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Prediction of human epidermal growth factor receptor 2 (HER2) status in breast cancer by mammographic radiomics features and clinical characteristics: a multicenter study

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Abstract

Objectives

To preoperatively evaluate the human epidermal growth factor 2 (HER2) status in breast cancer using mammographic radiomics features and clinical characteristics on a multi-vendor and multi-center basis.

Methods

This multi-center study included a cohort of 1512 Chinese female with invasive ductal carcinoma of no special type (IDC-NST) from two different hospitals and five devices (1332 from Institution A, used for training and testing the models, and 180 women from Institution B, as the external validation cohort). The Gradient Boosting Machine (GBM) was employed to establish radiomics and multiomics models. Model efficacy was evaluated by the area under the curve (AUC).

Results

The number of HER2-positive patients in the training, testing, and external validation cohort were 245(26.3%), 105 (26.3.8%), and 51(28.3%), respectively, with no statistical differences among the three cohorts (p = 0.842, chi-square test). The radiomics model, based solely on the radiomics features, achieved an AUC of 0.814 (95% CI, 0.784–0.844) in the training cohort, 0.776 (95% CI, 0.727–0.825) in the testing cohort, and 0.702 (95% CI, 0.614–0.790) in the external validation cohort. The multiomics model, incorporated radiomics features with clinical characteristics, consistently outperformed the radiomics model with AUC values of 0.838 (95% CI, 0.810–0.866) in the training cohort, 0.788 (95% CI, 0.741–0.835) in the testing cohort, and 0.722 (95% CI, 0.637–0.811) in the external validation cohort.

Conclusions

Our study demonstrates that a model based on radiomics features and clinical characteristics has the potential to accurately predict HER2 status of breast cancer patients across multiple devices and centers.

Clinical relevance statement

By predicting the HER2 status of breast cancer reliably, the presented model built upon radiomics features and clinical characteristics on a multi-vendor and multi-center basis can help in bolstering the model’s applicability and generalizability in real-world clinical scenarios.

Key Points

• The mammographic presentation of breast cancer is closely associated with the status of human epidermal growth factor receptor 2 (HER2).

• The radiomics model, based solely on radiomics features, exhibits sub-optimal performance in the external validation cohort.

• By combining radiomics features and clinical characteristics, the multiomics model can improve the prediction ability in external data.

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Abbreviations

AUC:

Area under the curve

CC:

Cranial caudal

CI:

Confidence interval

DM:

Digital mammography

GBM:

Gradient boosting machine

HER2:

Human epidermal growth factor 2

IDC-NST:

Invasive ductal carcinoma of no special type

LR:

Logistic regression

MLO:

Mediolateral oblique

ROI:

Region of interest

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Acknowledgements

We thank all authors who contributed to this study.

Funding

This project was supported by Clinical Research Plan of SHDC (grant no. SHDC2020CR4069), Medical Engineering Fund of Fudan University (grant no. yg2021-029), Shanghai Sailing Program (grant no. 21YF1404800), Youth Medical Talents–Medical Imaging Practitioner Program (grant no. 3030256001), and Shanghai Municipal Science and Technology Major Project (grant no. 2018shzdzx01).

Author information

Author notes

  1. Yalan Deng and Yiping Lu contributed equally to this work and should be regarded as the co-first authors.

Authors and Affiliations

  1. Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China

    Yalan Deng & Li Liu

  2. Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China

    Yalan Deng & Li Liu

  3. Department of Radiology, Huashan Hospital, Fudan University, Shanghai, 200040, China

    Yiping Lu, Xuanxuan Li, Yuqi Zhu, Yajing Zhao, Zhuoying Ruan, Nan Mei & Bo Yin

Authors
  1. Yalan Deng
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  4. Yuqi Zhu
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  8. Bo Yin
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  9. Li Liu
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Corresponding authors

Correspondence to Bo Yin or Li Liu.

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Guarantor

The scientific guarantor of this publication is Dr. Li Liu.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained from the Medical Ethics Committee of Fudan University Shanghai Cancer Center. The IRB number for this study is 1501143–8-NSFC. Written informed consent was waived.

Study subjects or cohorts overlap

No study subjects or cohorts have been previously reported.

Methodology

• retrospective

• cross sectional study

• multicenter study

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Deng, Y., Lu, Y., Li, X. et al. Prediction of human epidermal growth factor receptor 2 (HER2) status in breast cancer by mammographic radiomics features and clinical characteristics: a multicenter study. Eur Radiol (2024). https://doi.org/10.1007/s00330-024-10607-9

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  • DOI: https://doi.org/10.1007/s00330-024-10607-9

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