Advertisement

European Radiology

, Volume 28, Issue 2, pp 582–591 | Cite as

Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI

  • Yuhao Dong
  • Qianjin Feng
  • Wei Yang
  • Zixiao Lu
  • Chunyan Deng
  • Lu Zhang
  • Zhouyang Lian
  • Jing Liu
  • Xiaoning Luo
  • Shufang Pei
  • Xiaokai Mo
  • Wenhui Huang
  • Changhong Liang
  • Bin Zhang
  • Shuixing ZhangEmail author
Breast

Abstract

Objectives

To predict sentinel lymph node (SLN) metastasis in breast cancer patients using radiomics based on T2-weighted fat suppression (T2-FS) and diffusion-weighted MRI (DWI).

Methods

We enrolled 146 patients with histologically proven breast cancer. All underwent pretreatment T2-FS and DWI MRI scan. In all, 10,962 texture and four non-texture features were extracted for each patient. The 0.623 + bootstrap method and the area under the curve (AUC) were used to select the features. We constructed ten logistic regression models (orders of 1–10) based on different combination of image features using stepwise forward method.

Results

For T2-FS, model 10 with ten features yielded the highest AUC of 0.847 in the training set and 0.770 in the validation set. For DWI, model 8 with eight features reached the highest AUC of 0.847 in the training set and 0.787 in the validation set. For joint T2-FS and DWI, model 10 with ten features yielded an AUC of 0.863 in the training set and 0.805 in the validation set.

Conclusions

Full utilisation of breast cancer-specific textural features extracted from anatomical and functional MRI images improves the performance of radiomics in predicting SLN metastasis, providing a non-invasive approach in clinical practice.

Key Points

SLN biopsy to access breast cancer metastasis has multiple complications.

• Radiomics uses features extracted from medical images to characterise intratumour heterogeneity.

• We combined T 2 -FS and DWI textural features to predict SLN metastasis non-invasively.

Keywords

Imaging Breast cancer Sentinel lymph node metastasis Radiomics Preoperative prediction 

Abbreviations

ALN

Axillary lymph node

AUC

Area under the curve

DWI

Diffusion-weighted MRI

ER

Oestrogen receptor

PR

Progesterone receptor

SLN

Sentinel lymph node

T2FS

T2-weighted fat suppression

Notes

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Shuixing Zhang.

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.

Funding

This study has received funding by the National Scientific Foundation of China (81571664), the Science and Technology Planning Project of Guangdong Province (2014A020212244, 2016A020216020, 2015B010131011) and The Science and Technology Project of Guangdong Province (2015B010131011).

Statistics and biometry

One of the authors has significant statistical expertise.

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

• performed at one institution

Supplementary material

330_2017_5005_MOESM1_ESM.docx (37 kb)
ESM 1 (DOCX 36 kb)

References

  1. 1.
    Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A (2015) Global cancer statistics, 2012. CA Cancer J Clin 65:87–108CrossRefPubMedGoogle Scholar
  2. 2.
    Qiu PF, Liu JJ, Wang YS, Yang GR, Liu YB, Sun X et al (2012) Risk factors for sentinel lymph node metastasis and validation study of the MSKCC nomogram in breast cancer patients. Jpn J Clin Oncol 42:1002–1007CrossRefPubMedGoogle Scholar
  3. 3.
    Veronesi U, Paganelli G, Viale G, Luini A, Zurrida S, Galimberti V et al (2003) A randomized comparison of sentinel-node biopsy with routine axillary dissection in breast cancer. N Engl J Med 349:546–553CrossRefPubMedGoogle Scholar
  4. 4.
    Lyman GH, Giuliano AE, Somerfield MR, Benson AR, Bodurka DC, Burstein HJ et al (2005) American Society of Clinical Oncology guideline recommendations for sentinel lymph node biopsy in early-stage breast cancer. J Clin Oncol 23:7703–7720CrossRefPubMedGoogle Scholar
  5. 5.
    Kootstra J, Hoekstra-Weebers JE, Rietman H, de Vries J, Baas P, Geertzen JH et al (2008) Quality of life after sentinel lymph node biopsy or axillary lymph node dissection in stage I/II breast cancer patients: a prospective longitudinal study. Ann Surg Oncol 15:2533–2541CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Chen JY, Chen JJ, Yang BL, Liu ZB, Huang XY, Liu GY et al (2012) Predicting sentinel lymph node metastasis in a Chinese breast cancer population: assessment of an existing nomogram and a new predictive nomogram. Breast Cancer Res Treat 135:839–848CrossRefPubMedGoogle Scholar
  7. 7.
    Nottegar A, Veronese N, Senthil M, Roumen RM, Stubbs B, Choi AH et al (2016) Extra-nodal extension of sentinel lymph node metastasis is a marker of poor prognosis in breast cancer patients: a systematic review and an exploratory meta-analysis. Eur J Surg Oncol 42:919–925CrossRefPubMedGoogle Scholar
  8. 8.
    La Verde N, Biagioli E, Gerardi C, Cordovana A, Casiraghi C, Floriani I et al (2016) Role of patient and tumor characteristics in sentinel lymph node metastasis in patients with luminal early breast cancer: an observational study. Springerplus 5:114CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Viale G, Zurrida S, Maiorano E, Mazzarol G, Pruneri G, Paganelli G et al (2005) Predicting the status of axillary sentinel lymph nodes in 4351 patients with invasive breast carcinoma treated in a single institution. Cancer 103:492–500CrossRefPubMedGoogle Scholar
  10. 10.
    Ozemir IA, Orhun K, Eren T, Baysal H, Sagiroglu J, Leblebici M et al (2016) Factors affecting sentinel lymph node metastasis in Turkish breast cancer patients: Predictive value of Ki-67 and the size of lymph node. Bratisl Lek Listy 117:436–441PubMedGoogle Scholar
  11. 11.
    Matsuzawa F, Omoto K, Einama T, Abe H, Suzuki T, Hamaguchi J et al (2015) Accurate evaluation of axillary sentinel lymph node metastasis using contrast-enhanced ultrasonography with Sonazoid in breast cancer: a preliminary clinical trial. Springerplus 4:509CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Omoto K, Matsunaga H, Take N, Hozumi Y, Takehara M, Omoto Y et al (2009) Sentinel node detection method using contrast-enhanced ultrasonography with sonazoid in breast cancer: preliminary clinical study. Ultrasound Med Biol 35:1249–1256CrossRefPubMedGoogle Scholar
  13. 13.
    Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006PubMedPubMedCentralGoogle Scholar
  14. 14.
    Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577CrossRefPubMedGoogle Scholar
  15. 15.
    Kickingereder P, Gotz M, Muschelli J, Wick A, Neuberger U, Shinohara RT et al (2016) Large-scale radiomic profiling of recurrent glioblastoma identifies an imaging predictor for stratifying anti-angiogenic treatment response. Clin Cancer Res 22:5765–5771CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Li H, Zhu Y, Burnside ES, Drukker K, Hoadley KA, Fan C et al (2016) MR imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of MammaPrint, Oncotype DX, and PAM50 gene assays. Radiology 281:382–391CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Huang Y, Liu Z, He L, Chen X, Pan D, Ma Z et al (2016) Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (I or II) non-small cell lung cancer. Radiology 281:947–957CrossRefPubMedGoogle Scholar
  18. 18.
    Prasanna P, Patel J, Partovi S, Madabhushi A, Tiwari P (2016) Radiomic features from the peritumoral brain parenchyma on treatment-naive multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: preliminary findings. Eur Radiol. doi: 10.1007/s00330-016-4637-3 PubMedGoogle Scholar
  19. 19.
    Aerts HJ, Grossmann P, Tan Y, Oxnard GG, Rizvi N, Schwartz LH et al (2016) Defining a radiomic response phenotype: a pilot study using targeted therapy in NSCLC. Sci Rep 6:33860CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Gnep K, Fargeas A, Gutierrez-Carvajal RE, Commandeur F, Mathieu R, Ospina JD et al (2017) Haralick textural features on T2-weighted MRI are associated with biochemical recurrence following radiotherapy for peripheral zone prostate cancer. J Magn Reson Imaging 45:103–117CrossRefPubMedGoogle Scholar
  21. 21.
    Huang YQ, Liang CH, He L, Tian J, Liang CS, Chen X et al (2016) Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol 34:2157–2164CrossRefPubMedGoogle Scholar
  22. 22.
    Reshef DN, Reshef YA, Finucane HK, Grossman SR, McVean G, Turnbaugh PJ et al (2011) Detecting novel associations in large data sets. Science 334:1518–1524CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Lucci A, McCall LM, Beitsch PD, Whitworth PW, Reintgen DS, Blumencranz PW 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 25:3657–3663CrossRefPubMedGoogle Scholar
  24. 24.
    Toshikawa C, Koyama Y, Nagahashi M, Tatsuda K, Moro K, Tsuchida J et al (2015) Predictive factors for non-sentinel lymph node metastasis in the case of positive sentinel lymph node metastasis in two or fewer nodes in breast cancer. J Clin Med Res 7:620–626CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Olson JA, McCall LM, Beitsch P, Whitworth PW et al (2008) Impact of immediate versus delayed axillary node dissection on surgical outcomes in breast cancer patients with positive sentinel nodes: results from the American College of Surgeons Oncology Group Trials Z0010 and Z0011. J Clin Oncol 26:3530–3535CrossRefPubMedGoogle Scholar
  26. 26.
    Yeniay L, Carti E, Karaca C, Zekioglu O, Yararbas U, Yilmaz R et al (2012) A new and simple predictive formula for non-sentinel lymph node metastasis in breast cancer patients with positive sentinel lymph nodes, and validation of 3 different nomograms in Turkish breast cancer patients. Breast Care (Basel) 7:397–402CrossRefGoogle Scholar
  27. 27.
    Bi X, Wang Y, Li M, Chen P, Zhou Z, Liu Y et al (2015) Validation of the Memorial Sloan Kettering Cancer Center nomogram for predicting non-sentinel lymph node metastasis in sentinel lymph node-positive breast-cancer patients. Onco Targets Ther 8:487–493PubMedPubMedCentralGoogle Scholar
  28. 28.
    Klar M, Foeldi M, Markert S, Gitsch G, Stickeler E, Watermann D (2009) Good prediction of the likelihood for sentinel lymph node metastasis by using the MSKCC nomogram in a German breast cancer population. Ann Surg Oncol 16:1136–1142CrossRefPubMedGoogle Scholar
  29. 29.
    Zhu L, Jin L, Li S, Chen K, Jia W, Shan Q et al (2013) Which nomogram is best for predicting non-sentinel lymph node metastasis in breast cancer patients? A meta-analysis. Breast Cancer Res Treat 137:783–795CrossRefPubMedGoogle Scholar
  30. 30.
    Fujii T, Yajima R, Tatsuki H, Suto T, Morita H, Tsutsumi S et al (2015) Significance of lymphatic invasion combined with size of primary tumor for predicting sentinel lymph node metastasis in patients with breast cancer. Anticancer Res 35:3581–3584PubMedGoogle Scholar
  31. 31.
    Nie K, Shi L, Chen Q, Hu X, Jabbour SK, Yue N et al (2016) Rectal cancer: assessment of neoadjuvant chemoradiation outcome based on radiomics of multiparametric MRI. Clin Cancer Res 22:5256–5264CrossRefPubMedGoogle Scholar
  32. 32.
    Gladwish A, Milosevic M, Fyles A, Xie J, Halankar J, Metser U et al (2016) Association of apparent diffusion coefficient with disease recurrence in patients with locally advanced cervical cancer treated with radical chemotherapy and radiation therapy. Radiology 279:158–166CrossRefPubMedGoogle Scholar
  33. 33.
    Ryoo SM, Jeon SB, Sohn CH, Ahn S, Han C, Lee BK et al (2015) Predicting outcome with diffusion-weighted imaging in cardiac arrest patients receiving hypothermia therapy: multicenter retrospective cohort study. Crit Care Med 43:2370–2377CrossRefPubMedGoogle Scholar
  34. 34.
    Giganti F, Orsenigo E, Esposito A, Chiari D, Salerno A, Ambrosi A et al (2015) Prognostic role of diffusion-weighted MR imaging for resectable gastric cancer. Radiology 276:444–452CrossRefPubMedGoogle Scholar
  35. 35.
    Schipper RJ, Paiman ML, Beets-Tan RG, Nelemans PJ, de Vries B, Heuts EM et al (2015) Diagnostic performance of dedicated axillary T2- and diffusion-weighted MR imaging for nodal staging in breast cancer. Radiology 275:345–355CrossRefPubMedGoogle Scholar
  36. 36.
    Yoon HJ, Kim Y, Kim BS (2015) Intratumoral metabolic heterogeneity predicts invasive components in breast ductal carcinoma in situ. Eur Radiol 25:3648–3658CrossRefPubMedGoogle Scholar
  37. 37.
    Mori N, Ota H, Mugikura S, Takasawa C, Tominaga J, Ishida T et al (2013) Detection of invasive components in cases of breast ductal carcinoma in situ on biopsy by using apparent diffusion coefficient MR parameters. Eur Radiol 23:2705–2712CrossRefPubMedGoogle Scholar

Copyright information

© European Society of Radiology 2017

Authors and Affiliations

  • Yuhao Dong
    • 1
    • 2
  • Qianjin Feng
    • 3
  • Wei Yang
    • 3
  • Zixiao Lu
    • 3
  • Chunyan Deng
    • 3
  • Lu Zhang
    • 1
  • Zhouyang Lian
    • 1
  • Jing Liu
    • 1
  • Xiaoning Luo
    • 1
  • Shufang Pei
    • 1
  • Xiaokai Mo
    • 1
    • 2
  • Wenhui Huang
    • 1
  • Changhong Liang
    • 1
  • Bin Zhang
    • 1
  • Shuixing Zhang
    • 1
    Email author
  1. 1.Department of RadiologyGuangdong General Hospital/Guangdong Academy of Medical SciencesGuangzhouPeople’s Republic of China
  2. 2.Graduate CollegeShantou University Medical CollegeShantouPeople’s Republic of China
  3. 3.The Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical EngineeringSouthern Medical UniversityGuangzhouPeople’s Republic of China

Personalised recommendations