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.
Results
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.
Conclusion
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.
Abbreviations:
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
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Article info
Publication history
Published online: May 24, 2023
Accepted:
March 23,
2023
Received in revised form:
March 23,
2023
Received:
February 13,
2023
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved All rights reserved.
