Value of breast MRI omics features and clinical characteristics in Breast Imaging Reporting and Data System (BI-RADS) category 4 breast lesions: an analysis of radiomics-based diagnosis
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Background: The Breast Imaging Reporting and Data System (BI-RADS) category 4 breast lesions is categorized into 4A, 4B, and 4C, which reflect an increasing malignancy potential from low (2–10%) moderate (10–50%) and high (50–95%). Determining the benign and malignant of BI-RADS category 4 breast lesions is very important for accurate diagnosis and follow-up treatment. This study aimed to explore the value of breast magnetic resonance imaging (MRI) omics features and clinical characteristics in the assessment of BI-RADS category 4 breast lesions. Methods: This retrospective study analyzed 96 lesions (39 benign and 57 malignant) from 92 patients diagnosed with MRI BI-RADS category 4 lesions in the Second Affiliated Hospital of Dalian Medical University between May 2017 and December 2019. The lesions were sub-categorized as BI-RADS 4A, 4B, or 4C based on the MRI findings. An imaging omics analysis model was applied to extract the MRI features. The positive predictive value (PPV) of each subcategory was calculated, and the area under the curve (AUC) was used to describe the efficiency for different diagnoses. Moreover, we analyzed 17 clinical indicators to assess their diagnostic value for BI-RADS category 4 breast lesions. Results: The PPVs of BI-RADS 4A, 4B, and 4C were 7.1% (2/28), 41.2% (7/17), and 94.1% (48/51), respectively. The AUC, sensitivity, and specificity were 0.919, 84.2%, and 92.3%, respectively. The combination of T1-weighted images (T1WI) with dynamic contrast-enhanced (DCE) MRI yielded the best diagnostic results among all dual sequences. Two clinical indicators [progesterone receptor (PR) and Ki-67 expression] achieved an AUC almost equal to 1.0. The radiomics and redundancy reduction methods reduced the clinical data features from 1,233 to 14. Conclusions: High diagnostic performance can be achieved in distinguishing malignant breast BI-RADS category 4 lesions using the combination of T1WI and DCE in MRI. Combining the PR and Ki-67 expression variables can further improve MRI accuracy for breast BI-RADS category 4 lesions.Keywords:
BI-RADS
Breast imaging
Breast MRI
Abstract Objectives To investigate whether the application of the Kaiser score for breast magnetic resonance imaging (MRI) might downgrade breast lesions that present as mammographic calcifications and avoid unnecessary breast biopsies Methods This IRB-approved, retrospective, cross-sectional, single-center study included 167 consecutive patients with suspicious mammographic calcifications and histopathologically verified results. These patients underwent a pre-interventional breast MRI exam for further diagnostic assessment before vacuum-assisted stereotactic-guided biopsy (95 malignant and 72 benign lesions). Two breast radiologists with different levels of experience independently read all examinations using the Kaiser score, a machine learning–derived clinical decision-making tool that provides probabilities of malignancy by a formalized combination of diagnostic criteria. Diagnostic performance was assessed by receiver operating characteristics (ROC) analysis and inter-reader agreement by the calculation of Cohen’s kappa coefficients. Results Application of the Kaiser score revealed a large area under the ROC curve (0.859–0.889). Rule-out criteria, with high sensitivity, were applied to mass and non-mass lesions alike. The rate of potentially avoidable breast biopsies ranged between 58.3 and 65.3%, with the lowest rate observed with the least experienced reader. Conclusions Applying the Kaiser score to breast MRI allows stratifying the risk of breast cancer in lesions that present as suspicious calcifications on mammography and may thus avoid unnecessary breast biopsies. Key Points • The Kaiser score is a helpful clinical decision tool for distinguishing malignant from benign breast lesions that present as calcifications on mammography. • Application of the Kaiser score may obviate 58.3–65.3% of unnecessary stereotactic biopsies of suspicious calcifications. • High Kaiser scores predict breast cancer with high specificity, aiding clinical decision-making with regard to re-biopsy in case of negative results.
BI-RADS
Breast MRI
Neuroradiology
Breast imaging
Breast biopsy
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Reports on the specificity of breast MRI are heterogeneous, depending on the respective setting of the performed study.To retrospectively estimate the sensitivity and especially the specificity of breast MRI in the non-screening setting as an adjunct to mammography sorted by breast density and to estimate the accuracy of breast MRI in cases rated BI-RADS 0 and 3 mammographically.A total of 216 consecutive patients with referral to breast MRI and previously acquired mammography were enrolled in this analysis. Negative findings were followed up with a mean time of 26.7 months. The loss to follow-up was 10.8%. The single breast was regarded as the study subject (n=399, 364 cases were eligible for calculation of diagnostic accuracy). BI-RADS 1 and 2 were rated as benign, 4 and 5 as malignant. BI-RADS 0 and 3 were analyzed separately. The 95% confidence intervals (CIs) were calculated from the normally approximated binomial distribution and taken to represent significant differences for the two imaging modalities if they did not overlap.Among the study population, 62 malignant neoplasms were detected. For cases rated BI-RADS 1, 2, 4, and 5 (n=251), the sensitivity of breast MRI was 95.7% (95% CI 89.9-100.0%) and 74.5% (95% CI 62.0-87.0%) for mammography, respectively. The specificity of breast MRI was 96.1% (95% CI 93.4-98.8%) and 92.2% (95% CI 88.5-95.9%) for mammography, respectively. The diagnostic accuracy of breast MRI did not depend on breast density. In cases rated BI-RADS 0, n=57 (3, n=56), breast MRI achieved a sensitivity of 100% (90.9%) and a specificity of 98.1% (88.9%). There was a significant (P< 0.01) accumulation of dense breast tissue (ACR IV) in breasts rated BI-RADS 0 in mammography. Breast MRI missed three malignant lesions, two of them being smaller than 3 mm.There is no rationale to criticize the low specificity of breast MRI when used as an adjunct to mammography. The independency of the diagnostic accuracy of breast MRI from breast density makes it a worthwhile choice in mammographic BI-RADS 0 cases.
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The breast imaging reporting and data system (BI-RADS) lexicon provides a standardized terminology for describing leision characteristics but does not provide defined rules for converting specific imaging features into diagnostic categories. The inter-reader agreement of the BI-RADS is moderate. In this study, we explored the use of a simplified protocol and scoring system for BI-RADS categorization which integrates the morphologic features (MF), kinetic time-intensity curve (TIC), and apparent diffusion coefficient (ADC) values with equal weights, with a view to providing a convenient and practical method for breast magnetic resonance imaging (MRI) and improving the inter-reader agreement and diagnostic performance of BI-RADS.This cross-sectional, retrospective, single-center study included 879 patients with 898 histopathologically verified lesions who underwent an MRI scan on a 3.0 Tesla GE Discovery 750 MRI scanner between January 1, 2017, and June 30, 2020. The BI-RADS categorization of the studied lesions was assessed according to the sum of the assigned scores (the presence of malignant MF, lower ADC, and suspicious TIC each warranted a score of +1). Total scores of +2 and +3 were classified as category 5, scores of +1 were classified as category 4, and scores of +0 but with other lesions of interest were classified as category 3. The receiver operating characteristic (ROC) curves were plotted, and the sensitivity, specificity, and accuracy of this categorization were investigated to assess its efficacy and its consistency with pathology.There were 472 malignant, 104 risk, and 322 benign lesions. Our simplified scoring protocol had high diagnostic accuracy, with an area under curve (AUC) value of 0.896. In terms of the borderline effect of pathological risk and category 4 lesions, our results showed that when risk lesions were classified together with malignant ones, the AUC value improved (0.876 vs. 0.844 and 0.909 vs. 0.900). When category 4 and 5 lesions were classified as malignant, the specificity, accuracy, and AUC value decreased (82.3% vs. 93.2%, 89.3% vs. 90.2%, and 0.876 vs. 0.909, respectively). Therefore, to improve the diagnostic accuracy of the protocol for BI-RADS categorization, only category 5 lesions should be considered to be malignant.Our simplified scoring protocol that integrates MF, TIC, and ADC values with equal weights for BI-RADS categorization could improve both the diagnostic performance of the protocol for BI-RADS categorization in clinical practice and the understanding of the benign-risk-malignant breast diseases.
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Breast MRI
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Breast imaging
Breast MRI
Clinical Practice
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Breast MRI
Breast imaging
Breast biopsy
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Breast imaging
BI-RADS
Breast MRI
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Breast MRI
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Background: The Breast Imaging Reporting and Data System (BI-RADS) category 4 breast lesions is categorized into 4A, 4B, and 4C, which reflect an increasing malignancy potential from low (2–10%) moderate (10–50%) and high (50–95%). Determining the benign and malignant of BI-RADS category 4 breast lesions is very important for accurate diagnosis and follow-up treatment. This study aimed to explore the value of breast magnetic resonance imaging (MRI) omics features and clinical characteristics in the assessment of BI-RADS category 4 breast lesions. Methods: This retrospective study analyzed 96 lesions (39 benign and 57 malignant) from 92 patients diagnosed with MRI BI-RADS category 4 lesions in the Second Affiliated Hospital of Dalian Medical University between May 2017 and December 2019. The lesions were sub-categorized as BI-RADS 4A, 4B, or 4C based on the MRI findings. An imaging omics analysis model was applied to extract the MRI features. The positive predictive value (PPV) of each subcategory was calculated, and the area under the curve (AUC) was used to describe the efficiency for different diagnoses. Moreover, we analyzed 17 clinical indicators to assess their diagnostic value for BI-RADS category 4 breast lesions. Results: The PPVs of BI-RADS 4A, 4B, and 4C were 7.1% (2/28), 41.2% (7/17), and 94.1% (48/51), respectively. The AUC, sensitivity, and specificity were 0.919, 84.2%, and 92.3%, respectively. The combination of T1-weighted images (T1WI) with dynamic contrast-enhanced (DCE) MRI yielded the best diagnostic results among all dual sequences. Two clinical indicators [progesterone receptor (PR) and Ki-67 expression] achieved an AUC almost equal to 1.0. The radiomics and redundancy reduction methods reduced the clinical data features from 1,233 to 14. Conclusions: High diagnostic performance can be achieved in distinguishing malignant breast BI-RADS category 4 lesions using the combination of T1WI and DCE in MRI. Combining the PR and Ki-67 expression variables can further improve MRI accuracy for breast BI-RADS category 4 lesions.
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Breast imaging
Breast MRI
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Breast imaging
Breast MRI
Institutional review board
Univariate analysis
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