Rationale and Objectives
To develop and validate a nomogram for predicting the risk of malignancy of breast
imaging reporting and data system (BI-RADS) 4A lesions to reduce unnecessary invasive
examinations.
Materials and Methods
From January 2017 to July 2021, 190 cases of 4A lesions included in this study were
divided into training and validation sets in a ratio of 8:2. Radiomics features were
extracted from sonograms by Automatic Breast Volume Scanner (ABVS) and B-ultrasound.
We constructed the radiomics model and calculated the rad-scores. Univariate and multivariate
logistic regressions were used to assess demographics and lesion elastography values
(virtual touch tissue image, shear wave velocity) and to develop clinical model. A
clinical radiomics model was developed using rad-score and independent clinical factors,
and a nomogram was plotted. Nomogram performance was evaluated using discrimination,
calibration, and clinical utility.
Results
The nomogram included rad-score, age, and elastography, and showed good calibration.
In the training set, the area under the receiver operating characteristic curve (AUC)
of the clinical radiomics model (0.900, 95% confidence interval (CI): 0.843–0.958)
was superior to that of the radiomics model (0.860, 95% CI: 0.799–0.921) and clinical
model (0.816, 95% CI: 0.735–0.958) (p = 0.024 and 0.008, respectively). The decision curve analysis showed that the clinical
radiomics model had the highest net benefit in most threshold probability ranges.
Conclusion
ABVS and B-ultrasound-based radiomics nomograms have satisfactory performance in differentiating
benign and malignant 4A lesions. This can help clinicians make an accurate diagnosis
of 4A lesions and reduce unnecessary biopsy.
Key Words
Abbreviations:
ABVS (automatic breast volume scanner), ACR (American college of Radiology's), AUC (area under the receiver-operating characteristic curve), BI-RADS (breast imaging reporting and data system), BUS (B-ultrasound), DCA (decision curve analysis), FNR (false negative rate), FPR (false positive rate), GBDT (gradient boosting decision tree), ICC (correlation coefficient), LBP (local binary pattern), ROC (receiver operating characteristic), ROI (region of interest), SWV (shear wave velocity), VTI (virtual touch tissue image), VIF (variance inflation factors), VTQ (virtual touch tissue quantification)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: November 28, 2022
Accepted:
November 1,
2022
Received in revised form:
October 17,
2022
Received:
September 10,
2022
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
