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Utilizing grayscale ultrasound-based radiomics nomogram for preoperative identification of triple negative breast cancer

  • Breast Radiology
  • Published:
La radiologia medica Aims and scope Submit manuscript

Abstract

Purpose

This study aimed to develop a radiomics nomogram based on grayscale ultrasound (US) to distinguish triple-negative breast cancer (TNBC) from non-triple-negative breast cancer (NTNBC) prior to surgery.

Methods

A retrospective analysis of 454 breast carcinoma patients confirmed by pathology was conducted, with 317 patients in the training dataset (59 with TNBC) and 137 patients in the validation dataset (27 with TNBC). Clinical information, conventional US features, and radiomics features were collected, and the Radscore model was constructed after feature selection. Independent risk factors were identified using univariate and multivariate logistic regression analysis. The nomogram model was assessed using the receiver operating characteristic (ROC) curve analysis, calibration curve, decision curve analysis (DCA), net reclassification improvement (NRI) and integrated discrimination improvement (IDI).

Results

Tumor shape, margin, and calcification were independent risk factors in the clinical prediction model. Additionally, 16 radiomics features were selected to construct the Radscore model out of a total of 474 extracted features. The radiomics nomogram model, which incorporated tumor shape, margin, calcification, and Radscore, achieved an AUC value of 0.837 in the training dataset and 0.813 in the validation dataset, outperforming both the Radscore and clinical models in terms of predictive performance. The significant improvement of NRI and IDI indicated that the Radscore may be useful biomarkers for TNBC.

Conclusion

The US-based radiomics nomogram showed satisfactory preoperative prediction of TNBC.

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Abbreviations

TNBC:

Triple-negative breast cancer

NTNBC:

Non-triple-negative breast cancer

2D:

Two-dimensional

US:

Ultrasound

CUS:

Conventional ultrasound

Radscore:

Radiomic signature

AIC:

Akaike information criterion

DCA:

Decision curve analysis

ICC:

Interclass correlation coefficient

LASSO:

Least absolute shrinkage and selection operator

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Funding

This study has received funding by Wu Jieping Medical Foundation (Project No. 320.6750.19089–40); Jilin Province Science and Technology Development Plan (Project No. 20220203113SF); National Natural Science Foundation of China (Project No. 52275006); Climbing Foundation Clinical Research Project of National Cancer Center (Project No. NCC201917B04).

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Authors and Affiliations

  1. Department of Radiology, China-Japan Union Hospital of Jilin University, Xiantai Street, Changchun, 130033, China

    Maolin Xu & Guifeng Liu

  2. Department of Ultrasound, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, 116 Zhuodaoquan South Road, Wuhan, 430079, China

    Shue Zeng & Fang Li

Authors
  1. Maolin Xu
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  2. Shue Zeng
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  3. Fang Li
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  4. Guifeng Liu
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Contributions

The study was conceived and designed by FL and G-FL. FL and S-EZ acquired the data, while M-LX, FL, and S-EZ analyzed and interpreted the data. M-LX and FL drafted the manuscript, which was critically revised for important intellectual content by M-LX, FL, and G-FL. All authors contributed to the article and approved the submitted version.

Corresponding authors

Correspondence to Fang Li or Guifeng Liu.

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Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Ethical approval

This retrospective study was approved by the ethics committee of the Hubei Cancer Hospital (No.: LLHBCH2021YN-001).

Consent to participate

Written informed consent was waived by the Institutional Review Board.

Consent to publish

As this study was retrospective in nature, the requirement for obtaining informed consent for publication was waived.

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Xu, M., Zeng, S., Li, F. et al. Utilizing grayscale ultrasound-based radiomics nomogram for preoperative identification of triple negative breast cancer. Radiol med 129, 29–37 (2024). https://doi.org/10.1007/s11547-023-01739-x

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  • DOI: https://doi.org/10.1007/s11547-023-01739-x

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