本文利用影像组学的方法预测乳腺肿瘤分子标记物雌激素受体（ER）。首先采用基于相位信息的动态轮廓模型（PBAC）对乳腺图像进行分割，其次对乳腺超声图像中肿瘤的形态、纹理、小波三个方面的 404 个高通量特征进行提取并予以量化，然后利用 R 语言以及结合最大相关最小冗余（mRMR）准则的遗传算法进行特征筛选，最后利用支持向量机（SVM）和 AdaBoost 进行分类判别，实现根据乳腺超声图像预测分子病理指标 ER 的目的。对 104 例临床乳腺肿瘤超声图像数据进行实验，在使用 AdaBoost 作为分类器的情况下得到了最优指标，即分子标记物 ER 的预测准确率最高可以达到 75.96%，受试者操作特性曲线下的面积（AUC）最高达到 79.39%。实验结果证明了利用影像组学方法预测乳腺癌 ER 表达情况的可行性
References
[1]
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.
[2]
3. Kumar V, Gu Yuhua, Basu S, et al. Radiomics: the process and the challenges. Magn Reson Imaging, 2012, 30(9): 1234-1248.
[3]
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.
[4]
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.
[5]
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.
[6]
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.
[7]
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.
[8]
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.
[9]
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.
[10]
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.
[11]
1. 张建兴. 乳腺超声诊断学. 北京: 人民卫生出版社, 2012.
[12]
5. 万明习. 生物医学超声学. 北京: 科学出版社, 2010.
[13]
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.
[14]
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.
[15]
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.
