Feasibility analysis of predicting expression of estrogen receptor in breast cancer based on radiomics_Journal of Biomedical Engineering

Authors：

LIU Tongtong ^1,2 , LI Jiawei ³ , HU Yuzhou ^1,2 ,  YU Jinhua ^1,2 , GUO Yi ^1,2 , WANG Yuanyuan ^1,2 , CHANG Cai ³

1. Department of Electronic Engineering, Fudan University, Shanghai 200433, P.R.China;
2. Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai 200433, P.R.China;
3. Department of Ultrasound Diagnosis, Fudan University Shanghai Cancer Center, Shanghai 200032, P.R.China;

Corresponding author：

YU Jinhua, Email: jhy@fudan.edu.cn

Keywords：

radiomics; high level features; R language; molecular marker; estrogen receptor

DOI：

10.7507/1001-5515.201611033

Video：

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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.

Citation： LIU Tongtong, LI Jiawei, HU Yuzhou, YU Jinhua, GUO Yi, WANG Yuanyuan, CHANG Cai. Feasibility analysis of predicting expression of estrogen receptor in breast cancer based on radiomics. Journal of Biomedical Engineering, 2017, 34(4): 597-601. doi: 10.7507/1001-5515.201611033 Copy

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13.	Gomez W, Pereira W C, Infantosi A F. Analysis of co-occurrence texture statistics as a function of gray-level quantization for classifying breast ultrasound. IEEE Trans Med Imaging, 2012, 31(10): 1889-1899.
14.	Matsumoto M M S, Sehgal C M, Udupa J K. Local binary pattern texture-based classification of solid masses in ultrasound breast images [J]. Proc Spie, 2012, 8320(3):83201H-83201H-8.
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1. 张建兴. 乳腺超声诊断学. 北京: 人民卫生出版社, 2012.
2. Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer, 2012, 48(4): 441-446.
3. Kumar V, Gu Yuhua, Basu S, et al. Radiomics: the process and the challenges. Magn Reson Imaging, 2012, 30(9): 1234-1248.
4. Aerts H J, Velazquez E R, Leijenaar R T, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun, 2014, 5(5): 4006.
5. 万明习. 生物医学超声学. 北京: 科学出版社, 2010.
6. Goldhirsch A, Wood W C, Coates A S, et al. Strategies for subtypes--dealing with the diversity of breast cancer: highlights of the St. Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011. Ann Oncol, 2011, 22(8): 1736-1747.
7. Vallières M, Freeman C R, Skamene S R, et al. A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol, 2015, 60(14): 5471-5496.
8. Huang Yanqi, Liang Changhong, He Lan, et al. Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol, 2016, 34(18): 2427-2436.
9. Cameron A, Khalvati F, Haider M A, et al. MAPS: A quantitative radiomics approach for prostate cancer detection. IEEE Trans Biomed Eng, 2016, 63(6): 1145-1156.
10. 蔡凌云. 乳腺超声图像的自动分割与特征分析研究. 上海: 复旦大学, 2015.
11. 沈嘉琳. 乳腺肿瘤的超声图像分析及良恶性判别. 上海: 复旦大学, 2006.
12. 杨晓霜. 乳腺肿瘤超声图像分割中动态轮廓线算法研究及应用. 上海: 复旦大学, 2009.
13. Gomez W, Pereira W C, Infantosi A F. Analysis of co-occurrence texture statistics as a function of gray-level quantization for classifying breast ultrasound. IEEE Trans Med Imaging, 2012, 31(10): 1889-1899.
14. Matsumoto M M S, Sehgal C M, Udupa J K. Local binary pattern texture-based classification of solid masses in ultrasound breast images [J]. Proc Spie, 2012, 8320(3):83201H-83201H-8.
15. Yang M C, Moon W K, Wang Y C, et al. Robust texture analysis using multi-resolution gray-scale invariant features for breast sonographic tumor diagnosis. IEEE Trans Med Imaging, 2013, 32(12): 2262-2273.

Journal of Biomedical Engineering

Feasibility analysis of predicting expression of estrogen receptor in breast cancer based on radiomics

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