A classifier-combined method for grading breast cancer based on Dempster-Shafer evidence theory.

ABSTRACT:

Background

Preoperative non-invasive histologic grading of breast cancer is essential. This study aimed to explore the effectiveness of a machine learning classification method based on Dempster-Shafer (D-S) evidence theory for the histologic grading of breast cancer.

Methods

A total of 489 contrast-enhanced magnetic resonance imaging (MRI) slices with breast cancer lesions (including 171 grade Ⅰ, 140 grade Ⅱ, and 178 grade Ⅲ lesions) were used for analysis. All the lesions were segmented by two radiologists in consensus. For each slice, the quantitative pharmacokinetic parameters based on a modified Tofts model and the textural features of the segmented lesion on the image were extracted. Principal component analysis was then used to reduce feature dimensionality and obtain

SUBMITTER: Liu Z 

PROVIDER: S-EPMC10167441 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature