Radiomic nomogram for prediction of axillary lymph node metastasis in breast cancer

Objective To develop a radiomic nomogram for preoperative prediction of axillary lymph node (LN) metastasis in breast cancer patients. Methods Preoperative magnetic resonance imaging data from 411 breast cancer patients was studied. Patients were assigned to either a training cohort ( n  = 279) or a...

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Bibliographic Details
Published in:European radiology 2019-07, Vol.29 (7), p.3820-3829
Main Authors: Han, Lu, Zhu, Yongbei, Liu, Zhenyu, Yu, Tao, He, Cuiju, Jiang, Wenyan, Kan, Yangyang, Dong, Di, Tian, Jie, Luo, Yahong
Format: Article
Language:English
Subjects:
Adult
Aged
Axilla - pathology
Biopsy
Breast
Breast cancer
Breast Neoplasms - pathology
Cancer
Diagnostic Radiology
Feature extraction
Female
Humans
Imaging
Internal Medicine
Interventional Radiology
Lymph nodes
Lymph Nodes - pathology
Lymphatic Metastasis - diagnostic imaging
Lymphatic Metastasis - pathology
Lymphatic system
Lymphoma - pathology
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Mastectomy
Medical imaging
Medicine
Medicine & Public Health
Metastases
Metastasis
Middle Aged
Neuroradiology
Nomograms
Patients
Predictions
Prognosis
Radiology
Radiomics
Retrospective Studies
Support vector machines
Training
Ultrasound
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Summary:Objective To develop a radiomic nomogram for preoperative prediction of axillary lymph node (LN) metastasis in breast cancer patients. Methods 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. Results 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). Conclusions 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. Key Points • 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.
ISSN:0938-7994
1432-1084
DOI:10.1007/s00330-018-5981-2