AUTHOR=Wu Xiaoqian , Guo Yu , Sa Yu , Song Yipeng , Li Xinghua , Lv Yongbin , Xing Dong , Sun Yan , Cong Yizi , Yu Hui , Jiang Wei 

TITLE=Contrast-Enhanced Spectral Mammography-Based Prediction of Non-Sentinel Lymph Node Metastasis and Axillary Tumor Burden in Patients With Breast Cancer

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.823897

DOI=10.3389/fonc.2022.823897

ISSN=2234-943X

ABSTRACT=Purpose: To establish and evaluate non-invasive models for estimating the risk of non-sentinel lymph nodes (NSLNs) 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 the 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 respectively. 
Results: 182 patients (55 years±11 [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 number of positive SLNs, ratio of positive SLNs and lymph-vascular invasion (LVI), achieved the area under the receiver operating characteristic curves (AUCs) of 0.85 (95% CI: 0.71, 0.99) in the testing set and 0.83 (95% CI: 0.69, 0.98) in the temporal validation cohort. For predicting the high axillary tumor burden, the AUCs values of the developed radiomics nomogram are 0.83 (95% CI: 0.69, 0.98) in the testing set and 0.79 (95% CI: 0.63, 0.95) 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.