深度学习放射组学可预测早期乳腺癌腋窝淋巴结状态。

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

机构信息

Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.

Department of Electronic Engineering, Fudan University, Shanghai, China.

出版信息

Nat Commun. 2020 Mar 6;11(1):1236. doi: 10.1038/s41467-020-15027-z.

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.

准确识别早期乳腺癌患者腋窝淋巴结（ALN）的受累情况对于确定适当的腋窝治疗方案非常重要，从而避免不必要的腋窝手术和并发症。在这里，我们报告了常规超声和乳腺癌剪切波弹性成像的深度学习放射组学（DLR），用于预测早期乳腺癌患者术前 ALN 状态。临床参数联合 DLR 在预测无疾病腋窝和任何腋窝转移的 ALN 状态方面具有最佳的诊断性能，在测试队列中的受试者工作特征曲线（AUC）为 0.902（95%置信区间[CI]：0.843，0.961）。在测试队列中，这种临床参数联合 DLR 还可以区分腋窝疾病低转移负荷和高转移负荷，AUC 为 0.905（95%CI：0.814，0.996）。我们的研究为早期乳腺癌患者提供了一种预测 ALN 转移程度的非侵入性成像生物标志物。