乳腺癌
医学
腋窝
接收机工作特性
前哨淋巴结
阶段(地层学)
队列
放射科
肿瘤科
腋窝淋巴结清扫术
癌症
内科学
生物
古生物学
作者
Xueyi Zheng,Yao Zhao,Yili Huang,Yu Yan,Yun Wang,Yubo Liu,Rushuang Mao,Fei Li,Yang Xiao,Yuanyuan Wang,Yixin Hu,Jinhua Yu,Jianhua Zhou
标识
DOI:10.1038/s41467-020-15027-z
摘要
Abstract Accurate identification of axillary lymph node (ALN) involvement in patients with early-stage breast cancer is important for determining appropriate axillary treatment options and therefore avoiding unnecessary axillary surgery and complications. Here, we report deep learning radiomics (DLR) of conventional ultrasound and shear wave elastography of breast cancer for predicting ALN status preoperatively in patients with early-stage breast cancer. Clinical parameter combined DLR yields the best diagnostic performance in predicting ALN status between disease-free axilla and any axillary metastasis with areas under the receiver operating characteristic curve (AUC) of 0.902 (95% confidence interval [CI]: 0.843, 0.961) in the test cohort. This clinical parameter combined DLR can also discriminate between low and heavy metastatic burden of axillary disease with AUC of 0.905 (95% CI: 0.814, 0.996) in the test cohort. Our study offers a noninvasive imaging biomarker to predict the metastatic extent of ALN for patients with early-stage breast cancer.
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