Use of machine learning with two-dimensional synthetic mammography for axillary lymph node metastasis prediction in breast cancer: a preliminary study

ABSTRACT:

Background

As of 2020, breast cancer is the most common type of cancer and the fifth most common cause of cancer-related deaths worldwide. The non-invasive prediction of axillary lymph node (ALN) metastasis using two-dimensional synthetic mammography (SM) generated from digital breast tomosynthesis (DBT) could help mitigate complications related to sentinel lymph node biopsy or dissection. Thus, this study aimed to investigate the possibility of predicting ALN metastasis using radiomic analysis of SM images.

Methods

Seventy-seven patients diagnosed with breast cancer using full-field digital mammography (FFDM) and DBT were included in the study. Radiomic features were calculated using segmented mass lesions. The ALN prediction models were constructed based on a logistic r

SUBMITTER: Haraguchi T 

PROVIDER: S-EPMC10248572 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature