Prediction of Metastasis in the Axillary Lymph Nodes of Patients With Breast Cancer: A Radiomics Method Based on Contrast-Enhanced Computed Tomography

Chunmei Yang; Jing Dong; Ziyi Liu; Qingxi Guo; Yue Nie; Deqing Huang; Na Qin; Jian Shu

doi:10.3389/fonc.2021.726240

Prediction of Metastasis in the Axillary Lymph Nodes of Patients With Breast Cancer: A Radiomics Method Based on Contrast-Enhanced Computed Tomography

Front Oncol. 2021 Sep 20:11:726240. doi: 10.3389/fonc.2021.726240. eCollection 2021.

Authors

Chunmei Yang^{1

2}, Jing Dong^{1

2}, Ziyi Liu³, Qingxi Guo⁴, Yue Nie⁵, Deqing Huang³, Na Qin³, Jian Shu^{1

2}

Affiliations

¹ Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
² Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
³ The Institute of Systems Science and Technology, Southwest Jiaotong University, Chengdu, China.
⁴ Department of Pathology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
⁵ Department of Radiology, Luzhou People's Hospital, Luzhou, China.

Abstract

Background: The use of traditional techniques to evaluate breast cancer is restricted by the subjective nature of assessment, variation across radiologists, and limited data. Radiomics may predict axillary lymph node metastasis (ALNM) of breast cancer more accurately.

Purpose: The aim was to evaluate the diagnostic performance of a radiomics model based on ALNs themselves that used contrast-enhanced computed tomography (CECT) to detect ALNM of breast cancer.

Methods: We retrospectively enrolled 402 patients with breast cancer confirmed by pathology from January 2016 to October 2019. Three hundred and ninety-six features were extracted for all patients from axial CECT images of 825 ALNs using Artificial Intelligent Kit software (GE Medical Systems, Version V3.1.0.R). Next, the radiomics model was trained, validated, and tested for predicting ALNM in breast cancer by using a support vector machine algorithm. Finally, the performance of the radiomics model was evaluated in terms of its classification accuracy and the value of the area under the curve (AUC).

Results: The radiomics model yielded the best classification accuracy of 89.1% and the highest AUC of 0.92 (95% CI: 0.91-0.93, p=0.002) for discriminating ALNM in breast cancer in the validation cohorts. In the testing cohorts, the model also demonstrated better performance, with an accuracy of 88.5% and an AUC of 0.94 (95% CI: 0.93-0.95, p=0.005) for predicting ALNM in breast cancer.

Conclusion: The radiomics model based on CECT images can be used to predict ALNM in breast cancer and has significant potential in clinical noninvasive diagnosis and in the prediction of breast cancer metastasis.

Keywords: axillary lymph node; breast cancer; computed tomography; metastasis; radiomics.