Ultrasound radiomics based on axillary lymph nodes images for predicting lymph node metastasis in breast cancer - Tang, Yu-Long; Wang, Bin; Ou-Yang, Tao; Lv, Wen-Zhi; Tang, Shi-Chu; Wei, An; Cui, Xin-Wu; Huang, Jiang-Sheng (Frontiers in Oncology, 2023)
Objectives: To determine whether ultrasound radiomics can be used to distinguish axillary lymph nodes (ALN) metastases in breast cancer based on ALN imaging. Methods: A total of 147 breast cancer patients with 41 non-metastatic lymph nodes and 109 metastatic lymph nodes were divided into a training set (105 ALN) and validation set (45 ALN). Radiomics features were extract from ultrasound images and a radiomics signature (RS) was built. The Intraclass correlation coefficients (ICCs), Spearman correlation analysis, and least absolute shrinkage and selection operator (LASSO) methods were used to select the ALN status–related features. All images were assessed by two radiologists with at least 10 years of experience in ALN ultrasound examination. The performance levels of the model and radiologists in training, validation and subgroup were then evaluated and compared. Result: Radiomics signature accurately predicted the ALN status, achieved an area under the receiver operator characteristic curve of 0.929 (95%CI, 0.881-0.978) and area under curve(AUC) of 0.919 (95%CI, 95%CI, 0.841-0.997) in training and validation cohorts respectively. The radiomics model performed better than two experts’ prediction of ALN status in both cohorts (P<0.05). Besides, prediction in subgroups based on baseline clinicopathological information also achieved good discrimination performance, with an AUC of 0.937, 0.918, 0.885, 0.930 and 0.913 in HR+/HER2-, HER2+, triple-negative, tumor sized≤3cm and tumor sized＞3 cm, respectively. Conclusions: The radiomics model demonstrated good ability to predict ALN status in patients with breast cancer, which might provide essential information for decision-making.