Variability and errors when applying the BIRADS mammography classification

Boyer, Bruno; Canale, Sandra; Arfi-Rouche, Julia; Monzani, Quentin; Khaled, Wassef; Balleyguier, Corinne

doi:10.1016/j.ejrad.2012.02.005

Cited by 26 publications

(19 citation statements)

References 7 publications

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“…Of interest, many benign and malignant focal asymmetries and masses initially assessed as probably benign had no US correlate, and the lack of an ultrasound correlate had no predictive value for malignancy. It is important to note that even if the ultrasound examination is normal, the image modality depicting the most suspicious‐looking feature should be used to make the final assessment .…”

Section: Discussionmentioning

confidence: 99%

Imaging and Histopathologic Features of BI-RADS 3 Lesions Upgraded during Imaging Surveillance

et al. 2016

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To evaluate imaging and histopathologic differences between screen-detected benign and malignant upgraded lesions initially assessed as BI-RADS 3 at diagnostic evaluation. An IRB approved retrospective review of the mammography data base from January 1, 2004 to December 31, 2008 identified 1,188 (1.07%) of 110,776 screening examinations assessed as BI-RADS 3 following diagnostic evaluation at our academic center (staffed by breast specialists) or our outpatient center (staffed by general radiologists), 1,017 with at least 24 months follow-up or biopsy. Sixty (5.9%) BI-RADS 3 lesions were upgraded to BI-RADS 4 or 5 during imaging surveillance (study population). Prospective reports, patient demographics, and clinical outcomes were abstracted from the longitudinal medical record. Mean patient age was 54.1 years (range 35-85). Lesions consisted of 7 masses, 12 focal asymmetries and 41 calcifications. Fifteen (25%) of 60 lesions upgraded from initial BI-RADS 3 assessment were malignant (1.47% of total; 15/1,017 BI-RADS 3 studies). Malignancy rates by upgraded lesion type showed no significant difference: Thirty-three (73.3%) of 45 benign upgraded lesions were calcifications compared to 8 (53.3%) of 15 malignant upgraded lesions (p = 0.202). Twelve (26.7%) of 45 benign upgraded lesions were masses or focal asymmetries, compared to 7 (46.7%) of 15 upgraded malignant lesions (p = 0.202). Six (85.7%) of 7 malignant upgraded masses/focal asymmetries had no US correlate at initial BI-RADS 3 assessment compared to 7 (58.3%) of 12 benign upgraded masses/focal asymmetries (p = 0.33). Breast-imaging specialists interpreted 21 studies, 3 (14.3%) malignant; general radiologists interpreted 39 studies, 12 (30.8%) malignant (p = 0.218). There was no significant difference in malignancy rate among different types of upgraded mammographic lesions, nor depending on subspecialty interpretation versus nonsubspecialist interpretation. Although calcifications made up a majority of upgraded lesions, most were benign, suggesting that decreased surveillance of calcifications may be appropriate.

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Section: Discussionmentioning

confidence: 99%

Imaging and Histopathologic Features of BI-RADS 3 Lesions Upgraded during Imaging Surveillance

et al. 2016

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“…In clinical practice, it is important to assess and quantify the improvement in the detection of a malignancy offered by these imaging modalities. From this point of view, our aim was to strengthen the BIRADS classification score incorporating two different scores: the standard BIRADS score that is based on well-established mammographic criteria 14,15 and the CESM score that is based on enhanced areas of blood flow, according to our proposed scale (type of enhancement scale: 21, 0, 1, 2). The scale of enhancement arose by comparing lesion enhancement with background enhancement.…”

Section: Discussionmentioning

confidence: 99%

Adding the power of iodinated contrast media to the credibility of mammography in breast cancer diagnosis

Tsigginou

Gkali

Chalazonitis

et al. 2016

BJR

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Dual-energy contrast-enhanced spectral mammography (CESM) represents a relatively new diagnostic tool adjunct to mammography. The aim of this study was to strengthen the breast imaging-reporting and data system (BIRADS) classification score in order to improve early breast cancer diagnosis. For this reason, we propose a sum score, termed malignancy potential score (MPS), incorporating the standard BIRADS score and our proposed CESM score. From September 2014 to September 2015, 216 females (age range, 26-85 years; mean age 54.6 years) underwent CESM evaluation of mammographic findings that were primarily assessed as BIRADS 2-5. 10 of these patients had bilateral findings; a total of 226 lesions were examined. High-energy image evaluation was based on the intensity of contrast enhancement of the lesion compared with background enhancement, categorized as Type -1, 0, 1 or 2 enhancement. Histopathology reports were compared with imaging assessment. 98 of 226 lesions were malignant and 128 of 226 lesions were benign. The area under the curve was 0.843, 0.888 and 0.917 for mammographic BIRADS score, CESM score and MPS, respectively, with p-value , 0.05. The sensitivity, specificity and accuracy rates were 91.83, 80.47 and 85.40%, respectively, when a best MPS cut-off point of 4 was used. The malignancy potential score (MPS) has higher diagnostic performance than digital mammography or CESM alone. MPS empowers the credibility of the digital mammography BIRADS score and our proposed type of enhancement in dual-energy CESM and is a diagnostic tool that increases the accuracy rate in early breast cancer diagnosis.

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“…This schema is illustrated in Figure 6. As shown, GA only needs to be applied when L ranges from 2 to 129, the steps in which are the following: 8 BioMed Research International…”

Section: Additional Featuresmentioning

confidence: 99%

A New Computer‐Aided Diagnosis System with Modified Genetic Feature Selection for BI‐RADS Classification of Breast Masses in Mammograms

Boumaraf

Liu

Ferkous

et al. 2020

BioMed Research International

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Mammography remains the most prevalent imaging tool for early breast cancer screening. The language used to describe abnormalities in mammographic reports is based on the Breast Imaging Reporting and Data System (BI-RADS). Assigning a correct BI-RADS category to each examined mammogram is a strenuous and challenging task for even experts. This paper proposes a new and effective computer-aided diagnosis (CAD) system to classify mammographic masses into four assessment categories in BI-RADS. The mass regions are first enhanced by means of histogram equalization and then semiautomatically segmented based on the region growing technique. A total of 130 handcrafted BI-RADS features are then extracted from the shape, margin, and density of each mass, together with the mass size and the patient's age, as mentioned in BI-RADS mammography. Then, a modified feature selection method based on the genetic algorithm (GA) is proposed to select the most clinically significant BI-RADS features. Finally, a back-propagation neural network (BPN) is employed for classification, and its accuracy is used as the fitness in GA. A set of 500 mammogram images from the digital database for screening mammography (DDSM) is used for evaluation. Our system achieves classification accuracy, positive predictive value, negative predictive value, and Matthews correlation coefficient of 84.5%, 84.4%, 94.8%, and 79.3%, respectively. To our best knowledge, this is the best current result for BI-RADS classification of breast masses in mammography, which makes the proposed system promising to support radiologists for deciding proper patient management based on the automatically assigned BI-RADS categories.

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Variability and errors when applying the BIRADS mammography classification

Cited by 26 publications

References 7 publications

Imaging and Histopathologic Features of BI-RADS 3 Lesions Upgraded during Imaging Surveillance

Imaging and Histopathologic Features of BI-RADS 3 Lesions Upgraded during Imaging Surveillance

Adding the power of iodinated contrast media to the credibility of mammography in breast cancer diagnosis

A New Computer‐Aided Diagnosis System with Modified Genetic Feature Selection for BI‐RADS Classification of Breast Masses in Mammograms

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