Computer Aided Classification System for Breast Ultrasound Based on Breast Imaging Reporting and Data System (BI-RADS)

2007 ◽  
Vol 33 (11) ◽  
pp. 1688-1698 ◽  
Author(s):  
Wei-Chih Shen ◽  
Ruey-Feng Chang ◽  
Woo Kyung Moon
Keyword(s):  
Classification System ◽  
Breast Imaging ◽  
Breast Ultrasound ◽  
Data System ◽  
Computer Aided

scholarly journals Histo-pathological correlation of BI-RADS 4 lesions on mammography with emphasis on microcalcification patterns.

2008 ◽  
Vol 12 (4) ◽  
pp. 84
Author(s):  
F Ismail ◽  
J Holl ◽  
Z Lockhat ◽  
H J Akande
Keyword(s):  
Retrospective Study ◽  
Physical Examination ◽  
Digital Mammography ◽  
Classification System ◽  
Breast Imaging ◽  
Data System ◽  
Architectural Distortion ◽  
Final Histology ◽  
Radiological Appearance ◽  
Study Population

A retrospective study of 20 patients with Breast Imaging Reporting and Data System (BI-RADS) 4 lesions was undertaken. These patients were classified as BI-RADS 4 lesions due to presence of a mass (clinical or on mammography), architectural distortion and microcalcifications (MC). In some patients, the pattern of MC was benign but there were other features that were suspicious of malignancy. A comparison was made with the histological diagnosis in order to compare the radiological appearance of benign and malignant microcalcification patterns with the final histology. The study design included retrospective analysis of patients with MC on digital mammography who underwent biopsy. An analysis of the histology was then undertaken. Other factors in the history and physical examination were also considered. Results showed that although the study was not statistically significant due to limited study population, interesting trends are determined in assessing calcification patterns using the Breast Imaging Reporting and Data System (BI-RADS) classification system, since some lesions that were thought to have benign calcification patterns were actually malignant and vice versa. Further study in this field is required.

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

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 Evaluation of the effect of computer aided diagnosis system on breast ultrasound for inexperienced radiologists in describing and determining breast lesions

2019 ◽  
Vol 21 (3) ◽  
pp. 239
Author(s):  
Jeongmin Lee ◽  
Sanghee Kim ◽  
Bong Joo Kang ◽  
Sung Hun Kim ◽  
Ga Eun Park
Keyword(s):  
Breast Imaging ◽  
Diagnostic Performance ◽  
Computer Aided Diagnosis ◽  
Breast Ultrasound ◽  
Breast Lesions ◽  
Cad System ◽  
Computer Aided ◽  
Kappa Value ◽  
Inexperienced Reader ◽  
Aided Diagnosis

Aim: To investigate the effect of a computer-aided diagnosis (CAD) system on breast ultrasound (US) for inexperienced radiologists in describing and determining breast lesions.Materials and methods: Between October 2015 to January 2017, 500 suspicious or probable benign lesions in 413 patients were reviewed. Five experienced readers retrospectively reviewed for each of 100 lesions according to the Breast Imaging Reporting and Data System (BI-RADS) lexicon and category, with CAD system (S-detectTM). The readers then made final decisions by combining CAD results to their US results. Using the nested experiment design, five inexperienced readers were asked to select the appropriate BI-RADS lexicons, categories, CAD results, and combination results for each of the 100 lesions, retrospectively. Diagnostic performance of experienced and inexperienced radiologists and CAD were assessed. For each case, agreements in the lexicons and categories were analyzed among the experienced reader, inexperienced reader and CAD.Results: Indicators of the diagnostic performance for breast malignancy of the experienced group (AUC=0.83, 95%CI [0.80, 0.86]) were similar or higher than those of CAD (AUC = 0.79, 95%CI[0.74, 0.83], p=0.101), except for specificity. Conversely, indicators of diagnostic performance of inexperienced group (AUC=0.65, 95%CI[0.58, 0.71]) did not differ from or were lower than those of CAD(AUC=0.73, 95%CI[0.67, 0.78], p=0.013). Also, the diagnostic performance of the inexperienced group after combination with the CAD result was significantly improved (0.71, 95% CI [0.65, 0.77], p=0.001), whereas that of the experienced group did not change after combination with the CAD result, except for specificity and positive predictive value (PPV). Kappa values for the agreement of the categorization between CAD and each radiologist group were increased after applying the CAD result to their result of general US. Especially, the increase of the Kappa value was higher in the inexperienced group than in the experienced group. Also, for all the lexicons, the Kappa values between the experienced group and CAD were higher than those between the inexperienced group and CAD.Conclusion: By using the CAD system for classification of breast lesions, diagnostic performance of the inexperienced radiologists for malignancy was significantly improved, and better agreement was observed in lexicons between the experienced group and CAD than between the inexperienced group and CAD. CAD may be beneficial and educational for the inexperienced group.

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