Random forest with preoperative core biopsy categories: a novel method for refining ultrasonic Breast Imaging Reporting and Data System evaluation.

Background: Many benign breast lesions are classified as Breast Imaging Reporting and Data System (BI-RADS) category 4, resulting in unneeded biopsies. We thus aimed to build a model based on a core needle biopsy category (CBC) to improve upon BI-RADS classification by analyzing clinical and ultrasonic features.

Methods: A retrospective study was conducted in which female patients with solid breast tumors who underwent ultrasound-guided core needle biopsy (CNB) were enrolled.

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

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.

Perfluorobutane contrast-enhanced ultrasonography for the diagnosis of HCC: a systematic review and meta-analysis.

Objectives: Perfluorobutane ultrasound contrast agent as a new type of contrast agent has a good performance in the diagnosis of hepatocellular carcinoma (HCC). This study aim to evaluate the accuracy and reliability of Perfluorobutane contrast-enhanced ultrasonography (P-CEUS) in the diagnosis of HCC with a systematic review and meta-analysis.

Methods: Web of Science, EMBASE, Cochrane, Clinical Key, Wan Fang, CBM and CNKI databases were systematically searched and checked for studies using P-CEUS in HCC, from 2007 to 2020.