Dataset Information

ACR BI-RADS Assessment Category 4 Subdivisions in Diagnostic Mammography: Utilization and Outcomes in the National Mammography Database.

ABSTRACT: Purpose To determine the utilization and positive predictive value (PPV) of the American College of Radiology (ACR) Breast Imaging Data and Reporting System (BI-RADS) category 4 subdivisions in diagnostic mammography in the National Mammography Database (NMD). Materials and Methods This study involved retrospective review of diagnostic mammography data submitted to the NMD from January 1, 2008 to December 30, 2014. Utilization rates of BI-RADS category 4 subdivisions were compared by year, facility (type, location, census region), and examination (indication, finding type) characteristics. PPV3 (positive predictive value for biopsies performed) was calculated overall and according to category 4 subdivision. The ?2 test was used to test for significant associations. Results Of 1 309 950 diagnostic mammograms, 125 447 (9.6%) were category 4, of which 33.3% (41 841 of 125 447) were subdivided. Subdivision utilization rates were higher (P < .001) in practices that were community, suburban, or in the West; for examination indication of prior history of breast cancer; and for the imaging finding of architectural distortion. Of 41 841 category 4 subdivided examinations, 4A constituted 55.6% (23 258 of 41 841) of the examinations; 4B, 31.8% (13 302 of 41 841) of the examinations; and 4C, 12.6% (5281 of 41 841) of the examinations. Pathologic outcomes were available in 91 563 examinations, and overall category 4 PPV3 was 21.1% (19 285 of 91 563). There was a statistically significant difference in PPV3 according to category 4 subdivision (P < .001): The PPV of 4A was 7.6% (1274 of 16 784), that of 4B was 22% (2317 of 10 408), and that of 4C was 69.3% (2839 of 4099). Conclusion Although BI-RADS suggests their use, subdivisions were utilized in the minority (33.3% [41 841 of 125 447]) of category 4 diagnostic mammograms, with variability based on facility and examination characteristics. When subdivisions were used, PPV3s were in BI-RADS-specified malignancy ranges. This analysis supports the use of subdivisions in broad practice and, given benefits for patient care, should motivate increased utilization. © RSNA, 2018 Online supplemental material is available for this article.

SUBMITTER: Elezaby M

PROVIDER: S-EPMC6413875 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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ACR BI-RADS Assessment Category 4 Subdivisions in Diagnostic Mammography: Utilization and Outcomes in the National Mammography Database.

Elezaby Mai M Li Geng G Bhargavan-Chatfield Mythreyi M Burnside Elizabeth S ES DeMartini Wendy B WB

Radiology 20180109 2

Purpose To determine the utilization and positive predictive value (PPV) of the American College of Radiology (ACR) Breast Imaging Data and Reporting System (BI-RADS) category 4 subdivisions in diagnostic mammography in the National Mammography Database (NMD). Materials and Methods This study involved retrospective review of diagnostic mammography data submitted to the NMD from January 1, 2008 to December 30, 2014. Utilization rates of BI-RADS category 4 subdivisions were compared by year, facil ...[more]

PMID: 29315061

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Project description:Purpose To evaluate Breast Imaging Reporting and Data System (BI-RADS) category 3 assessment at diagnostic examination after recall from screening in a large urban population after implementation of digital breast tomosynthesis (DBT) by focusing both on overall use and use stratified by recalled finding type and outcome at 2 years. Materials and Methods This was an intuitional review board-approved and HIPAA-compliant retrospective review of 10 728 digital mammography (DM) examinations from September 1, 2010, to August 30, 2011, and 15 571 screening DBT examinations from October 1, 2011, to February 28, 2013. The recall populations for DM and DBT were 1112 of 10 728 (10.4% of women screened) and 1366 of 15 571 (8.8% of women screened), respectively. Recall examinations were classified according to finding type: calcifications, asymmetry or focal asymmetry, mass, and architectural distortion. Differences between groups were compared by using the χ2 test. Results Although there was no significant change in the utilization rate of BI-RADS category 3 in those patients screened with DM compared with DBT (168 of 10 728, 1.6% for DM vs 206 of 15 571, 1.3% for DBT; P = .102), there was a mean overall reduction of 2.4 women per 1000 (95% confidence interval [CI]: -0.5, 5.4) recommended for short-term follow-up. Lesion types given a BI-RADS category 3 assessment after diagnostic work-up did not change. The distribution of recalled finding types significantly changed with DBT, with increased recall examinations for architectural distortion and mass (P < .001) and decreased recall examinations for asymmetries (P ≤ .001). There was no change in recall examinations for calcifications (P = .977). Conclusion Screening with DBT did not significantly change the utilization rate of BI-RADS category 3 classification; however, the overall number of patients recommended for short-interval follow-up decreased by a mean of 2.4 women per 1000 (95% CI: -0.5, 5.4). © RSNA, 2017 Online supplemental material is available for this article.

Project description:ObjectiveThis study aims to investigate the value of contrast-enhanced ultrasound (CEUS) in the differential diagnosis and risk stratification of ACR TI-RADS category 4 and 5 thyroid nodules with non-hypovascular.MethodsFrom January 2016 to December 2019 in our hospital, 217 ACR TI-RADS category 4 and 5 nodules with non-hypovascular in 210 consecutive patients were included for a derivation cohort. With surgery and/or fine-needle aspiration (FNA) as a reference, conventional ultrasound (US) features and CEUS features were analyzed. Multivariate logistic regression analysis was used to screen the independent risk factors and establish a risk predictive model. Between January 2020 and March 2021, a second cohort of 100 consecutive patients with 101 nodules were included for an external validation cohort. The model was converted into a simplified risk score and was validated in the validation cohort. The area under the receiver operating characteristic curves (AUC) were used to assess the models' diagnostic performance.ResultsMicro-calcification, irregular margin, earlier wash-out, centripetal enhancement, and absence of ring enhancement were independent risk factors and strongly discriminated malignancy in the derivation cohort (AUC = 0.921, 95% CI 0.876-0.953) and the validation cohort (0.900, 0.824-0.951). There was no significant difference (P = 0.3282) between the conventional US and CEUS in differentiating malignant non-hypovascular thyroid nodules, but a combination of them (the predictive model) had better performance than the single method (all P <0.05), with a sensitivity of 87.0%, specificity of 86.2%, and accuracy of 86.6% in the derivation cohort. The risk score based on the independent risk factors divided non-hypovascular thyroid nodules into low-suspicious (0-3 points; malignancy risk <50%) and high-suspicious (4-7 points; malignancy risk ≥ 50%), the latter with nodule ≥10mm was recommended for FNA. The risk score showed a good ability of risk stratification in the validation cohort. Comparing ACR TI-RADS in screening suitable non-hypovascular nodules for FNA, the risk score could avoid 30.8% benign nodules for FNA.ConclusionsCEUS is helpful in combination with conventional US in differentiating ACR TI-RADS category 4 and 5 nodules with non-hypovascular. The risk score in this study has the potential to improve the diagnosis and risk stratification of non-hypovascular thyroid nodules.

Project description:The Breast Imaging Reporting and Data System (BI-RADS) breast composition descriptors are used for standardized mammographic reporting and are assessed visually. This reporting is clinically relevant because breast composition can impact mammographic sensitivity and is a breast cancer risk factor. New techniques are presented and evaluated for generating automated BI-RADS breast composition descriptors using both raw and calibrated full field digital mammography (FFDM) image data.A matched case-control dataset with FFDM images was used to develop three automated measures for the BI-RADS breast composition descriptors. Histograms of each calibrated mammogram in the percent glandular (pg) representation were processed to create the new BR(pg) measure. Two previously validated measures of breast density derived from calibrated and raw mammograms were converted to the new BR(vc) and BR(vr) measures, respectively. These three measures were compared with the radiologist-reported BI-RADS compositions assessments from the patient records. The authors used two optimization strategies with differential evolution to create these measures: method-1 used breast cancer status; and method-2 matched the reported BI-RADS descriptors. Weighted kappa (κ) analysis was used to assess the agreement between the new measures and the reported measures. Each measure's association with breast cancer was evaluated with odds ratios (ORs) adjusted for body mass index, breast area, and menopausal status. ORs were estimated as per unit increase with 95% confidence intervals.The three BI-RADS measures generated by method-1 had κ between 0.25-0.34. These measures were significantly associated with breast cancer status in the adjusted models: (a) OR = 1.87 (1.34, 2.59) for BR(pg); (b) OR = 1.93 (1.36, 2.74) for BR(vc); and (c) OR = 1.37 (1.05, 1.80) for BR(vr). The measures generated by method-2 had κ between 0.42-0.45. Two of these measures were significantly associated with breast cancer status in the adjusted models: (a) OR = 1.95 (1.24, 3.09) for BR(pg); (b) OR = 1.42 (0.87, 2.32) for BR(vc); and (c) OR = 2.13 (1.22, 3.72) for BR(vr). The radiologist-reported measures from the patient records showed a similar association, OR = 1.49 (0.99, 2.24), although only borderline statistically significant.A general framework was developed and validated for converting calibrated mammograms and continuous measures of breast density to fully automated approximations for the BI-RADS breast composition descriptors. The techniques are general and suitable for a broad range of clinical and research applications.

Project description:PURPOSE: To determine (a) how often the Breast Imaging Reporting and Data System (BI-RADS) category 3 was used in the American College of Radiology Imaging Network (ACRIN) Digital Mammographic Imaging Screening Trial (DMIST), either at the time of screening mammography or after work-up, (b) how often subjects actually returned for the recommended follow-up examination, and (c) the rate and stages of any malignancies subsequently found in subjects for whom short-term interval follow-up was recommended. MATERIALS AND METHODS: This study was approved by the Institutional Review Board at all institutions where subjects were enrolled. All subjects participating in DMIST gave informed consent and the study was HIPAA-compliant. A total of 47,599 DMIST-eligible and evaluable subjects, all of whom consented to undergo both digital and screen-film mammography, were included in this analysis. Cases referred for short-term interval follow-up based on digital, screen-film, or both imaging examinations were determined. Compliance with the recommendations and the final outcome (malignancy diagnosis at biopsy or no malignancy confirmed through follow-up) of each evaluable case were determined. RESULTS: A total of 1114 of the 47,599 (2.34%) subjects had tumors assigned a BI-RADS 3 category and were recommended to undergo short-interval follow-up. In this study, 791 of 1114 (71%) of the subjects were compliant with the recommendation and returned for short-interval follow-up. Of the women who did not return for short-interval follow-up, 70% (226 of 323) did return for their next annual mammography. Among all subjects whose tumors were assigned a BI-RADS 3 category either at screening mammography or after additional work-up, nine of 1114 (0.81%) were found to have cancer. Of the nine biopsy-proved cancers, six were invasive cancers and three were ductal carcinoma in situ stage Tis-T1c. The invasive cancers were all less than 2 cm in size. CONCLUSION: In DMIST, radiologists used the BI-RADS 3 classification infrequently (2.3% of patients). Tumors assigned a BI-RADS 3 category had a low rate of malignancy. The relatively high rate of noncompliance with short-interval follow-up recommendations (323 of 1114, or 29%) supports prior recommendations that radiologists thoroughly evaluate lesions before placing them in this category.

Dataset Information

ACR BI-RADS Assessment Category 4 Subdivisions in Diagnostic Mammography: Utilization and Outcomes in the National Mammography Database.

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ACR BI-RADS Assessment Category 4 Subdivisions in Diagnostic Mammography: Utilization and Outcomes in the National Mammography Database.

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