Non-invasive Assessment of Axillary Lymph Node Metastasis Risk in Early Invasive Breast Cancer Adopting Automated Breast Volume Scanning-Based Radiomics Nomogram: A Multicenter Study

Hui Wang; Xin-Wu Yang; Fei Chen; Yuan-Yuan Qin; Xuan-Bo Li; Su-Mei Ma; Jun-Qiang Lei; Cai-Ling Nan; Wei-Yang Zhang; Wei Chen; Shun-Lin Guo

doi:10.1016/j.ultrasmedbio.2023.01.006

Non-invasive Assessment of Axillary Lymph Node Metastasis Risk in Early Invasive Breast Cancer Adopting Automated Breast Volume Scanning-Based Radiomics Nomogram: A Multicenter Study

Ultrasound Med Biol. 2023 May;49(5):1202-1211. doi: 10.1016/j.ultrasmedbio.2023.01.006. Epub 2023 Feb 5.

Authors

Hui Wang¹, Xin-Wu Yang², Fei Chen³, Yuan-Yuan Qin², Xuan-Bo Li², Su-Mei Ma³, Jun-Qiang Lei⁴, Cai-Ling Nan³, Wei-Yang Zhang³, Wei Chen⁵, Shun-Lin Guo⁶

Affiliations

¹ Department of Ultrasound, First Hospital of Lanzhou University, Lanzhou, China; First Clinical Medical College, Lanzhou University, Lanzhou, China.
² College of Computer Science, Faculty of Information Technology, Beijing University of Technology, Beijing, China.
³ Department of Ultrasound, First Hospital of Lanzhou University, Lanzhou, China.
⁴ Department of Radiology, First Hospital of Lanzhou University, Lanzhou, China.
⁵ Department of Ultrasound, Ningxia Hui Autonomous Region People's Hospital, Yinchuan, China.
⁶ Department of Radiology, First Hospital of Lanzhou University, Lanzhou, China. Electronic address: guoshl2021@163.com.

PMID: 36746744
DOI: 10.1016/j.ultrasmedbio.2023.01.006

Abstract

Objective: The aim of the work described here was to develop a non-invasive tool based on the radiomics and ultrasound features of automated breast volume scanning (ABVS), clinicopathological factors and serological indicators to evaluate axillary lymph node metastasis (ALNM) in patients with early invasive breast cancer (EIBC).

Methods: We retrospectively analyzed 179 ABVS images of patients with EIBC at a single center from January 2016 to April 2022 and divided the patients into training and validation sets (ratio 8:2). Additionally, 97 ABVS images of patients with EIBC from a second center were enrolled as the test set. The radiomics signature was established with the least absolute shrinkage and selection operator. Significant ALNM predictors were screened using univariate logistic regression analysis and further combined to construct a nomogram using the multivariate logistic regression model. The receiver operating characteristic curve assessed the nomogram's predictive performance.

Discussion: The constructed radiomics nomogram model, including ABVS radiomics signature, ultrasound assessment of axillary lymph node (ALN) status, convergence sign and erythrocyte distribution width (standard deviation), achieved moderate predictive performance for risk probability evaluation of ALNs in patients with EIBC. Compared with ultrasound, the nomogram model was able to provide a risk probability evaluation tool not only for the ALNs with positive ultrasound features but also for micrometastatic ALNs (generally without positive ultrasound features), which benefited from the radiomics analysis of multi-sourced data of patients with EIBC.

Conclusion: This ABVS-based radiomics nomogram model is a pre-operative, non-invasive and visualized tool that can help clinicians choose rational diagnostic and therapeutic protocols for ALNM.

Keywords: Automated breast volume scanning; Axillary lymph node metastasis; Breast cancer; Nomogram.

Publication types

Multicenter Study
Research Support, Non-U.S. Gov't

MeSH terms

Breast Neoplasms* / diagnostic imaging
Breast Neoplasms* / pathology
Female
Humans
Lymph Nodes / diagnostic imaging
Lymph Nodes / pathology
Lymphatic Metastasis / diagnostic imaging
Lymphatic Metastasis / pathology
Nomograms*
Retrospective Studies