Improving Breast Ultrasound Interpretation in Uganda Using a Condensed Breast Imaging Reporting and Data System

2016 ◽  
Vol 23 (10) ◽  
pp. 1271-1277 ◽  
Author(s):  
John R. Scheel ◽  
Sue Peacock ◽  
Jackson Orem ◽  
Samuel Bugeza ◽  
Zeridah Muyinda ◽  
...  
Keyword(s):  
Breast Imaging ◽  
Breast Ultrasound ◽  
Data System

scholarly journals Breast ultrasound diagnostic performance and outcomes for mass lesions using Breast Imaging Reporting and Data System category 0 mammogram

Clinics ◽  
2011 ◽  
Vol 66 (3) ◽  
pp. 443-448 ◽  
Author(s):  
Paulo Almazy Zanello ◽  
Andre Felipe Cica Robim ◽  
Tatiane Mendes Gonçalves de Oliveira ◽  
Jorge Elias Junior ◽  
Jurandyr Moreira de Andrade ◽  
...  
Keyword(s):  
Breast Imaging ◽  
Diagnostic Performance ◽  
Breast Ultrasound ◽  
Data System ◽  
Mass Lesions

Breast Imaging Reporting and Data System (BI-RADS)

2018 ◽  
Author(s):  
Carl D’Orsi
Keyword(s):  
Cancer Detection ◽  
Breast Imaging ◽  
False Negative ◽  
Breast Mri ◽  
Breast Ultrasound ◽  
Cancer Detection Rate ◽  
Data System ◽  
True Positive ◽  
True Negative ◽  
Predictive Values

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.

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

Diagnostics ◽  
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.

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

2008 ◽  
Vol 65 (2) ◽  
pp. 293-298 ◽  
Author(s):  
Hye-Jeong Lee ◽  
Eun-Kyung Kim ◽  
Min Jung Kim ◽  
Ji Hyun Youk ◽  
Ji Young Lee ◽  
...  
Keyword(s):  
Breast Imaging ◽  
Breast Ultrasound ◽  
Data System ◽  
Observer Variability

scholarly journals Application of a second opinion ultrasound in Breast Imaging Reporting and Data System 4A cases: can immediate biopsy be avoided?

2021 ◽  
Vol 49 (6) ◽  
pp. 030006052110244
Author(s):  
Yantao Cai ◽  
Chenfang Zhu ◽  
Qianqian Chen ◽  
Feng Zhao ◽  
Shanyu Guo
Keyword(s):  
Minimally Invasive ◽  
Predictive Factors ◽  
Breast Imaging ◽  
Protective Factor ◽  
Multivariate Analyses ◽  
Data System ◽  
Second Opinion ◽  
Factors Associated ◽  
Peripheral Location ◽  
Biopsy Rate

Objective The probability of malignancy in women who are diagnosed with a Breast Imaging Reporting and Data System (BI-RADS) 4A score is low. Application of a second opinion ultrasound (SOUS), which is low in cost and minimally invasive, may lower the biopsy rate for patients who fall into this category. This study aimed to apply SOUS to patients with a BI-RADS score of 4A and predict the pathological results of a biopsy. Methods One hundred seventy-eight patients were analyzed. Univariate and multivariate analyses were performed to screen for predictive factors that are associated with malignancy. Categorical alteration of downgraded, unchanged, or upgraded was made after SOUS results. Changes in category were compared with biopsies to determine their predictive value of benignancy or malignancy. Results Independent factors associated with malignancy were age (>50 years), tumor size (≥20 mm), margin (not circumscribed), orientation (not parallel), and peripheral location, and an upgraded categorical alteration from SOUS. Downgraded categorical alterations were associated with benignancy. Conclusions In BI-RADS 4A cases, a biopsy is recommended when independent factors are associated with malignancy. A downgraded result from an SOUS examination is a protective factor, supporting the likelihood of benignancy in these patients.

Evidencias y controversias de la categoría 3 del Breast Imaging Reporting and Data System® en enfermedad mamaria

2013 ◽  
Vol 26 (1) ◽  
pp. 18-24
Author(s):  
María Martínez Gálvez ◽  
José Aguilar Jiménez ◽  
Jose Ignacio Gil Izquierdo
Keyword(s):  
Breast Imaging ◽  
Data System
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