BI-RADS for Sonography: Positive and Negative Predictive Values of Sonographic Features

Hong, Andrea S.; Rosen, Eric L.; Soo, Mary Scott; Baker, Jay A.

doi:10.2214/ajr.184.4.01841260

Cited by 313 publications

(251 citation statements)

References 11 publications

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“…A variety of image features based on shape, orientation, margin, lesion boundary, echogenicity, posterior acoustic features, calcifications, and associated findings have been used. Taken together, these qualitative descriptors have been shown to improve the specificity of ultrasonographic findings [28].…”

Section: Discussionmentioning

confidence: 94%

“…For determination of the final assessment, a breast US specialist usually attempts to identify a typical benign feature or suspicious finding. A spiculated margin, irregular shape, and non-parallel orientation are known to have a high predictive value for malignancy and a circumscribed margin, oval shape, and parallel orientation are highly predictive of a benign lesion [28]. If there are any suspicious findings, a biopsy is recommended.…”

Section: Discussionmentioning

confidence: 99%

“…The standardized BI-RADS sonographic lexicon is known to be helpful in distinguishing benign from malignant solid masses [28]. A large number of structured reporting software programs have recently been developed.…”

Section: Discussionmentioning

confidence: 99%

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A Comparison of Logistic Regression Analysis and an Artificial Neural Network Using the BI-RADS Lexicon for Ultrasonography in Conjunction with Introbserver Variability

et al. 2012

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To determine which Breast Imaging Reporting and Data System (BI-RADS) descriptors for ultrasound are predictors for breast cancer using logistic regression (LR) analysis in conjunction with interobserver variability between breast radiologists, and to compare the performance of artificial neural network (ANN) and LR models in differentiation of benign and malignant breast masses. Five breast radiologists retrospectively reviewed 140 breast masses and described each lesion using BI-RADS lexicon and categorized final assessments. Interobserver agreements between the observers were measured by kappa statistics. The radiologists' responses for BI-RADS were pooled. The data were divided randomly into train (n070) and test sets (n0 70). Using train set, optimal independent variables were determined by using LR analysis with forward stepwise selection. The LR and ANN models were constructed with the optimal independent variables and the biopsy results as dependent variable. Performances of the models and radiologists were evaluated on the test set using receiver-operating characteristic (ROC) analysis. Among BI-RADS descriptors, margin and boundary were determined as the predictors according to stepwise LR showing moderate interobserver agreement. Area under the ROC curves (AUC) for both of LR and ANN were 0.87 (95% CI, 0.77-0.94). AUCs for the five radiologists ranged 0.79-0.91. There was no significant difference in AUC values among the LR, ANN, and radiologists (p>0.05). Margin and boundary were found as statistically significant predictors with good interobserver agreement. Use of the LR and ANN showed similar performance to that of the radiologists for differentiation of benign and malignant breast masses.

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Section: Discussionmentioning

confidence: 94%

Section: Discussionmentioning

confidence: 99%

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A Comparison of Logistic Regression Analysis and an Artificial Neural Network Using the BI-RADS Lexicon for Ultrasonography in Conjunction with Introbserver Variability

et al. 2012

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“…Infiltrating ductal carcinoma and its variants too are known to present clinically ascystic masses 7,8 . Sometimes, color or power Dopplersonography can show vascularity in such cases, whereas purely cysticlesions do not show any vascularity.…”

Section: Resultsmentioning

confidence: 99%

“…Sometimes, color or power Dopplersonography can show vascularity in such cases, whereas purely cysticlesions do not show any vascularity. Appearance of the margin of the lesion has been reported to be of great value asmalignancies with cyst like appearancesomewhere have an indistinct margin or local wall thickeningin addition to some internal vascularity 7,8 .…”

Section: Resultsmentioning

confidence: 99%

Meme Kistlerinin Patolojik Panoraması

Sankaye¹,

Kachewar²

2014

Cukurova Medical Journal

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Purpose: Cystic breast lesions are a common finding in young as well as elderly females. Although, mostly benign; they can at times be malignant too. Timely diagnosis is possible with help of Fine needle aspiration cytology (FNAC). This study was carried out with the aim of studying the panorama of various cystic breast lesions on FNAC in our setup. Materials and Methods: This was a four year prospective study carried out from May 2010 to January 2014. Cystic nature of breast mass was confirmed by palpation and by sonomammography. FNAC was then performed and the smears were stained with MGG and Papanicolaou stain. Results: Out of the 72 cases that were diagnosed to be cystic breast lesions clinically or on sonomammography, 64 were found to be benign and 08 were found to be malignant on FNAC. Retrospective imaging correlation of the 08 cystic cases revealed that they were of complex cystic nature and had either thick septae( 03), solid areas (04) or dense contents (01) within. This internal nature of cystic lesions that were found to be malignant on FNAC was not identifiable by clinical palpation alone. None of the lesions diagnosed as simple cystic lesion on sonomammography was found to be malignant on FNAC. Conclusions: FNAC is a satisfactory technique for the primary categorization of palpable cystic breast lumps into benign and malignant categories. Cystic breast lesions are more commonly benign than malignant. Palpation alone cannot identify the simple or complex nature of any palpable cystic lesion and hence sonomammography and ultrasound guided FNAC are indicated in complex cystic lesions of breast.

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Enzymatic Nanolithography of Polyaniline Nanopatterns by Using Peroxidase‐Modified Atomic Force Microscopy Tips

Liu

Pedrosa

Wang

2009

Chemistry A European J

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BI-RADS for Sonography: Positive and Negative Predictive Values of Sonographic Features

Abstract: Descriptors from the new sonographic BI-RADS lexicon can be useful in differentiating benign from malignant solid masses.

Cited by 313 publications

References 11 publications

A Comparison of Logistic Regression Analysis and an Artificial Neural Network Using the BI-RADS Lexicon for Ultrasonography in Conjunction with Introbserver Variability

A Comparison of Logistic Regression Analysis and an Artificial Neural Network Using the BI-RADS Lexicon for Ultrasonography in Conjunction with Introbserver Variability

Meme Kistlerinin Patolojik Panoraması

Enzymatic Nanolithography of Polyaniline Nanopatterns by Using Peroxidase‐Modified Atomic Force Microscopy Tips

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