TY - JOUR
T1 - Contrast-Enhanced Spectral Mammography-Based Prediction of Non-Sentinel Lymph Node Metastasis and Axillary Tumor Burden in Patients With Breast Cancer
AU - Wu, Xiaoqian
AU - Guo, Yu
AU - Sa, Yu
AU - Song, Yipeng
AU - Li, Xinghua
AU - Lv, Yongbin
AU - Xing, Dong
AU - Sun, Yan
AU - Cong, Yizi
AU - Yu, Hui
AU - Jiang, Wei
N1 - Funding Information:
The study was supported by Major Science and Technology Projects in Tianjin (18ZXZNSY00240), Shandong Provincial Natural Science Foundation (No. ZR202102210508), Shandong Medical and Health Science and Technology Development Project (No. 202004081034), Special fund for clinical research of Wu Jieping Medical Foundation (No. 320.6750.2020-20-4), and Yantai Science and Technology Innovation Development Plan Project (No. 2021YD007, 2021YD005).
Publisher Copyright:
Copyright © 2022 Wu, Guo, Sa, Song, Li, Lv, Xing, Sun, Cong, Yu and Jiang.
PY - 2022/5/6
Y1 - 2022/5/6
N2 - Purpose: To establish and evaluate non-invasive models for estimating the risk of non-sentinel lymph node (NSLN) metastasis and axillary tumor burden among breast cancer patients with 1–2 positive sentinel lymph nodes (SLNs). Materials and Methods: Breast cancer patients with 1–2 positive SLNs who underwent axillary lymph node dissection (ALND) and contrast-enhanced spectral mammography (CESM) examination were enrolled between 2018 and 2021. CESM-based radiomics and deep learning features of tumors were extracted. The correlation analysis, least absolute shrinkage and selection operator (LASSO), and analysis of variance (ANOVA) were used for further feature selection. Models based on the selected features and clinical risk factors were constructed with multivariate logistic regression. Finally, two radiomics nomograms were proposed for predicting NSLN metastasis and the probability of high axillary tumor burden. Results: A total of 182 patients [53.13 years ± 10.03 (standard deviation)] were included. For predicting the NSLN metastasis status, the radiomics nomogram built by 5 selected radiomics features and 3 clinical risk factors including the number of positive SLNs, ratio of positive SLNs, and lymphovascular invasion (LVI), achieved the area under the receiver operating characteristic curve (AUC) of 0.85 [95% confidence interval (CI): 0.71–0.99] in the testing set and 0.82 (95% CI: 0.67–0.97) in the temporal validation cohort. For predicting the high axillary tumor burden, the AUC values of the developed radiomics nomogram are 0.82 (95% CI: 0.66–0.97) in the testing set and 0.77 (95% CI: 0.62–0.93) in the temporal validation cohort. Discussion: CESM images contain useful information for predicting NSLN metastasis and axillary tumor burden of breast cancer patients. Radiomics can inspire the potential of CESM images to identify lymph node metastasis and improve predictive performance.
AB - Purpose: To establish and evaluate non-invasive models for estimating the risk of non-sentinel lymph node (NSLN) metastasis and axillary tumor burden among breast cancer patients with 1–2 positive sentinel lymph nodes (SLNs). Materials and Methods: Breast cancer patients with 1–2 positive SLNs who underwent axillary lymph node dissection (ALND) and contrast-enhanced spectral mammography (CESM) examination were enrolled between 2018 and 2021. CESM-based radiomics and deep learning features of tumors were extracted. The correlation analysis, least absolute shrinkage and selection operator (LASSO), and analysis of variance (ANOVA) were used for further feature selection. Models based on the selected features and clinical risk factors were constructed with multivariate logistic regression. Finally, two radiomics nomograms were proposed for predicting NSLN metastasis and the probability of high axillary tumor burden. Results: A total of 182 patients [53.13 years ± 10.03 (standard deviation)] were included. For predicting the NSLN metastasis status, the radiomics nomogram built by 5 selected radiomics features and 3 clinical risk factors including the number of positive SLNs, ratio of positive SLNs, and lymphovascular invasion (LVI), achieved the area under the receiver operating characteristic curve (AUC) of 0.85 [95% confidence interval (CI): 0.71–0.99] in the testing set and 0.82 (95% CI: 0.67–0.97) in the temporal validation cohort. For predicting the high axillary tumor burden, the AUC values of the developed radiomics nomogram are 0.82 (95% CI: 0.66–0.97) in the testing set and 0.77 (95% CI: 0.62–0.93) in the temporal validation cohort. Discussion: CESM images contain useful information for predicting NSLN metastasis and axillary tumor burden of breast cancer patients. Radiomics can inspire the potential of CESM images to identify lymph node metastasis and improve predictive performance.
KW - axillary tumor burden
KW - breast cancer
KW - contrast-enhanced spectral mammography
KW - non-sentinel lymph node metastasis
KW - radiomics
UR - http://www.scopus.com/inward/record.url?scp=85130682916&partnerID=8YFLogxK
U2 - 10.3389/fonc.2022.823897
DO - 10.3389/fonc.2022.823897
M3 - Article
AN - SCOPUS:85130682916
SN - 2234-943X
VL - 12
JO - Frontiers in Oncology
JF - Frontiers in Oncology
M1 - 823897
ER -