Bayesian Probability of Malignancy With BI‐RADS Sonographic Features

Bouzghar, Ghizlane; Levenback, Benjamin J.; Sultan, Laith R.; Venkatesh, Santosh S.; Cwanger, Alyssa; Conant, Emily F.; Sehgal, Chandra M.

doi:10.7863/ultra.33.4.641

Cited by 12 publications

(7 citation statements)

References 17 publications

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“…Other previous studies have shown that the US features that most likely predict a malignant diagnosis are irregular shapes, nonparallel orientations, and no circumscribed margin (22)(23)(24)(25). Our results were consistent with these findings, suggesting that other US features, such as internal echo, posterior features, calcification, and vascularity, cannot be used as significant predictors for malignancy (24,25). The US is a useful imaging modality to evaluate axillary nodes using morphological criteria, such as cortical thickening, hilar effacement, and nonhilar cortical blood flow.…”

Section: Discussionmentioning

confidence: 97%

“…Benndorf et al reported that breast cancer's family history was an independent risk factor (odds ratio: 5.53) in the setting of an MG BI-RADS 3 assessment (21). Other previous studies have shown that the US features that most likely predict a malignant diagnosis are irregular shapes, nonparallel orientations, and no circumscribed margin (22)(23)(24)(25). Our results were consistent with these findings, suggesting that other US features, such as internal echo, posterior features, calcification, and vascularity, cannot be used as significant predictors for malignancy (24,25).…”

Section: Discussionmentioning

confidence: 99%

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A new nomogram for predicting the malignant diagnosis of Breast Imaging Reporting and Data System (BI-RADS) ultrasonography category 4A lesions in women with dense breast tissue in the diagnostic setting

Yang¹,

Hu²,

Shen³

et al. 2021

Quant Imaging Med Surg

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Background: Biopsy has been recommended for Breast Imaging Reporting and Data System (BI-RADS) category 4 lesions. However, the malignancy rate of category 4A lesions is very low (2-10%). Therefore, most biopsies of category 4A lesions are benign, and the results will generally cause additional health care costs and patient anxiety. Methods: A prediction model was developed based on an analysis of 418 BI-RADS ultrasonography (US) category 4A patients at Sun Yat-sen Memorial Hospital. Univariate and multivariate logistic regression analyses were applied to identify significant variables for inclusion in the final nomogram. The predictive accuracy and discriminative ability were evaluated using the concordance index (C-index) and calibration curves. An independent cohort of 97 patients from the Second Affiliated Hospital of Guangzhou Medical University was used for external validation. Results: The independent risk factors from the multivariate analysis for the training cohort were family history of breast cancer (OR =4.588, P=0.004), US features [margin (OR =2.916, P=0.019), shape (irregular vs. oval, OR =2.474, P=0.044; round vs. oval, OR =1.935, P=0.276), parallel orientation vs. not parallel (OR =2.204, P=0.040)], low suspicious lymph nodes (OR =7.664, P=0.019), and suspicious calcifications on mammography (MG) (OR =6.736, P=0.001). The C-index was good in the training [0.813, 95% confidence interval (95% CI), 0.733 to 0.893] and validation cohorts (0.765, 95% CI, 0.584 to 0.946). The calibration curves showed optimal agreement between the nomogram prediction and actual observations for the probability of malignancy. Also, the cutoff score was set to 100 for discriminating high and low risk. The model performed well in discerning different risk groups.Conclusions: We developed a well-discriminated and calibrated nomogram to predict the malignancy of BI-RADS US category 4A lesions in dense breast tissue, which may help clinicians identify patients at lower or higher risk.

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Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

A new nomogram for predicting the malignant diagnosis of Breast Imaging Reporting and Data System (BI-RADS) ultrasonography category 4A lesions in women with dense breast tissue in the diagnostic setting

Yang¹,

Hu²,

Shen³

et al. 2021

Quant Imaging Med Surg

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“…There continues to be a need for further innovations to improve confidence and reliability of breast imaging. In this context, several studies have proposed the use of computer algorithms and machine learning methods to improve the diagnostic value of breast ultrasound [ 2 ]-[ 7 ]. These computer based systems can serve as a second reader to decrease false positive rates of breast images [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

“…These computer based systems can serve as a second reader to decrease false positive rates of breast images [ 2 ]. In our earlier study, we introduced an approach that combines individual sonographic features quantitatively by machine learning to determine the probability of malignancy of solid breast masses [ 7 ]. The results show that the Bayesian method of weighting provides a systematic approach for combining ultrasound BI-RADS features yielding a high level of diagnostic performance, with an A z of approximately 0.884.…”

Section: Introductionmentioning

confidence: 99%

“…The diagnostic performances of the probability estimates of three observations were compared to determine diagnostic variability. Since the predictive values of the sonographic features are influenced by the age of the patients [ 7 ], we also evaluated the diagnostic performance of the sonographic features in conjunction with the patient age.…”

Section: Introductionmentioning

confidence: 99%

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Observer Variability in BI-RADS Ultrasound Features and Its Influence on Computer-Aided Diagnosis of Breast Masses

Sultan

Bouzghar

Levenback

et al. 2015

ABCR

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Objective: Computer classification of sonographic BI-RADS features can aid differentiation of the malignant and benign masses. However, the variability in the diagnosis due to the differences in the observed features between the observations is not known. The goal of this study is to measure the variation in sonographic features between multiple observations and determine the effect of features variation on computer-aided diagnosis of the breast masses. Materials and Methods: Ultrasound images of biopsy proven solid breast masses were analyzed in three independent observations for BI-RADS sonographic features. The BI-RADS features from each observation were used with Bayes classifier to determine probability of malignancy. The observer agreement in the sonographic features was measured by kappa coefficient and the difference in the diagnostic performances between observations was determined by the area under the ROC curve, Az, and interclass correlation coefficient. Results: While some features were repeatedly observed, κ = 0.95, other showed a significant variation, κ = 0.16. For all features, combined intra-observer agreement was substantial, κ = 0.77. The agreement, however, decreased steadily to 0.66 and 0.56 as time between the observations increased from 1 to 2 and 3 months, respectively. Despite the variation in features between observations the probabilities of malignancy estimates from Bayes classifier were robust and consistently yielded same level of diagnostic performance, Az was 0.772-0.817 for sonographic features alone and 0.828-0.849 for sonographic features and age combined. The difference in the performance, ΔA z , between the observations for the two groups was small (0.003-0.044) and was not statistically significant (p < 0.05). Interclass correlation coefficient for the observations was 0.822 (CI: 0.787-0.853) for BI-RADS sonographic features alone and for those combined with age was 0.833 (CI: 0.800-0.862). Conclusion: Despite the differences in the BI-RADS sonographic features between different observations, the diagnostic per-* Corresponding author. L. R. Sultan et al. 2 formance of computer-aided analysis for differentiating breast masses did not change. Through continual retraining, the computer-aided analysis provides consistent diagnostic performance independent of the variations in the observed sonographic features.

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Machine learning for diagnostic ultrasound of triple-negative breast cancer

Sultan

Tian

et al. 2018

Breast Cancer Res Treat

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Bayesian Probability of Malignancy With BI‐RADS Sonographic Features

Abstract: A naïve Bayes model provides a systematic approach for combining sonographic features and other patient characteristics for assessing the probability of malignancy to differentiate malignant and benign breast masses.

Cited by 12 publications

References 17 publications

A new nomogram for predicting the malignant diagnosis of Breast Imaging Reporting and Data System (BI-RADS) ultrasonography category 4A lesions in women with dense breast tissue in the diagnostic setting

A new nomogram for predicting the malignant diagnosis of Breast Imaging Reporting and Data System (BI-RADS) ultrasonography category 4A lesions in women with dense breast tissue in the diagnostic setting

Observer Variability in BI-RADS Ultrasound Features and Its Influence on Computer-Aided Diagnosis of Breast Masses

Machine learning for diagnostic ultrasound of triple-negative breast cancer

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