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Ultrasound-based radiomics nomogram for predicting axillary lymph node metastasis in early-stage breast cancer

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

Abstract

Purpose

We aimed at assessing the predictive ability of ultrasound-based radiomics combined with clinical characteristics for axillary lymph node (ALN) status in early-stage breast cancer patients and to compare performance in different peritumoral regions.

Materials and methods

A total of 755 patients (527 in the primary cohort and 228 in the external validation cohort) were enrolled in this study. Ultrasound images for all patients were acquired and radiomics analysis performed for intratumoral and different peritumoral regions. The MRMR and LASSO regression analyses were performed on extracted features from the primary cohort to construct a radiomics signature formula combined with clinical characteristics. Pearson’s coefficient and the variance inflation factor (VIF) were performed to check the correlation and the multicollinearity among the final predictors. The best performing model was selected to develop a nomogram, which was established by performing binary logistic regression and acquiring cut-off values based on the corresponding nomogram scores of the masses.

Results

Among all the radiomics models, the “Mass + Margin3mm” model exhibited the best performance. The areas under the curves (AUC) of the nomogram in the primary and external validation cohorts were 0.906 (95% confidence intervals [CI] 0.882–0.930) and 0.922 (95% CI 0.894–0.960), respectively. They both showed good calibrations. The nomogram exhibited a good ability to discriminate between positive and negative lymph nodes (AUC: 0.853 (95% CI 0.816–0.889) in primary cohort, 0.870 (95% CI 0.818–0.922) in validation cohort), and between low-volume and high-volume lymph nodes (AUC: 0.832 (95% CI 0.781–0.884) in primary cohort, 0.911 (95% CI 0.858–0.964) in validation cohort).

Conclusions

The established nomogram is a prospective clinical prediction tool for non-invasive assessment of ALN status. It has the ability to enhance the accuracy of early-stage breast cancer treatment.

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Data availability

The datasets generated during and/or analysed during the current study are not publicly available due to patient permission was not sought for the sharing of data at the time of recruitment, but are available from the corresponding author on reasonable request.

Abbreviations

ALN:

Axillary lymph node

ALND:

Axillary lymph node dissection

ALNM:

Axillary lymph node metastasis

AUC:

Area under curve

BI-RADS:

The breast imaging reporting and data system

BMUS:

B-mode ultrasound

DCA:

Decision curve analysis

DFS:

Disease-free survival

ER:

Estrogenic receptor

FNR:

False negative rate

FPR:

False positive rate

HER2:

Human epidermal growth factor receptor-2

ICCs:

Interclass correlation coefficients

LASSO:

Least absolute shrinkage and selection operator

LRNI:

Locoregional nodal irradiation

MRMR:

Max-relevance and min-redundancy

NPV:

Negative predictive value

PPV:

Positive Predictive Value

PR:

Progesterone receptor

ROC:

Receiver operating characteristic

ROI:

Region of interest

SLNB:

Sentinel lymph node biopsy

SLND:

Sentinel lymph node dissection

US:

Ultrasound

VIF:

Variance inflation factor

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Acknowledgments

The authors thank Home for Researchers editorial team (https://www.home-for-researchers.com) for language editing service.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. In-Patient Ultrasound Department, The Second Affiliated Hospital of Harbin Medical University, 246 Xuefu Road, NanGang District, Harbin, 150086, China

    Wuyue Zhang, Siying Wang, Yichun Wang, Jiawei Sun, Hong Wei, Weili Xue, Xueying Dong & Xiaolei Wang

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  1. Wuyue Zhang
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  2. Siying Wang
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  3. Yichun Wang
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  4. Jiawei Sun
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  7. Xueying Dong
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  8. Xiaolei Wang
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Contributions

All authors contributed to the study conception and design. Data collection and material preparation and analysis were performed by JS, HW, XD, WX, SW and YW. The first draft of the manuscript was written by WZ and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Xiaolei Wang.

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Conflict of interest

The authors declare that they have no conflict of interest. The authors have no relevant financial or non-financial interests to disclose.

Ethical approval

This retrospective study was approved by the ethical committee and informed consents were obtained from all patients (approval number: KY2022-301).

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Zhang, W., Wang, S., Wang, Y. et al. Ultrasound-based radiomics nomogram for predicting axillary lymph node metastasis in early-stage breast cancer. Radiol med 129, 211–221 (2024). https://doi.org/10.1007/s11547-024-01768-0

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