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Nomogram based on radiomics analysis of primary breast cancer ultrasound images: prediction of axillary lymph node tumor burden in patients

  • Breast
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Abstract

Objectives

To establish a prediction model for evaluating the axillary lymph node (ALN) status of patients with T1/T2 invasive breast cancer based on radiomics analysis of US images of primary breast lesions.

Methods

Between August 2016 and November 2018, a total of 343 patients with histologically proven malignant breast tumors were included in this study and randomly assigned to the training and validation groups at a ratio of 7:3. ALN tumor burden was defined as low (< 3 metastatic ALNs) or high (≥ 3 metastatic ALNs). Radiomics features were obtained using the PyRadiomics package, and the radiomics score was established by least absolute shrinkage and selection operator regression. A nomogram combining the breast cancer US radiomics score with patient age and lesion size was generated based on the multivariate logistic regression results.

Results

In the training and validation cohorts, 29.1% (69/237) and 32.08% (34/106) of patients were pathologically diagnosed with more than 2 metastatic ALNs, respectively. The radiomics score consisted of 16 US features, and patient age and lesion diameter identified by US were included to construct the model. The AUC of the model was 0.846 (95% CI, 0.790–0.902) for the training cohort and 0.733 (95% CI, 0.613–0.852) for the validation cohort. The calibration curves showed good agreement between the predictions and observations.

Conclusions

Our novel nomogram demonstrates high accuracy in predicting ALN tumor burden in breast cancer patients. We also suggest further development of PyRadiomics to improve US radiomics.

Key Points

• A nomogram based on US was developed to predict ALN tumor burden (low, < 3 metastatic ALNs; high, ≥ 3 metastatic ALNs).

• The nomogram could assist clinicians in evaluating treatment strategies for T1/T2 invasive breast cancer.

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Abbreviations

2D:

Two-dimensional

ALN:

Axillary lymph node

ALND:

Axillary lymph node dissection

ASCO:

American Society of Clinical Oncology

LASSO:

Least absolute shrinkage and selection operator

SLNB:

Sentinel lymph node biopsy

VIF:

Variance inflation factor

References

  1. Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM (2010) Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer 127:2893–2917

    Article  CAS  Google Scholar 

  2. Zuo TT, Zheng RS, Zeng HM, Zhang SW, Chen WQ (2017) Female breast cancer incidence and mortality in China, 2013. Thorac Cancer 8:214–218

    Article  Google Scholar 

  3. DeSantis C, Ma J, Bryan L, Jemal A (2014) Breast cancer statistics, 2013. CA Cancer J Clin 64:52–62

    Article  Google Scholar 

  4. Harbeck N, Gnant M (2017) Breast cancer. Lancet 389:1134–1150

    Article  Google Scholar 

  5. Rao R, Euhus D, Mayo HG, Balch C (2013) Axillary node interventions in breast cancer: a systematic review. JAMA 310:1385–1394

    Article  CAS  Google Scholar 

  6. Lucci A, McCall LM, Beitsch PD et al (2007) Surgical complications associated with sentinel lymph node dissection (SLND) plus axillary lymph node dissection compared with SLND alone in the American College of Surgeons Oncology Group Trial Z0011. J Clin Oncol 25:3657–3663

    Article  Google Scholar 

  7. Abass MO, Gismalla MDA, Alsheikh AA, Elhassan MMA (2018) Axillary lymph node dissection for breast cancer: efficacy and complication in developing countries. J Glob Oncol 4:1–8

    PubMed  Google Scholar 

  8. Manca G, Rubello D, Tardelli E et al (2016) Sentinel lymph node biopsy in breast cancer: indications, contraindications, and controversies. Clin Nucl Med 41:126–133

    Article  Google Scholar 

  9. 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: the ACOSOG Z0011 (Alliance) randomized clinical trial. JAMA 318:918–926

    Article  Google Scholar 

  10. Lyman GH, Somerfield MR, Bosserman LD, Perkins CL, Weaver DL, Giuliano AE (2017) Sentinel lymph node biopsy for patients with early-stage breast cancer: American Society of Clinical Oncology clinical practice guideline update. J Clin Oncol 35:561–564

    Article  Google Scholar 

  11. Valente SA, Levine GM, Silverstein MJ et al (2012) Accuracy of predicting axillary lymph node positivity by physical examination, mammography, ultrasonography, and magnetic resonance imaging. Ann Surg Oncol 19:1825–1830

    Article  Google Scholar 

  12. Alvarez S, Anorbe E, Alcorta P, Lopez F, Alonso I, Cortes J (2006) Role of sonography in the diagnosis of axillary lymph node metastases in breast cancer: a systematic review. AJR Am J Roentgenol 186:1342–1348

    Article  Google Scholar 

  13. Engohan-Aloghe C, Hottat N, Noel JC (2010) Accuracy of lymph nodes cell block preparation according to ultrasound features in preoperative staging of breast cancer. Diagn Cytopathol 38:5–8

    PubMed  Google Scholar 

  14. Cools-Lartigue J, Meterissian S (2012) Accuracy of axillary ultrasound in the diagnosis of nodal metastasis in invasive breast cancer: a review. World J Surg 36:46–54

    Article  Google Scholar 

  15. Chen X, He Y, Wang J et al (2018) Feasibility of using negative ultrasonography results of axillary lymph nodes to predict sentinel lymph node metastasis in breast cancer patients. Cancer Med. https://doi.org/10.1002/cam4.1606

  16. Zhu Y, Zhou W, Jia XH, Huang O, Zhan WW (2018) Preoperative axillary ultrasound in the selection of patients with a heavy axillary tumor burden in early-stage breast cancer: what leads to false-positive results? J Ultrasound Med 37:1357–1365

    Article  Google Scholar 

  17. Ahmed M, Jozsa F, Baker R, Rubio IT, Benson J, Douek M (2017) Meta-analysis of tumour burden in pre-operative axillary ultrasound positive and negative breast cancer patients. Breast Cancer Res Treat 166:329–336

    Article  Google Scholar 

  18. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577

    Article  Google Scholar 

  19. Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762

    Article  Google Scholar 

  20. Valdora F, Houssami N, Rossi F, Calabrese M, Tagliafico AS (2018) Rapid review: radiomics and breast cancer. Breast Cancer Res Treat 169:217–229

    Article  Google Scholar 

  21. Carter CL, Allen C, Henson DE (1989) Relation of tumor size, lymph node status, and survival in 24,740 breast cancer cases. Cancer 63:181–187

    Article  CAS  Google Scholar 

  22. Weigel MT, Dowsett M (2010) Current and emerging biomarkers in breast cancer: prognosis and prediction. Endocr Relat Cancer 17:R245–R262

    Article  CAS  Google Scholar 

  23. Giuliano AE, Barth AM, Spivack B, Beitsch PD, Evans SW (1996) Incidence and predictors of axillary metastasis in T1 carcinoma of the breast. J Am Coll Surg 183:185–189

    CAS  PubMed  Google Scholar 

  24. Hu HT, Wang Z, Huang XW et al (2019) Ultrasound-based radiomics score: a potential biomarker for the prediction of microvascular invasion in hepatocellular carcinoma. Eur Radiol 29:2890–2901

    Article  Google Scholar 

  25. Qiu SQ, Zeng HC, Zhang F et al (2016) A nomogram to predict the probability of axillary lymph node metastasis in early breast cancer patients with positive axillary ultrasound. Sci Rep 6:21196

    Article  CAS  Google Scholar 

  26. Xie X, Tan W, Chen B et al (2018) Preoperative prediction nomogram based on primary tumor miRNAs signature and clinical-related features for axillary lymph node metastasis in early-stage invasive breast cancer. Int J Cancer 142:1901–1910

    Article  CAS  Google Scholar 

  27. Yu FH, Wang JX, Ye XH, Deng J, Hang J, Yang B (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 

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

    Article  Google Scholar 

  29. Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc B 58:267–288

    Google Scholar 

  30. Meretoja TJ, Heikkila PS, Mansfield AS et al (2014) A predictive tool to estimate the risk of axillary metastases in breast cancer patients with negative axillary ultrasound. Ann Surg Oncol 21:2229–2236

    Article  CAS  Google Scholar 

  31. Rizzo S, Botta F, Raimondi S et al (2018) Radiomics: the facts and the challenges of image analysis. Eur Radiol Exp 2:36

    Article  Google Scholar 

  32. Brattain LJ, Telfer BA, Dhyani M, Grajo JR, Samir AE (2018) Machine learning for medical ultrasound: status, methods, and future opportunities. Abdom Radiol (NY) 43:786–799

    Article  Google Scholar 

  33. Veeraraghavan H, Dashevsky BZ, Onishi N et al (2018) Appearance constrained semi-automatic segmentation from DCE-MRI is reproducible and feasible for breast cancer radiomics: a feasibility study. Sci Rep 8:4838

    Article  Google Scholar 

Download references

Acknowledgments

We thank Rui Liu for his suggestion on application of PyRadiomics.

Funding

This study has received funding by Special Fund for National Natural Sciences Foundation of China (81771855) and CAMS Innovation Fund for Medical Sciences (2017-I2M-1-006).

Author information

Author notes

  1. Yuanjing Gao and Yanwen Luo contributed equally to this work.

Authors and Affiliations

  1. Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Shuaifuyuan 1st, Dongcheng District, Beijing, 100730, China

    Yuanjing Gao, Yanwen Luo, Chenyang Zhao, Mengsu Xiao, Li Ma, Wenbo Li, Jing Qin, Qingli Zhu & Yuxin Jiang

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  1. Yuanjing Gao
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Correspondence to Qingli Zhu or Yuxin Jiang.

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The scientific guarantor of this publication is Qingli Zhu and Yuxin Jiang.

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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

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Gao, Y., Luo, Y., Zhao, C. et al. Nomogram based on radiomics analysis of primary breast cancer ultrasound images: prediction of axillary lymph node tumor burden in patients. Eur Radiol 31, 928–937 (2021). https://doi.org/10.1007/s00330-020-07181-1

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  • DOI: https://doi.org/10.1007/s00330-020-07181-1

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