Day 15. Deep learning radiomics can predict axillary lymphnode status in early-stage breast cancer

Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer
深度学习预测早期乳腺癌的腋窝淋巴状态

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）的情况对于确定合适的腋窝治疗方案、避免不必要的腋窝手术和并发症具有重要意义。在这里，我们报告了乳腺癌常规超声和横波弹性成像的深度学习放射组学（DLR）对早期乳腺癌患者术前ALN状态的预测。临床参数结合DLR在预测无病腋窝和腋窝转移之间的ALN状态方面具有最佳的诊断性能，在试验队列中，受试者操作特征曲线（AUC）下的面积为0.902（95%置信区间[CI]：0.843，0.961）。这个临床参数结合DLR也可以区分腋窝疾病的低转移负荷和重转移负荷，AUC为0.905（95%CI:0.814，0.996）。我们的研究提供了一种无创性的影像生物标记物来预测早期乳腺癌患者ALN的转移范围。