Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
EBioMedicine. 2020 Oct;60:103018. doi: 10.1016/j.ebiom.2020.103018. Epub 2020 Sep 24.
Completion axillary lymph node dissection is overtreatment for patients with sentinel lymph node (SLN) metastasis in whom the metastatic risk of residual non-SLN (NSLN) is low. However, the National Comprehensive Cancer Network panel posits that none of the previous studies has successfully identified such subset patients. Here, we develop a multicentre deep learning radiomics of ultrasonography model (DLRU) to predict the risk of SLN and NSLN metastasis.
In total, 937 eligible breast cancer patients with ultrasound images were enrolled from two hospitals as the training set (n = 542) and independent test set (n = 395) respectively. Using the images, we developed and validated a prediction model combined with deep learning radiomics and axillary ultrasound to sequentially identify the metastatic risk of SLN and NSLN, thereby, classifying patients to relevant axillary management groups.
In the test set, the DLRU yields the best performance in identifying patients with metastatic disease in SLNs (sensitivity=98.4%, 95% CI 96.6-100) and NSLNs (sensitivity=98.4%, 95% CI 95.6-99.9). The DLRU also accurately stratifies patients without metastasis in SLN or NSLN into the corresponding low-risk (LR)-SLN and high-risk (HR)-SLN&LR-NSLN category with the negative predictive value of 97% (95% CI 94.2-100) and 91.7% (95% CI 88.8-97.9), respectively. Moreover, compared with the current clinical management, DLRU appropriately assigned 51% (39.6%/77.4%) of overtreated patients in the entire study cohort into the LR group, perhaps avoiding overtreatment.
The performance of the DLRU indicates that it may offer a simple preoperative tool to promote personalized axillary management of breast cancer.
The National Nature Science Foundation of China; The National Outstanding Youth Science Fund Project of National Natural Science Foundation of China; The Scientific research project of Heilongjiang Health Committee; The Postgraduate Research &Practice Innovation Program of Harbin Medical University.
对于前哨淋巴结（SLN）转移的患者，完成腋窝淋巴结清扫术是过度治疗，因为这些患者残留非前哨淋巴结（NSLN）的转移风险较低。然而，美国国家综合癌症网络专家组认为，之前的研究都没有成功确定这样的亚组患者。在此，我们开发了一种基于深度学习的超声放射组学模型（DLRU），用于预测 SLN 和 NSLN 转移的风险。
共纳入来自两家医院的 937 例符合条件的乳腺癌患者的超声图像作为训练集（n=542）和独立测试集（n=395）。使用这些图像，我们开发并验证了一种结合深度学习放射组学和腋窝超声的预测模型，以依次识别 SLN 和 NSLN 的转移风险，从而将患者分类到相关的腋窝管理组。
在测试集中，DLRU 在识别 SLN 和 NSLN 中转移性疾病患者方面表现最佳（敏感性=98.4%，95%CI 96.6-100）。DLRU 还可以准确地将 SLN 或 NSLN 中无转移的患者分层为相应的低风险（LR）-SLN 和高风险（HR）-SLN&LR-NSLN 类别，阴性预测值分别为 97%（95%CI 94.2-100）和 91.7%（95%CI 88.8-97.9）。此外，与当前的临床管理相比，DLRU 将整个研究队列中 51%（39.6%/77.4%）的过度治疗患者适当分配到 LR 组，可能避免了过度治疗。
DLRU 的性能表明，它可能提供一种简单的术前工具，以促进乳腺癌的个体化腋窝管理。
国家自然科学基金；国家自然科学基金优秀青年科学基金项目；黑龙江省卫生健康委员会科研项目；哈尔滨医科大学研究生科研创新项目。
