Deep learning radiomics of ultrasonography: Identifying the risk of axillary non-sentinel lymph node involvement in primary breast cancer.

ABSTRACT:

Background

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.

Methods

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

SUBMITTER: Guo X 

PROVIDER: S-EPMC7519251 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature