人工智能可预测早期乳腺癌淋巴结转移

　　准确发现早期乳腺癌患者腋窝淋巴结转移，对于确定合理治疗方案并且避免不必要的腋窝手术和并发症至关重要。

　　2020年3月6日，英国《自然》旗下《自然通讯》在线发表中山大学肿瘤防治中心、复旦大学信息科学与工程学院电子工程系、上海市医学图像处理与计算机辅助手术重点实验室、中国科学院深圳先进技术研究院生物医学与健康工程研究所劳特伯生物医学成像研究中心的研究报告，通过人工智能深度学习乳腺癌的超声影像数据特征，对早期乳腺癌患者术前腋窝淋巴结转移进行了预测。

　　该研究将临床指标结合深度学习普通超声和剪切波弹性成像数据特征，对早期乳腺癌患者腋窝淋巴结转移有无进行预测，接受者操作特征曲线下面积达0.902（95%置信区间：0.843～0.961）。

　　此外，该临床指标结合深度学习普通超声和剪切波弹性成像数据特征，对早期乳腺癌患者腋窝淋巴结转移多少进行预测，接受者操作特征曲线下面积达0.905（95%置信区间：0.814～0.996）。

　　因此，该研究结果表明，通过人工智能深度学习无创成像生物学指标，可以预测早期乳腺癌患者腋窝淋巴结转移程度。

Nat Commun. 2020 Mar 6. [Epub ahead of print]

Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer.

Xueyi Zheng, Zhao Yao, Yini Huang, Yanyan Yu, Yun Wang, Yubo Liu, Rushuang Mao, Fei Li, Yang Xiao, Yuanyuan Wang, Yixin Hu, Jinhua Yu, Jianhua Zhou.

Sun Yat-Sen University Cancer Center, Guangzhou, China; Fudan University, Shanghai, China; Paul C. Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; The key laboratory of medical imaging computing and computer assisted intervention of Shanghai, Shanghai, China.

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.

DOI: 10.1038/s41467-020-15027-z