BI-RADS Category 5 Assessments at Diagnostic Breast Imaging:Outcomes Analysis Based on Lesion Descriptors

Yao, Melissa Min-Szu; Joe, Bonnie N.; Sickles, Edward A.; Lee, Cindy

doi:10.1016/j.acra.2018.07.018

Cited by 8 publications

(4 citation statements)

References 10 publications

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“…Such rules stated that BI-RADS 5 should be assumed if at least 4 BI-RADS descriptors were found to indicate malignancy. 7 No such rule has been evaluated so far to differentiate between whether a patient should be classified as BI-RADS 3 (undergo follow-up imaging) or as BI-RADS 4 (undergo biopsy). We suggest a new clinical decision rule for these patients: assume malignancy if the patient is aged 50 years or older, or if either nonparallel orientation of mass, noncircumscribed margin, posterior shadowing, or calcification is present.…”

Section: Discussionmentioning

confidence: 99%

A New Practical Decision Rule to Better Differentiate BI‐RADS 3 or 4 Breast Masses on Breast Ultrasound

Pfob

Barr

Duda

et al. 2021

J of Ultrasound Medicine

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Objectives-The BI-RADS classification provides a standardized way to describe ultrasound findings in breast cancer diagnostics. However, there is little information regarding which BI-RADS descriptors are most strongly associated with malignancy, to better distinguish BI-RADS 3 (follow-up imaging) and 4 (diagnostic biopsy) breast masses.Methods-Patients were recruited as part of an international, multicenter trial (NCT02638935). The trial enrolled 1294 women (6 excluded) categorized as BI-RADS 3 or 4 upon routine B-mode ultrasound examination. Ultrasound images were evaluated by three expert physicians according to BI-RADS. All patients underwent histopathological confirmation (reference standard). We performed univariate and multivariate analyses (chi-square test, logistic regression, and Krippendorff's alpha).Results-Histopathologic evaluation showed malignancy in 368 of 1288 masses (28.6%). Upon performing multivariate analysis, the following descriptors were significantly associated with malignancy (P < .05): age ≥50 years (OR 8.99), non-circumscribed indistinct (OR 4.05) and microlobulated margin (OR 2.95), nonparallel orientation (OR 2.69), and calcification (OR 2.64). A clinical decision rule informed by these results demonstrated a 97% sensitivity and missed fewer cancers compared to three physician experts (range of sensitivity 79-95%) and a previous decision rule (sensitivity 59%). Specificity was 44% versus 22-83%, respectively. The inter-reader reliability of the BI-RADS descriptors and of the final BI-RADS score was fair-moderate.Conclusions-A patient should undergo a diagnostic biopsy (BI-RADS 4) instead of follow-up imaging (BI-RADS 3) if the patient is 50 years or older or exhibits at least one of the following features: calcification, nonparallel orientation of mass, non-circumscribed margin, or posterior shadowing.

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Section: Discussionmentioning

confidence: 99%

A New Practical Decision Rule to Better Differentiate BI‐RADS 3 or 4 Breast Masses on Breast Ultrasound

Pfob

Barr

Duda

et al. 2021

J of Ultrasound Medicine

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“…Malignancy descriptors are more useful for predicting malignancy when combined than when used alone. 20–22 Some rules have been suggested to make consistent assessments according to sonographic features, 19 , 23 and they have excellent diagnostic performance and variability. Therefore, the standardization of BI-RADS lexicons seems necessary because it can not only reduce operator dependency and enhance the consistency of image interpretation but also improve diagnostic accuracy of breast cancer.…”

Section: Discussionmentioning

confidence: 99%

A National Chinese Survey on Ultrasound Feature Interpretation and Risk Assessment of Breast Masses Under ACR BI-RADS

Wen¹,

Liu²,

Wang³

et al. 2021

CMAR

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Purpose Through this nationwide survey on ACR BI-RADS including ultrasound images of 10 selected breast lesions, we aimed to learn about consistency in feature interpretation and assessment categories and to identify factors that might contribute to inconsistencies, thereby promoting the application of BI-RADS in China. Materials and Methods The survey was delivered through a self-developed website about blinded image interpretation and was released to the public through online platforms and social media. A total of 10 representative lesions were selected by an experienced radiologist to gather information about the general practice of BI-RADS lexicons and categories. The Kappa statistic, the chi-squared test, and descriptive statistics were used for data analysis. Results Nine hundred ultrasound workers completed the questionnaire, coming from all provinces and major cities in China. They had different positions, grades of work organization, and seniority. The interrater agreement of BI-RADS features was fair to substantial (kappa value: 0.37–0.66). For BI-RADS categories, the highest agreement was observed in the typical benign group (average constituent rate = 74.78%), and generally lower agreement was observed in the typical malignant (average constituent rate = 36.03%) and suspicious groups (average constituent rate = 39.02%). Conclusion We found inconsistencies in BI-RADS applications, providing direction for image feature research using big data. Therefore, we call for more efforts to improve the consistency of BI-RADS application and provide an evidence-based basis for identifying benign and malignant lesions by sonographic features.

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“…Breast cancer remains a significant global health concern, being a predominant cancer among women and posing substantial mortality and morbidity [1]. The essence of mammography lies in its ability to offer detailed images of the breast tissue, utilizing low-dose ionizing radiation to pinpoint abnormalities and irregularities potentially indicative of cancerous cells [2]. The vital role of mammography is underscored by its capacity for early detection, which is crucial in identifying breast cancer at a stage where it is more treatable and the chances for recovery are heightened [3].…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Quality and Authenticity of Synthetic Mammogram Images for Improved Breast Cancer Detection

Shah,

Khan,

Abrar

et al. 2024

IEEE Access

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Breast cancer is widespread worldwide and can be cured if diagnosed early.Mammography is an irreplaceable and critical technique in modern medicine, serving as a foundation for breast cancer detection. In medical imaging, the reliability of synthetic mammogram images is produced by Deep Convolutional Generative Adversarial Networks (DCGANs). The human validation for assessing the quality of synthetic images for examining and calculating the perceptual variations between synthetic images and real-world counterparts is a difficult task. Thus, this research focused on improving the quality and authenticity of synthetic mammogram images. For this, we explored and identified a new research gap because the radiologists consistently expressed much higher confidence levels in real mammogram images in their assessment process. This research highlights the key difference between synthetic and real mammograms by defining mean scores. The defined mean identifies a sizable gap, with real mammographic images receiving an average score of 0.73 and a synthetic score of 0.31. we performed a statistical analysis, which yielded a T-statistic of -6.35, a p-value less than 0.001, and a 95% confidence interval ranging from -0.50 to -0.28. These results have a wide range of ramifications. It emphasizes the urgent need for further generative model improvement, improving the legitimacy and caliber of synthetic mammogram images. Our research highlights how crucial it is to incorporate synthetic images into clinical practice with caution and thought. The Ethical considerations must encompass the potential consequences of relying on synthetic data in medical decision-making, alongside concerns related to diagnostic accuracy and patient safety. In future research, we work on improving generative models, leveraging more extensive and more diverse datasets. Comprehensive validation studies, encompassing a broader spectrum of radiologists and datasets, are pivotal to validate and generalize our findings. This study sets the stage for a deeper understanding of the validity of synthetic mammogram images.

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BI-RADS Category 5 Assessments at Diagnostic Breast Imaging:Outcomes Analysis Based on Lesion Descriptors

Cited by 8 publications

References 10 publications

A New Practical Decision Rule to Better Differentiate BI‐RADS 3 or 4 Breast Masses on Breast Ultrasound

A New Practical Decision Rule to Better Differentiate BI‐RADS 3 or 4 Breast Masses on Breast Ultrasound

A National Chinese Survey on Ultrasound Feature Interpretation and Risk Assessment of Breast Masses Under ACR BI-RADS

Enhancing the Quality and Authenticity of Synthetic Mammogram Images for Improved Breast Cancer Detection

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