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Intra- and peritumoral radiomics for predicting malignant BiRADS category 4 breast lesions on contrast-enhanced spectral mammography: a multicenter study

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Abstract

Objective

To construct and test a nomogram based on intra- and peritumoral radiomics and clinical factors for predicting malignant BiRADS 4 lesions on contrast-enhanced spectral mammography.

Methods

A total of 884 patients with BiRADS 4 lesions were enrolled from two centers. For each lesion, five ROIs were defined using the intratumoral region (ITR), peritumoral regions (PTRs) of 5 and 10 mm around the tumor, and ITR plus PTRs of 5 mm and 10 mm. Five radiomics signatures were established by LASSO after selecting features. A nomogram was built using selected signatures and clinical factors by multivariable logistic regression analysis. The performance of the nomogram was assessed with the AUC, decision curve analysis, and calibration curves, and also compared with the radiomics model, clinical model, and radiologists.

Results

The nomogram built by three radiomics signatures (constructed from ITR, 5 mm PTR, and ITR + 10 mm PTR) and two clinical factors (age and BiRADS category) showed powerful predictive ability in internal and external test sets with AUCs of 0.907 and 0.904, respectively. The calibration curves, decision curve analysis, showed favorable predictive performance of the nomogram. In addition, radiologists improved the diagnostic performance with the help of nomogram.

Conclusion

The nomogram established via intratumoral and peritumoral radiomics features and clinical risk factors had the best performance in distinguishing benign and malignant BiRADS 4 lesions, which could help radiologists improve diagnostic capabilities.

Key Points

• Radiomics features from peritumoral regions in contrast-enhanced spectral mammography images may provide valuable information for the diagnosis of benign and malignant breast imaging reporting and data system category 4 breast lesions.

• The nomogram incorporated intra- and peritumoral radiomics features and clinical variables have good application prospects in assisting clinical decision-makers.

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Abbreviations

AUC:

Area under the ROC curve

BiRADS:

Breast Imaging Reporting and Data System

CC:

Craniocaudal

CESM:

Contrast-enhanced spectral mammography

DCA:

Decision curve analysis

ITR:

Intratumoral region

LASSO:

Least absolute shrinkage and selection operator

LE:

Low energy

MLO:

Mediolateral oblique

MRI:

Magnetic resonance imaging

PTR:

Peritumoral region

RC:

Recombined

ROC:

Receiver operating characteristic

ROI:

Region of interest

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Acknowledgements

We thank all the study participants and referring technicians for their participation in this study, especially thanks Haicheng Zhang and Ran Zhang for providing technical support to the study.

Funding

This study has received funding by the National Natural Science Foundation of China (82001775), the Natural Science Foundation of Shandong Province (ZR2021MH120), and the Special Fund for Breast Disease Research of Shandong Medical Association (YXH2021ZX055).

Author information

Authors and Affiliations

  1. Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding East Street, Yantai, Shandong, People’s Republic of China, 264000

    Shijie Zhang, Huafei Shao, Wenjuan Li, Haicheng Zhang, Fan Lin, Qianqian Zhang, Han Zhang, Zhongyi Wang, Ning Mao & Haizhu Xie

  2. School of Medical Imaging, Binzhou Medical University, Yantai, Shandong, People’s Republic of China, 2640003

    Jing Gao

  3. Huiying Medical Technology Co, Ltd, Beijing, People’s Republic of China, 100192

    Ran Zhang

  4. Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200000, China

    Yajia Gu

  5. Department of Radiology, Yantai Hospital of Traditional Chinese Medicine, Yantai, Shandong, People’s Republic of China, 264000

    Yunqiang Wang

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  1. Shijie Zhang
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Corresponding authors

Correspondence to Yunqiang Wang, Ning Mao or Haizhu Xie.

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Guarantor

The scientific guarantor of this publication is Haizhu Xie.

Conflict of interest

One of the authors of this manuscript (Ran Zhang) is Huiying Medical Technology Co. Ltd. The remaining authors declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

Two of the authors (Haicheng Zhang and Ran Zhang) have significant statistical expertise.

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Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• multicenter study

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Shijie Zhang and Huafei Shao are co-first authors.

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Zhang, S., Shao, H., Li, W. et al. Intra- and peritumoral radiomics for predicting malignant BiRADS category 4 breast lesions on contrast-enhanced spectral mammography: a multicenter study. Eur Radiol 33, 5411–5422 (2023). https://doi.org/10.1007/s00330-023-09513-3

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