Special Review|Articles in Press

Improved Differential Diagnosis Based on BI-RADS Descriptors and Apparent Diffusion Coefficient for Breast Lesions: A Multiparametric MRI Analysis as Compared to Kaiser Score

      Rationale and Objectives

      To develop the nomogram utilizing the American College of Radiology BI-RADS descriptors, clinical features, and apparent diffusion coefficient (ADC) to differentiate benign from malignant breast lesions.

      Materials and Methods

      A total of 341 lesions (161 malignant and 180 benign) were included. Clinical data and imaging features were reviewed. Univariable and multivariable logistic regression analyses were performed to determine the independent variables. ADC as a continuous or classified into binary form with a cutoff value of 1.30 × 10−3 mm2/s, incorporated other independent predictors to construct two nomograms, respectively. Receiver operating curve and calibration plot was employed to test the models’ discriminative ability. The diagnostic performance between the developed model and the Kaiser score (KS) was also compared.


      In both models, high patient age, the presence of root sign, time-intensity curves (TICs) types (plateau and washout), heterogenous internal enhancement, the presence of peritumoral edema, and ADC were independently associated with malignancy. The AUCs of two multivariable models (AUC, 0.957; 95% CI: 0.929–0.976 and AUC, 0.958; 95% CI: 0.931–0.976) were significantly higher than that of the KS (AUC, 0.919, 95% CI: 0.885–0.946; both P < 0.001). At the same sensitivity of 95.7%, our models showed an increase in specificity by 5.56% (P = 0.076) and 6.11% (P = 0.035), respectively, as compared to the KS.


      The models incorporating MRI features (root sign, TIC, margins, internal enhancement, and presence of edema), quantitative ADC value, and patient age showed improved diagnostic performance and might have avoided more unnecessary biopsies in comparison with the KS, although further external validation is required.


      BI-RADS (Breast Imaging Reporting and Data System), DWI (Diffusion-weighted imaging), ADC (Apparent diffusion coefficient), KS (Kaiser score), TIC (Time-intensity curve), DCE-MRI (Dynamic contrast-enhanced magnetic resonance imaging), EUSOBI (European Society of Breast Imaging), ROI (Regions of interest), ICC (Intraclass correlation coefficient), OR (Odds ratio), AUC (Area under the curve), ROC (Receiver operating characteristic), VIF (Variance inflation factor), AIC (Akaike information criterion), DCA (Decision curve analysis)

      Key Words

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'


      Subscribe to Academic Radiology
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect


        • Siegel R.L.
        • Miller K.D.
        • Fuchs H.E.
        • et al.
        Cancer statistics, 2021.
        CA Cancer J Clin. 2021; 71: 7-33
        • Marino M.A.
        • Riedl C.C.
        • Bernathova M.
        • et al.
        Imaging phenotypes in women at high risk for breast cancer on mammography, ultrasound, and magnetic resonance imaging using the fifth edition of the breast imaging reporting and data system.
        Eur J Radiol. 2018; 106: 150-159
        • Baltzer P.A.
        • Dietzel M.
        • Kaiser W.A.
        A simple and robust classification tree for differentiation between benign and malignant lesions in MR-mammography.
        Eur Radiol. 2013; 23: 2051-2060
        • Dietzel M.
        • Baltzer P.A.T.
        How to use the Kaiser score as a clinical decision rule for diagnosis in multiparametric breast MRI: a pictorial essay.
        Insights Imaging. 2018; 9: 325-335
        • Dietzel M.
        • Krug B.
        • Clauser P.
        • et al.
        A multicentric comparison of apparent diffusion coefficient mapping and the Kaiser score in the assessment of breast lesions.
        Invest Radiol. 2021; 56: 274-282
        • Marino M.A.
        • Clauser P.
        • Woitek R.
        • et al.
        A simple scoring system for breast MRI interpretation: does it compensate for reader experience?.
        Eur Radiol. 2016; 26: 2529-2537
        • Milos R.I.
        • Pipan F.
        • Kalovidouri A.
        • et al.
        The Kaiser score reliably excludes malignancy in benign contrast-enhancing lesions classified as BI-RADS 4 on breast MRI high-risk screening exams.
        Eur Radiol. 2020; 30: 6052-6061
        • Wengert G.J.
        • Pipan F.
        • Almohanna J.
        • et al.
        Impact of the Kaiser score on clinical decision-making in BI-RADS 4 mammographic calcifications examined with breast MRI.
        Eur Radiol. 2020; 30: 1451-1459
        • Jajodia A.
        • Sindhwani G.
        • Pasricha S.
        • et al.
        Application of the Kaiser score to increase diagnostic accuracy in equivocal lesions on diagnostic mammograms referred for MR mammography.
        Eur J Radiol. 2021; 134109413
        • Iima M.
        • Honda M.
        • Sigmund E.E.
        • et al.
        Diffusion MRI of the breast: current status and future directions.
        J Magn Reson Imaging. 2020; 52: 70-90
        • Rahbar H.
        • Zhang Z.
        • Chenevert T.L.
        • et al.
        Utility of diffusion-weighted imaging to decrease unnecessary biopsies prompted by breast MRI: a trial of the ECOG-ACRIN Cancer Research Group (A6702).
        Clin Cancer Res. 2019; 25: 1756-1765
        • Clauser P.
        • Krug B.
        • Bickel H.
        • et al.
        Diffusion-weighted imaging allows for downgrading MR BI-RADS 4 lesions in contrast-enhanced MRI of the breast to avoid unnecessary biopsy.
        Clin Cancer Res. 2021; 27: 1941-1948
        • Baltzer A.
        • Dietzel M.
        • Kaiser C.G.
        • et al.
        Combined reading of contrast enhanced and diffusion weighted magnetic resonance imaging by using a simple sum score.
        Eur Radiol. 2016; 26: 884-891
        • D'Orsi C.
        • Morris E.
        • Mendelson E.
        ACR BI-RADS® Atlas. Breast Imaging Reporting and Data System, 2013
        • Woitek R.
        • Spick C.
        • Schernthaner M.
        • et al.
        A simple classification system (the Tree flowchart) for breast MRI can reduce the number of unnecessary biopsies in MRI-only lesions.
        Eur Radiol. 2017; 27: 3799-3809
        • Baltzer P.
        • Mann R.M.
        • Iima M.
        • et al.
        Diffusion-weighted imaging of the breast-a consensus and mission statement from the EUSOBI International Breast Diffusion-Weighted Imaging working group.
        Eur Radiol. 2020; 30: 1436-1450
        • Kim Y.
        • Margonis G.A.
        • Prescott J.D.
        • et al.
        Nomograms to predict recurrence-free and overall survival after curative resection of adrenocortical carcinoma.
        JAMA Surg. 2016; 151: 365-373
        • Steyerberg E.W.
        • Vergouwe Y.
        Towards better clinical prediction models: seven steps for development and an ABCD for validation.
        Eur Heart J. 2014; 35: 1925-1931
        • Liu L.
        • Xie J.
        • Wu W.
        • et al.
        A simple nomogram for predicting failure of non-invasive respiratory strategies in adults with COVID-19: a retrospective multicentre study.
        Lancet Digit Health. 2021; 3: e166-e174
        • Lei Z.
        • Li J.
        • Wu D.
        • et al.
        Nomogram for preoperative estimation of microvascular invasion risk in hepatitis B virus–related hepatocellular carcinoma within the Milan criteria.
        JAMA Surg. 2016; 151: 356-363
        • Zhang M.
        • Horvat J.V.
        • Bernard-Davila B.
        • et al.
        Multiparametric MRI model with dynamic contrast-enhanced and diffusion-weighted imaging enables breast cancer diagnosis with high accuracy.
        J Magn Reson Imaging. 2019; 49: 864-874
        • Sun S.Y.
        • Ding Y.
        • Li Z.
        • et al.
        Multiparameter MRI model with DCE-MRI, DWI, and synthetic MRI improves the diagnostic performance of BI-RADS 4 lesions.
        Front Oncol. 2021; 11699127
        • Rahbar H.
        • Partridge S.C.
        • Demartini W.B.
        • et al.
        In vivo assessment of ductal carcinoma in situ grade: a model incorporating dynamic contrast-enhanced and diffusion-weighted breast MR imaging parameters.
        Radiology. 2012; 263: 374-382
        • Tahmassebi A.
        • Wengert G.J.
        • Helbich T.H.
        • et al.
        Impact of machine learning with multiparametric magnetic resonance imaging of the breast for early prediction of response to neoadjuvant chemotherapy and survival outcomes in breast cancer patients.
        Invest Radiol. 2019; 54: 110-117
        • Yang X.
        • Dong M.
        • Li S.
        • et al.
        Diffusion-weighted imaging or dynamic contrast-enhanced curve: a retrospective analysis of contrast-enhanced magnetic resonance imaging-based differential diagnoses of benign and malignant breast lesions.
        Eur Radiol. 2020; 30: 4795-4805
        • Leithner D.
        • Wengert G.J.
        • Helbich T.H.
        • et al.
        Clinical role of breast MRI now and going forward.
        Clin Radiol. 2018; 73: 700-714
        • Uematsu T.
        Focal breast edema associated with malignancy on T2-weighted images of breast MRI: peritumoral edema, prepectoral edema, and subcutaneous edema.
        Breast Cancer (Tokyo, Japan). 2015; 22: 66-70
        • Baltzer P.A.
        • Yang F.
        • Dietzel M.
        • et al.
        Sensitivity and specificity of unilateral edema on T2w-TSE sequences in MR-mammography considering 974 histologically verified lesions.
        Breast J. 2010; 16: 233-239
        • Park N.J.
        • Jeong J.Y.
        • Park J.Y.
        • et al.
        Peritumoral edema in breast cancer at preoperative MRI: an interpretative study with histopathological review toward understanding tumor microenvironment.
        Sci Rep. 2021; 11: 12992
        • Liang T.
        • Hu B.
        • Du H.
        • et al.
        Predictive value of T2-weighted magnetic resonance imaging for the prognosis of patients with mass-type breast cancer with peritumoral edema.
        Oncol Lett. 2020; 20: 314
        • Tao W.
        • Lu M.
        • Zhou X.
        • et al.
        Machine learning based on multi-parametric MRI to predict risk of breast cancer.
        Front Oncol. 2021; 11570747
        • Huang Y.X.
        • Chen Y.L.
        • Li S.P.
        • et al.
        Development and validation of a simple-to-use nomogram for predicting the upgrade of atypical ductal hyperplasia on core needle biopsy in ultrasound-detected breast lesions.
        Front Oncol. 2020; 10609841
        • Wang H.
        • Lai J.
        • Li J.
        • et al.
        Does establishing a preoperative nomogram including ultrasonographic findings help predict the likelihood of malignancy in patients with microcalcifications?.
        Cancer Imaging. 2019; 19: 46
        • Baltzer P.A.T.
        • Kapetas P.
        • Marino M.A.
        • et al.
        New diagnostic tools for breast cancer.
        Memo. 2017; 10: 175-180
        • Lo Gullo R.
        • Sevilimedu V.
        • Baltzer P.
        • et al.
        A survey by the European Society of Breast Imaging on the implementation of breast diffusion-weighted imaging in clinical practice.
        Eur Radiol. 2022; 32: 6588-6597
        • An Y.
        • Mao G.
        • Ao W.
        • et al.
        Can DWI provide additional value to Kaiser score in evaluation of breast lesions.
        Eur Radiol. 2022; 32: 5964-5973
        • Chen Z.W.
        • Zhao Y.F.
        • Liu H.R.
        • et al.
        Assessment of breast lesions by the Kaiser score for differential diagnosis on MRI: the added value of ADC and machine learning modeling.
        Eur Radiol. 2022; 32: 6608-6618