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Automatic Breast Volume Scanner and B-Ultrasound-Based Radiomics Nomogram for Clinician Management of BI-RADS 4A Lesions

  • Author Footnotes
    † These authors have contributed equally to this work.
    Qianqing Ma
    Footnotes
    † These authors have contributed equally to this work.
    Affiliations
    Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, AH, P R China
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  • Author Footnotes
    † These authors have contributed equally to this work.
    Junli Wang
    Footnotes
    † These authors have contributed equally to this work.
    Affiliations
    Department of Ultrasound, The Second People's Hospital of WuHu, Wuhu, AH P R China
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  • Author Footnotes
    † These authors have contributed equally to this work.
    Daojing Xu
    Footnotes
    † These authors have contributed equally to this work.
    Affiliations
    Department of Ultrasound, The Second People's Hospital of WuHu, Wuhu, AH P R China
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  • Author Footnotes
    † These authors have contributed equally to this work.
    Chao Zhu
    Footnotes
    † These authors have contributed equally to this work.
    Affiliations
    Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, AH, P R China.
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  • Jing Qin
    Affiliations
    Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, AH, P R China
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  • Yimin Wu
    Affiliations
    Department of Ultrasound, The Second People's Hospital of WuHu, Wuhu, AH P R China
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  • Yankun Gao
    Affiliations
    Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, AH, P R China.
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  • Chaoxue Zhang
    Correspondence
    Address correspondence to: C. Z.
    Affiliations
    Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, AH, P R China
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  • Author Footnotes
    † These authors have contributed equally to this work.
Published:November 28, 2022DOI:https://doi.org/10.1016/j.acra.2022.11.002

      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)
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