagnostics12010066 ◽

2021 ◽

Vol 12 (1) ◽

pp. 66

Author(s):

Yung-Hsien Hsieh ◽

Fang-Rong Hsu ◽

Seng-Tong Dai ◽

Hsin-Ya Huang ◽

Dar-Ren Chen ◽

...

Keyword(s):

Deep Learning ◽

Breast Imaging ◽

Area Under The Curve ◽

Semantic Segmentation ◽

Breast Ultrasound ◽

Data System ◽

Ultrasound Images ◽

Convolutional Network ◽

Learning Network ◽

Deep Learning Network

In this study, we applied semantic segmentation using a fully convolutional deep learning network to identify characteristics of the Breast Imaging Reporting and Data System (BI-RADS) lexicon from breast ultrasound images to facilitate clinical malignancy tumor classification. Among 378 images (204 benign and 174 malignant images) from 189 patients (102 benign breast tumor patients and 87 malignant patients), we identified seven malignant characteristics related to the BI-RADS lexicon in breast ultrasound. The mean accuracy and mean IU of the semantic segmentation were 32.82% and 28.88, respectively. The weighted intersection over union was 85.35%, and the area under the curve was 89.47%, showing better performance than similar semantic segmentation networks, SegNet and U-Net, in the same dataset. Our results suggest that the utilization of a deep learning network in combination with the BI-RADS lexicon can be an important supplemental tool when using ultrasound to diagnose breast malignancy.

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