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Radiomics nomogram of contrast-enhanced spectral mammography for prediction of axillary lymph node metastasis in breast cancer: a multicenter study

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Abstract

Objective

This study aims to establish and validate a radiomics nomogram based on contrast-enhanced spectral mammography (CESM) for prediction of axillary lymph node (ALN) metastasis in breast cancer.

Methods

This retrospective study included 394 patients with breast cancer who underwent CESM examination in two hospitals. The least absolute shrinkage and selection operator (LASSO) logistic regression was established for feature selection and utilized to construct radiomics signature. The nomogram model included the radiomics signature and independent clinical factors. The receiver operating characteristic (ROC) curves were used to confirm the performance of the nomogram in training and validation sets.

Results

The nomogram model, which includes the radiomics signature and the CESM-reported lymph node status, has areas under the ROC curves of 0.774 (95% confidence interval (CI) 0.689–0.858), 0.767 (95% CI 0.583–0.857), and 0.79 (95% CI 0.63–0.94) in the training, internal validation, and external validation sets, respectively. We identified the cutoff score in the radiomics nomogram as − 1.49, which corresponded to a total point of 49 that could diagnose ALN metastasis with a sensitivity of > 95%.

Conclusions

The CESM-based radiomics nomogram is a noninvasive predictive tool that shows good application prospects in the preoperative prediction of ALN metastasis in breast cancer.

Key Points

• The CESM-based radiomics nomogram shows good performance in predicting ALN metastasis in breast cancer.

• The application of radiomics nomogram in this study provides a new approach for establishing a prediction model with multiple characteristics.

• The nomogram has good application prospects in assisting clinical decision makers.

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Abbreviations

ALN:

Axillary lymph node

AUC:

Area under the curve

CESM:

Contrast-enhanced spectral mammography

GLCM:

Gray-level co-occurrence matrix

GLSZM:

Gray-level size zone matrix (form factor matrix)

ICC:

Intraclass correlation coefficient

LASSO:

Least absolute shrinkage and selection operator

RLM:

Run length matrix

ROC:

Receiver operating characteristic

ROI:

Region of interest

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Funding

This study was supported by the Shandong Medical and Health Science and Technology Development Plan (2016WS0713), Natural Science Foundation of Shandong Province of China (ZR2017PH043, ZR2017LH053), Natural Science Foundation of China (81671654), and Special Fund of China Medical Education Association (2016SKT-M034).

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Authors and Affiliations

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

    Ning Mao, Qinglin Wang, Meijie Liu, Heng Ma, Jianjun Dong, Kaili Che, Zhongyi Wang & Haizhu Xie

  2. Department of Radiology, Peking University People’s Hospital, No. 11 Xizhimen South Street, Beijing, 100044, People’s Republic of China

    Ping Yin & Nan Hong

  3. Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200000, People’s Republic of China

    Qin Li

  4. GE Healthcare, Shanghai, 200000, People’s Republic of China

    Shaofeng Duan & Xuexi Zhang

Authors
  1. Ning Mao
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  2. Ping Yin
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  3. Qin Li
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  4. Qinglin Wang
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  7. Jianjun Dong
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  8. Kaili Che
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  9. Zhongyi Wang
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  10. Shaofeng Duan
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  11. Xuexi Zhang
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  12. Nan Hong
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Corresponding authors

Correspondence to Nan Hong or Haizhu Xie.

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Guarantor

The scientific guarantor of this publication is Haizhu Xie.

Conflict of interest

Two of the authors of this manuscript (Shaofeng Duan, Xuexi Zhang) are employees GE Healthcare. 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

One of the authors, Shaofeng Duan, has 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.

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Mao, N., Yin, P., Li, Q. et al. Radiomics nomogram of contrast-enhanced spectral mammography for prediction of axillary lymph node metastasis in breast cancer: a multicenter study. Eur Radiol 30, 6732–6739 (2020). https://doi.org/10.1007/s00330-020-07016-z

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  • DOI: https://doi.org/10.1007/s00330-020-07016-z

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