Radiomic nomogram for prediction of axillary lymph node metastasis in breast cancer
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To develop a radiomic nomogram for preoperative prediction of axillary lymph node (LN) metastasis in breast cancer patients.
Preoperative magnetic resonance imaging data from 411 breast cancer patients was studied. Patients were assigned to either a training cohort (n = 279) or a validation cohort (n = 132). Eight hundred eight radiomic features were extracted from the first phase of T1-DCE images. A support vector machine was used to develop a radiomic signature, and logistic regression was used to develop a nomogram.
The radiomic signature based on 12 LN status–related features was constructed to predict LN metastasis, its prediction ability was moderate, with an area under the curve (AUC) of 0.76 and 0.78 in training and validation cohorts, respectively. Based on a radiomic signature and clinical features, a nomogram was developed and showed excellent predictive ability for LN metastasis (AUC 0.84 and 0.87 in training and validation sets, respectively). Another radiomic signature was constructed to distinguish the number of metastatic LNs (less than 2 positive nodes/more than 2 positive nodes), which also showed moderate performance (AUC 0.79).
We developed a nomogram and a radiomic signature that can be used to identify LN metastasis and distinguish the number of metastatic LNs (less than 2 positive nodes/more than 2 positive nodes). Both nomogram and radiomic signature can be used as tools to assist clinicians in assessing LN metastasis in breast cancer patients.
• ALNM is an important factor affecting breast cancer patients’ treatment and prognosis.
• Traditional imaging examinations have limited value for evaluating axillary LNs status.
• We developed a radiomic nomogram based on MR imagings to predict LN metastasis.
KeywordsBreast cancer Axillary lymph node metastasis Radiomics Preoperative prediction MRI
Axillary lymph node dissection
Axillary lymph node metastasis
Area under the curve
Gray level co-occurrence matrix
Gray level run length matrix
Gray level size zone matrix
Human epidermal growth factor receptor 2
Receiver operating characteristic
Sentinel lymph node biopsy
T1-weighted images of dynamic contrast enhanced
This study has received funding by Special Fund for Research in the Public Interest of China (201402020), National Key R&D Program of China (2017YFA0205200, 2017YFC1308700, 2017YFC1308701, 2017YFC1309100, 2016YFC0103803), National Natural Science Foundation of China (81227901, 81771924, 81501616, 81671851, 81671854, and 81527805), the Beijing Natural Science Foundation (L182061), the Bureau of International Cooperation of Chinese Academy of Sciences (173211KYSB20160053), the Science and Technology Service Network Initiative of the Chinese Academy of Sciences (KFJ-SW-STS-160), the Beijing Municipal Science and Technology Commission (Z171100000117023, Z161100002616022), the Instrument Developing Project of the Chinese Academy of Sciences (YZ201502), and the Youth Innovation Promotion Association CAS (2017175).
Compliance with ethical standards
The scientific guarantor of this publication is Yahong Luo.
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
One of the authors has significant statistical expertise.
Written informed consent was waived by the Institutional Review Board.
Institutional Review Board approval was obtained.
• diagnostic study
• performed at one institution
- 1.Ferlay J, Soerjomataram I, Dikshit R et al (2015) Cancer incidence and mortality worldwide: sources, methods and major patterns in globocan 2012. Int J Cancer 136:E359Google Scholar
- 23.Fan M, Wu G, Cheng H, Zhang J, Shao G, Li L (2017) Radiomic analysis of DCE-MRI for prediction of response to neoadjuvant chemotherapy in breast cancer patients. Eur J Radiol 94:140–147Google Scholar
- 27.van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104–e107Google Scholar
- 28.Lakhani SR, Ellis IO, Schnitt SJ, Tan PH, van de Vijver MJ (2012) WHO classification of tumours of the breast. International Agency for Research on Cancer, LyonGoogle Scholar
- 29.Bevilacqua JL, Kattan MW, Fey JV, Cody HS 3rd, Borgen PI, Van Zee KJ (2007) Doctor, what are my chances of having a positive sentinel node? A validated nomogram for risk estimation. J Clin Oncol 25:3670–3679Google Scholar