Use of breast imaging-reporting and data system (BI-RADS) ultrasound classification in pediatric and adolescent patients overestimates likelihood of malignancy

Davis, John R.; Liang, Juliana; Roh, Albert T.; Kittrell, Laurel; Petterson, Matthew; Winton, Lisa M.; Connell, Mary J.; Viscusi, Rebecca K.; Komenaka, Ian K.; Jamshidi, Ramin

doi:10.1016/j.jpedsurg.2020.12.025

Cited by 6 publications

(3 citation statements)

References 21 publications

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“…Image-guided biopsy has become a major method in evaluation of the breast masses in the last two decades, and our findings demonstrate a high radiologic-histopathologic correlation rate in US-guided breast biopsy samples. As expected, several studies have shown that the use of classification systems such as BI-RADS increases the radiologichistopathologic agreement in adult patients, although its utility in pediatric cases has still not been proven (5,6,7,8). Image-guided breast biopsy is particularly important in the early diagnosis of breast cancer.…”

Section: Discussionmentioning

confidence: 86%

Ultrasound-guided Breast Biopsy: Evaluation of the Correlation Between Radiologic and Histopathologic Findings

Eren¹,

Akkurt²,

Ozmen³

et al. 2022

csmedj

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Section: Discussionmentioning

confidence: 86%

Ultrasound-guided Breast Biopsy: Evaluation of the Correlation Between Radiologic and Histopathologic Findings

Eren¹,

Akkurt²,

Ozmen³

et al. 2022

csmedj

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“…BI-RADS 0 refers to an incomplete diagnosis that needs an additional image for reclassification. Table 1 shows the categories for BI-RADS in detail [13,14]. The authors follow the Al-Amoudi Center of Excellence in Breast Cancer to scale cases from 0 to 6.…”

Section: Discussionmentioning

confidence: 99%

King Abdulaziz University Breast Cancer Mammogram Dataset (KAU-BCMD)

Alsolami

Shalash

Alsaggaf

et al. 2021

Data

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The current era is characterized by the rapidly increasing use of computer-aided diagnosis (CAD) systems in the medical field. These systems need a variety of datasets to help develop, evaluate, and compare their performances fairly. Physicians indicated that breast anatomy, especially dense ones, and the probability of breast cancer and tumor development, vary highly depending on race. Researchers reported that breast cancer risk factors are related to culture and society. Thus, there is a massive need for a local dataset representing breast cancer in our region to help develop and evaluate automatic breast cancer CAD systems. This paper presents a public mammogram dataset called King Abdulaziz University Breast Cancer Mammogram Dataset (KAU-BCMD) version 1. To our knowledge, KAU-BCMD is the first dataset in Saudi Arabia that deals with a large number of mammogram scans. The dataset was collected from the Sheikh Mohammed Hussein Al-Amoudi Center of Excellence in Breast Cancer at King Abdulaziz University. It contains 1416 cases. Each case has two views for both the right and left breasts, resulting in 5662 images based on the breast imaging reporting and data system. It also contains 205 ultrasound cases corresponding to a part of the mammogram cases, with 405 images as a total. The dataset was annotated and reviewed by three different radiologists. Our dataset is a promising dataset that contains different imaging modalities for breast cancer with different cancer grades for Saudi women.

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“…The American College of Radiology has developed a Breast Imaging Reporting and Data System (BI-RADS) according to the different growth patterns of breast tumors and ultrasound manifestations 5 . BI-RADS not only grades the benign and malignant degree of breast tumors, but also standardizes the description language of ultrasonic signs, which makes us diagnosis of breast more standardized 6 . However, as the conventional ultrasound BI-RADS grading is a visual judgment, it is greatly affected by the technical experience of ultrasound doctors.…”

Section: Introductionmentioning

confidence: 99%

A ultrasonic nomogram of quantitative parameters for diagnosing breast cancer

Wang,

Che

2023

Sci Rep

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This study aimed to develop a nomogram through the collection of quantitative ultrasound parameters to predict breast cancer. From March 2021 to September 2022, a total of 313 breast tumors were included with pathological results. Through collecting quantitative ultrasound parameters of breast tumors and multivariate regression analysis, a nomogram was developed. The diagnostic performances, calibration and clinical usefulness of the nomogram for predicting breast cancer were assessed. A total of 182 benign and 131 malignant breast tumors were included in this study. The nomogram indicated excellent predictive properties with an AUC of 0.934, sensitivity of 0.881, specificity of 0.848, PPV of 0.795 and NPV of 0.841. The calibration curve showed the predicted values are basically consistent with the actual observed values. The optimum cut-off for the nomogram was 0.310 for predicting cancer. The decision curve analysis results corroborated good clinical usefulness. The model including BI-RADS score, SWE and VI is potentially useful for predicting breast cancer.

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Use of breast imaging-reporting and data system (BI-RADS) ultrasound classification in pediatric and adolescent patients overestimates likelihood of malignancy

Cited by 6 publications

References 21 publications

Ultrasound-guided Breast Biopsy: Evaluation of the Correlation Between Radiologic and Histopathologic Findings

Ultrasound-guided Breast Biopsy: Evaluation of the Correlation Between Radiologic and Histopathologic Findings

King Abdulaziz University Breast Cancer Mammogram Dataset (KAU-BCMD)

A ultrasonic nomogram of quantitative parameters for diagnosing breast cancer

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