Skip to main content

Advertisement

SpringerLink
Log in

Radiomics model based on shear-wave elastography in the assessment of axillary lymph node status in early-stage breast cancer

  • Breast
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To develop and validate an ultrasound elastography radiomics nomogram for preoperative evaluation of the axillary lymph node (ALN) burden in early-stage breast cancer.

Methods

Data of 303 patients from hospital #1 (training cohort) and 130 cases from hospital #2 (external validation cohort) between Jun 2016 and May 2019 were enrolled. Radiomics features were extracted from shear-wave elastography (SWE) and corresponding B-mode ultrasound (BMUS) images. The minimum redundancy maximum relevance and least absolute shrinkage and selection operator algorithms were used to select ALN status–related features. Proportional odds ordinal logistic regression was performed using the radiomics signature together with clinical data, and an ordinal nomogram was subsequently developed. We evaluated its performance using C-index and calibration.

Results

SWE signature, US-reported LN status, and molecular subtype were independent risk factors associated with ALN status. The nomogram based on these variables showed good discrimination in the training (overall C-index: 0.842; 95%CI, 0.773–0.879) and the validation set (overall C-index: 0.822; 95%CI, 0.765–0.838). For discriminating between disease-free axilla (N0) and any axillary metastasis (N + (≥ 1)), it achieved a C-index of 0.845 (95%CI, 0.777–0.914) for the training cohort and 0.817 (95%CI, 0.769–0.865) for the validation cohort. The tool could also discriminate between low (N + (1–2)) and heavy metastatic ALN burden (N + (≥ 3)), with a C-index of 0.827 (95%CI, 0.742–0.913) in the training cohort and 0.810 (95%CI, 0.755–0.864) in the validation cohort.

Conclusion

The radiomics model shows favourable predictive ability for ALN staging in patients with early-stage breast cancer, which could provide incremental information for decision-making.

Key Points

• Radiomics analysis helps radiologists to evaluate the axillary lymph node status of breast cancer with accuracy.

• This multicentre retrospective study showed that radiomics nomogram based on shear-wave elastography provides incremental information for risk stratification.

• Treatment can be given with more precision based on the model.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Abbreviations

ALN:

Axillary lymph node

AUC:

Area under the receiver operating characteristic curve

BMUS:

B-mode ultrasound

CI:

Confidence interval

HER2:

Human epidermal growth factor receptor 2

IHC:

Immunohistochemical

LASSO:

Least absolute shrinkage and selection operator

MRMR:

Minimum redundancy maximum relevance

SLN:

Sentinel lymph node

SWE:

Shear-wave elastography

References

  1. Ahmed M, Purushotham AD, Douek M (2014) Novel techniques for sentinel lymph node biopsy in breast cancer: a systematic review. Lancet Oncol 15:e351–e362

    Article  Google Scholar 

  2. Cardoso F, Kyriakides S, Ohno S et al (2019) Early breast cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 30:1194–1220

    Article  CAS  Google Scholar 

  3. Giuliano AE, Ballman KV, McCall L et al (2017) Effect of axillary dissection vs no axillary dissection on 10-year overall survival among women with invasive breast cancer and sentinel node metastasis. JAMA 318:918

    Article  Google Scholar 

  4. Giuliano AE, Hunt KK, Ballman KV et al (2011) Axillary dissection vs no axillary dissection in women with invasive breast cancer and sentinel node metastasis: a randomized clinical trial. JAMA 305:569–575

    Article  CAS  Google Scholar 

  5. Zheng X, Yao Z, Huang Y et al (2020) Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer. Nat Commun 11:1236

    Article  CAS  Google Scholar 

  6. Lim GH, Upadhyaya VS, Acosta HA et al (2018) Preoperative predictors of high and low axillary nodal burden in Z0011 eligible breast cancer patients with a positive lymph node needle biopsy result. Eur J Surg Oncol 44:945–950

    Article  Google Scholar 

  7. Chu KU, Turner RR, Hansen NM, Brennan MB, Giuliano AE (1999) Sentinel node metastasis in patients with breast carcinoma accurately predicts immunohistochemically detectable nonsentinel node metastasis. Ann Surg Oncol 6:756–761

    Article  CAS  Google Scholar 

  8. Kamath VJ, Giuliano R, Dauway EL et al (2001) Characteristics of the sentinel lymph node in breast cancer predict further involvement of higher-echelon nodes in the axilla: a study to evaluate the need for complete axillary lymph node dissection. Arch Surg 136:688–692

    Article  CAS  Google Scholar 

  9. Kim GR, Choi JS, Han BK et al (2018) Preoperative axillary US in early-stage breast cancer: potential to prevent unnecessary axillary lymph node dissection. Radiology 288:55–63

    Article  Google Scholar 

  10. Youk JH, Kwak JY, Lee E, Son EJ, Kim JA (2020) Grayscale ultrasound radiomic features and shear-wave elastography radiomic features in benign and malignant breast masses. Ultraschall Med 41:390–396

    Article  Google Scholar 

  11. Berg WA, Cosgrove DO, Doré CJ et al (2012) Shear-wave elastography improves the specificity of breast US: the BE1 multinational study of 939 masses. Radiology 262:435–449

    Article  Google Scholar 

  12. Evans A, Rauchhaus P, Whelehan P et al (2014) Does shear wave ultrasound independently predict axillary lymph node metastasis in women with invasive breast cancer? Breast Cancer Res Treat 143:153–157

    Article  Google Scholar 

  13. Zhao Q, Sun JW, Zhou H et al (2018) Pre-operative conventional ultrasound and sonoelastography evaluation for predicting axillary lymph node metastasis in patients with malignant breast lesions. Ultrasound Med Biol 44:2587–2595

    Article  Google Scholar 

  14. Youk JH, Son EJ, Kim JA, Gweon HM (2017) Pre-operative evaluation of axillary lymph node status in patients with suspected breast cancer using shear wave elastography. Ultrasound Med Biol 43:1581–1586

    Article  Google Scholar 

  15. Bi WL, Hosny A, Schabath MB et al (2019) Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J Clin 69:127–157

    PubMed  PubMed Central  Google Scholar 

  16. Yu F, Wang J, Ye X et al (2019) Ultrasound-based radiomics nomogram: a potential biomarker to predict axillary lymph node metastasis in early-stage invasive breast cancer. Eur J Radiol 119:108658

    Article  Google Scholar 

  17. Han L, Zhu Y, Liu Z et al (2019) Radiomic nomogram for prediction of axillary lymph node metastasis in breast cancer. Eur Radiol 29:3820–3829

    Article  Google Scholar 

  18. Koelliker SL, Chung MA, Mainiero MB, Steinhoff MM, Cady B (2008) Axillary lymph nodes: US-guided fine-needle aspiration for initial staging of breast cancer-correlation with primary tumor size. Radiology 246:81–89

    Article  Google Scholar 

  19. Curigliano G, Burstein HJ, Winer EP et al (2017) De-escalating and escalating treatments for early-stage breast cancer: the St. Gallen International Expert Consensus Conference on the Primary Therapy of Early Breast Cancer 2017. Ann Oncol 28:1700–1712

    Article  CAS  Google Scholar 

  20. Zhou J, Zhan W, Chang C et al (2014) Breast lesions: evaluation with shear wave elastography, with special emphasis on the “stiff rim” sign. Radiology 272:63–72

    Article  Google Scholar 

  21. van Griethuysen J, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104–e107

    Article  Google Scholar 

  22. Sauerbrei W, Royston P, Binder H (2007) Selection of important variables and determination of functional form for continuous predictors in multivariable model building. Stat Med 26:5512–5528

    Article  Google Scholar 

  23. Alba AC, Agoritsas T, Walsh M et al (2017) Discrimination and calibration of clinical prediction models: users’ guides to the medical literature. JAMA 318:1377–1384

    Article  Google Scholar 

  24. Pencina MJ, Fine JP, D’Agostino RB (2017) Discrimination slope and integrated discrimination improvement - properties, relationships and impact of calibration. Stat Med 36:4482–4490

    Article  Google Scholar 

  25. Harrell FE (2015) Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis. Springer, New York

    Book  Google Scholar 

  26. Farrell TPJ, Adams NC, Stenson M et al (2015) The Z0011 Trial: is this the end of axillary ultrasound in the pre-operative assessment of breast cancer patients? Eur Radiol 25:2682–2687

    Article  CAS  Google Scholar 

  27. Krag D, Weaver D, Ashikaga T et al (1998) The sentinel node in breast cancer–a multicenter validation study. N Engl J Med 339:941–946

    Article  CAS  Google Scholar 

  28. Krag DN, Anderson SJ, Julian TB et al (2007) Technical outcomes of sentinel-lymph-node resection and conventional axillary-lymph-node dissection in patients with clinically node-negative breast cancer: results from the NSABP B-32 randomized phase III trial. Lancet Oncol 8:881–888

    Article  CAS  Google Scholar 

  29. Pesek S, Ashikaga T, Krag LE, Krag D (2012) The false-negative rate of sentinel node biopsy in patients with breast cancer: a meta-analysis. World J Surg 36:2239–2251

    Article  Google Scholar 

  30. Goldhirsch A, Winer EP, Coates AS et al (2013) Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013. Ann Oncol 24:2206–2223

    Article  CAS  Google Scholar 

  31. Van Calster B, VandenBempt I, Drijkoningen M et al (2009) Axillary lymph node status of operable breast cancers by combined steroid receptor and HER-2 status: triple positive tumours are more likely lymph node positive. Breast Cancer Res Tr 113:181–187

    Article  Google Scholar 

  32. Voduc KD, Cheang MCU, Tyldesley S et al (2010) Breast cancer subtypes and the risk of local and regional relapse. J Clin Oncol 28:1684–1691

    Article  Google Scholar 

  33. Pinker K, Chin J, Melsaether AN, Morris EA, Moy L (2018) Precision medicine and radiogenomics in breast cancer: new approaches toward diagnosis and treatment. Radiology 287:732–747

    Article  Google Scholar 

  34. Lam SW, Jimenez CR, Boven E (2014) Breast cancer classification by proteomic technologies: current state of knowledge. Cancer Treat Rev 40:129–138

    Article  CAS  Google Scholar 

  35. Huber KE, Carey LA, Wazer DE (2009) Breast cancer molecular subtypes in patients with locally advanced disease: impact on prognosis, patterns of recurrence, and response to therapy. Semin Radiat Oncol 19:204–210

    Article  Google Scholar 

  36. Guiu S, Michiels S, André F et al (2012) Molecular subclasses of breast cancer: how do we define them? The IMPAKT 2012 Working Group Statement. Ann Oncol 23:2997–3006

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors thank all radiologists of the two hospitals for assisting with collection of the imaging data used in this study. The manuscript has been published as a preprint (DOI:https://doi.org/10.21203/rs.3.rs-75554/v1, LICENSE: under a CC BY 4.0 License), but is not being considered for publication by any other journal.

Funding

This work was supported by the project funded by the China Postdoctoral Science Foundation (2020M682422), Wuhan Science and Technology Bureau (No. 2017060201010181), and Health Commission of Hubei Province (WJ2019M077, WJ2019H227).

Author information

Authors and Affiliations

  1. Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei Province, China

    Meng Jiang & Xin-Wu Cui

  2. Department of Geratology, Hubei Provincial Hospital of Integrated Chinese and Western Medicine, 11 Lingjiaohu Avenue, Wuhan, 430015, China

    Chang-Li Li

  3. Department of Medical Ultrasound, Yunnan Cancer Hospital & The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, China

    Xiao-Mao Luo & Zhi-Rui Chuan

  4. Department of Medical Ultrasound, Wuchang Hospital, Wuhan, 430030, China

    Rui-Xue Chen

  5. Department of Medical Ultrasound, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, 410013, Hunan, China

    Shi-Chu Tang

  6. Department of Artificial Intelligence, Julei Technology, Wuhan, 430030, China

    Wen-Zhi Lv

  7. Department of Internal Medicine, Hirslanden Clinic, Schänzlihalde 11, 3013, Bern, Switzerland

    Christoph F. Dietrich

Authors
  1. Meng Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chang-Li Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiao-Mao Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhi-Rui Chuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rui-Xue Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shi-Chu Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Wen-Zhi Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Xin-Wu Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Christoph F. Dietrich
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Xiao-Mao Luo or Xin-Wu Cui.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Professor Xin-Wu Cui.

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.

Methodology

• retrospective

• diagnostic or prognostic study

• multicentre study

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 1809 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jiang, M., Li, CL., Luo, XM. et al. Radiomics model based on shear-wave elastography in the assessment of axillary lymph node status in early-stage breast cancer. Eur Radiol 32, 2313–2325 (2022). https://doi.org/10.1007/s00330-021-08330-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-021-08330-w

Keywords

Search

Navigation