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Updated: Feb 10, 2026

Profiling of Estrogen-regulated MicroRNAs in Breast Cancer Cells
Published on: February 21, 2014
[Feasibility analysis of predicting expression of estrogen receptor in breast cancer based on radiomics]
Tongtong Liu1, Jiawei Li2, Yuzhou Hu1
1Department of Electronic Engineering, Fudan University, Shanghai 200433, P.R.China;Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai 200433, P.R.China.
Abstract:
This study aims to predict expression of estrogen receptor (ER) in breast cancer by radiomics. Firstly, breast cancer images are segmented automatically by phase-based active contour (PBAC) method. Secondly, high-throughput features of ultrasound images are extracted and quantized. A total of 404 high-throughput features are divided into three categories, such as morphology, texture and wavelet. Then, the features are selected by R language and genetic algorithm combining minimum-redundancy-maximum-relevance (mRMR) criterion. Finally, support vector machine (SVM) and AdaBoost are used as classifiers, achieving the goal of predicting ER by breast ultrasound image. One hundred and four cases of breast cancer patients were conducted in the experiment and optimal indicator was obtained using AdaBoost. The prediction accuracy of molecular marker ER could achieve 75.96% and the highest area under the receiver operating characteristic curve (AUC) was 79.39%. According to the results of experiment, the feasibility of predicting expression of ER in breast cancer using radiomics was verified.
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