Ultrasound-based prediction of preoperative core biopsy categories in solid breast tumor using machine learning.

ABSTRACT:

Background

The American College of Radiology Breast Imaging Reporting and Data System (ACR BI-RADS) used with ultrasonography cannot guide the individual management of solid breast tumors, but preoperative core biopsy categories (CBCs) can. We aimed to use machine learning to analyze clinical and ultrasonic features for predicting CBCs and to aid in the development of a new ultrasound (US) imaging reporting system for solid tumors of the breast.

Methods

This retrospective study included women with solid breast tumors who underwent US-guided core needle biopsy from March 1, 2019, to December 31, 2019. All patients were randomly assigned to a training or validation cohort (7:3 ratio). CBC was predicted using 5 machine learning models: random forest (RF), support vector machine

SUBMITTER: Liang T 

PROVIDER: S-EPMC10102795 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature