Bayesian Probability of Malignancy With BI‐RADS Sonographic Features. (1st April 2014)

• Record Type: Journal Article
• Authors: Bouzghar, Ghizlane; Levenback, Benjamin J.; Sultan, Laith R.; Venkatesh, Santosh S.; Cwanger, Alyssa; Conant, Emily F.; Sehgal, Chandra M.
• Is Part Of: Journal of ultrasound in medicine. Volume 33:Number 4(2014)
• Page Start: 641
• Page End: 648
• Publication Date: 2014-04-01
• Subjects: Bayes probability of malignancy -- breast cancer imaging -- Breast Imaging Reporting and Data System (BI-RADS) -- negative predictive value -- positive predictive value; Ultrasonics in medicine -- Periodicals; Ultrasonics; Ultrasonography; Ultrasonics in medicine; Electronic journals; Periodicals; Periodicals; 616.07543
• DOI: 10.7863/ultra.33.4.641
• Languages: English
• ISSNs: 0278-4297

Abstract

Abstract : Objectives: The purpose of this study was to develop a quantitative approach for combining individual American College of Radiology Breast Imaging Reporting and Data System (BI‐RADS) sonographic features of breast masses for assessing the overall probability of malignancy. Methods: Sonograms of solid breast masses were analyzed by 2 observers blinded to patient age, mammographic features, and lesion pathologic findings. BI‐RADS sonographic features were determined by using American College of Radiology criteria. A naïve Bayes model was used to determine the probability of malignancy of all the sonographic features together and with age and BI‐RADS mammographic features. The diagnostic performance for various combinations was evaluated by using the area under the receiver operating curve (Az ). Results: Sonographic features had high positive and negative predictive values. The Az values for BI‐RADS sonographic features for the 2 observers ranged from 0.772 to 0.884, which increased to 0.866 to 0.924 when used with patient age and BI‐RADS mammographic features. The benefit of adding age and mammographic information was more marked for the observer with lower initial diagnostic performance. Age‐specific analysis showed that diagnostic performance varied with age, with higher performance for patients aged 45 years and younger and patients older than 60 years compared to those aged 46 to 60 years. In 85% of cases, the diagnosis of the observers matched. When the consensus between the observers was used for diagnostic decisions, a high level of diagnostic performance (Az, 0.954) was achieved. Conclusions: 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.