Utility of BI-RADS Assessment Category 4 Subdivisions for Screening Breast MRI

Strigel, Roberta M.; Burnside, Elizabeth S.; Elezaby, Mai; Fowler, Amy M.; Kelcz, Frederick; Salkowski, Lonie R.; DeMartini, Wendy B.

doi:10.2214/ajr.16.16730

Cited by 45 publications

(34 citation statements)

References 13 publications

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“…In all, 110 patients were excluded from our analysis based on our study eligibility criteria, whereby only lesions that were identified as enhanced mass lesions on DCE‐MRI with a histologic diagnosis were enrolled. Moreover, screening breast MRI is typically performed in high‐risk populations 34 . Thus, patients undergoing screening breast MRI typically have a significantly increased risk of malignancy compared with those who undergo typical mammography screening at clinical practice.…”

Section: Discussionmentioning

confidence: 99%

Enhanced Masses on Contrast‐Enhanced Breast: Differentiation Using a Combination of Dynamic Contrast‐Enhanced MRI and Quantitative Evaluation with Synthetic MRI

Matsuda

Tsuda

Ebihara

et al. 2020

Magnetic Resonance Imaging

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BackgroundThe addition of synthetic MRI might improve the diagnostic performance of dynamic contrast‐enhanced MRI (DCE‐MRI) in patients with breast cancer.PurposeTo evaluate the diagnostic value of a combination of DCE‐MRI and quantitative evaluation using synthetic MRI for differentiation between benign and malignant breast masses.Study TypeRetrospective, observational.PopulationIn all, 121 patients with 131 breast masses who underwent DCE‐MRI with additional synthetic MRI were enrolled.Field Strength/Sequence3.0 Tesla, T1‐weighted DCE‐MRI and synthetic MRI acquired by a multiple‐dynamic, multiple‐echo sequence.AssessmentAll lesions were differentiated as benign or malignant using the following three diagnostic methods: DCE‐MRI type based on the Breast Imaging–Reporting and Data System; synthetic MRI type using quantitative evaluation values calculated by synthetic MRI; and a combination of the DCE‐MRI + Synthetic MRI types. The diagnostic performance of the three methods were compared.Statistical TestsUnivariate (Mann–Whitney U‐test) and multivariate (binomial logistic regression) analyses were performed, followed by receiver‐operating characteristic curve (AUC) analysis.ResultsUnivariate and multivariate analyses showed that the mean T1 relaxation time in a breast mass obtained by synthetic MRI prior to injection of contrast agent (pre‐T1) was the only significant quantitative value acquired by synthetic MRI that could independently differentiate between malignant and benign breast masses. The AUC for all enrolled breast masses assessed by DCE‐MRI + Synthetic MRI type (0.83) was significantly greater than that for the DCE‐MRI type (0.70, P < 0.05) or synthetic MRI type (0.73, P < 0.05). The AUC for category 4 masses assessed by the DCE‐MRI + Synthetic MRI type was significantly greater than that for those assessed by the DCE‐MRI type (0.74 vs. 0.50, P < 0.05).Data ConclusionA combination of synthetic MRI and DCE‐MRI improves the accuracy of diagnosis of benign and malignant breast masses, especially category 4 masses.Level of Evidence 4Technical Efficacy Stage 2J. MAGN. RESON. IMAGING 2021;53:381–391.

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Section: Discussionmentioning

confidence: 99%

Enhanced Masses on Contrast‐Enhanced Breast: Differentiation Using a Combination of Dynamic Contrast‐Enhanced MRI and Quantitative Evaluation with Synthetic MRI

Matsuda

Tsuda

Ebihara

et al. 2020

Magnetic Resonance Imaging

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“…A suspicious lesion detected by MRI performed for staging of a known cancer should be biopsied. There is ongoing research on the utility of BI‐RADS category 4 subdivisions for better stratification of the likelihood of malignancy in screening MRI . Our finding of the identification of benign US lesions will likely be of value for a subgroup of suspicious MRI lesions (category 4A, risk of malignancy >2 to ≤10%) detected during screening and shown to have benign US correlates.…”

Section: Discussionmentioning

confidence: 99%

Clinical Value of Shear Wave Elastography Added to Targeted Ultrasound (Second‐Look Ultrasound) in the Evaluation of Breast Lesions Suspicious of Malignancy Detected on Magnetic Resonance Imaging

Ghai

et al. 2019

J of Ultrasound Medicine

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Objectives-To determine the value of shear wave elastography (SWE) added to targeted ultrasound (US) after breast magnetic resonance imaging (MRI).Methods-From July 2015 to October 2017, 40 patients who underwent targeted US evaluations of suspicious MRI-detected American College of Radiology Breast Imaging Reporting and Data System category 4 lesions (mass or nonmass enhancement) were enrolled in this prospective study. B-mode US and SWE examinations were performed to detect US correlates to MRI-detected lesions; their Breast Imaging Reporting and Data System categories were recorded; lesions that were dark blue on a 6-point color scale or had maximum elasticity of 30 kPa or less were categorized as soft. Biopsy was performed with US or MRI guidance, with the pathologic findings correlated with MRI, US, and SWE findings. The value of SWE for lesion detection and identification of benign lesions was determined.Results-The mean age of the 40 patients was 51.1 years. There were 48 MRIdetected lesions (20 cancers, 3 high-risk lesions, and 25 benign lesions). Ultrasound correlates (8 category 3 and 25 category 4) were shown for 33 lesions (69%; P < .0001), with 16 cancers (80%; P < .0001) and 17 benign lesions. Shear wave elastography assisted detection of 3 (19%) cancers on US imaging. All 7 soft US category 3 lesions were benign (7 of 33 [21%]; P = .0014).Conclusions-Shear wave elastography was useful with targeted US after breast MRI to increase cancer detection by US. A significant number of US correlates to MRI-detected lesions could have been identified as benign (category 3 and soft) before biopsy, with the potential of short-interval follow-up of MRIdetected lesions with benign US correlates instead of biopsy.

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“…The signs of the lesions are overlapping and intricate. These lesions, benign or malignant, are all classified as BI-RADS 4, along with recommended invasive procedures such as needle biopsy to obtain pathological evidence [7][8][9]. Therefore, comprehensive understanding and improved evaluation methods of benign and malignant breast lesions are urgently needed to reduce invasive operations and the burden on patients.…”

Section: Introductionmentioning

confidence: 99%

Feasibility of Using Improved Convolutional Neural Network to Classify BI‐RADS 4 Breast Lesions: Compare Deep Learning Features of the Lesion Itself and the Minimum Bounding Cube of Lesion

Sheng

Tang

et al. 2021

Wireless Communications and Mobile Computing

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To determine the feasibility of using a deep learning (DL) approach to identify benign and malignant BI-RADS 4 lesions with preoperative breast DCE-MRI images and compare two 3D segmentation methods. The patients admitted from January 2014 to October 2020 were retrospectively analyzed. Breast MRI examination was performed before surgical resection or biopsy, and the masses were classified as BI-RADS 4. The first postcontrast images of DCE-MRI T1WI sequence were selected. There were two 3D segmentation methods for the lesions, one was manual segmentation along the edge of the lesion slice by slice, and the other was the minimum bounding cube of the lesion. Then, DL feature extraction was carried out; the pixel values of the image data are normalized to 0-1 range. The model was established based on the blueprint of the classic residual network ResNet50, retaining its residual module and improved 2D convolution module to 3D. At the same time, an attention mechanism was added to transform the attention mechanism module, which only fit the 2D image convolution module, into a 3D-Convolutional Block Attention Module (CBAM) to adapt to 3D-MRI. After the last CBAM, the algorithm stretches the output high-dimensional features into a one-dimensional vector and connects 2 fully connected slices, before finally setting two output results (P1, P2), which, respectively, represent the probability of benign and malignant lesions. Accuracy, sensitivity, specificity, negative predictive value, positive predictive value, the recall rate and area under the ROC curve (AUC) were used as evaluation indicators. A total of 203 patients were enrolled, with 207 mass lesions including 101 benign lesions and 106 malignant lesions. The data set was divided into the training set ( n = 145 ), the validation set ( n = 22 ), and the test set ( n = 40 ) at the ratio of 7 : 1 : 2; fivefold cross-validation was performed. The mean AUC based on the minimum bounding cube of lesion and the 3D-ROI of lesion itself were 0.827 and 0.799, the accuracy was 78.54% and 74.63%, the sensitivity was 78.85% and 83.65%, the specificity was 78.22% and 65.35%, the NPV was 78.85% and 71.31%, the PPV was 78.22% and 79.52%, the recall rate was 78.85% and 83.65%, respectively. There was no statistical difference in AUC based on the lesion itself model and the minimum bounding cube model ( Z = 0.771 , p = 0.4408 ). The minimum bounding cube based on the edge of the lesion showed higher accuracy, specificity, and lower recall rate in identifying benign and malignant lesions. Based on the lesion 3D-ROI segmentation using a minimum bounding cube can more effectively reflect the information of the lesion itself and the surrounding tissues. Its DL model performs better than the lesion itself. Using the DL approach with a 3D attention mechanism based on ResNet50 to identify benign and malignant BI-RADS 4 lesions was feasible.

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Utility of BI-RADS Assessment Category 4 Subdivisions for Screening Breast MRI

Cited by 45 publications

References 13 publications

Enhanced Masses on Contrast‐Enhanced Breast: Differentiation Using a Combination of Dynamic Contrast‐Enhanced MRI and Quantitative Evaluation with Synthetic MRI

Enhanced Masses on Contrast‐Enhanced Breast: Differentiation Using a Combination of Dynamic Contrast‐Enhanced MRI and Quantitative Evaluation with Synthetic MRI

Clinical Value of Shear Wave Elastography Added to Targeted Ultrasound (Second‐Look Ultrasound) in the Evaluation of Breast Lesions Suspicious of Malignancy Detected on Magnetic Resonance Imaging

Feasibility of Using Improved Convolutional Neural Network to Classify BI‐RADS 4 Breast Lesions: Compare Deep Learning Features of the Lesion Itself and the Minimum Bounding Cube of Lesion

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