基于磁共振图像的乳腺癌分子分型研究进展

doi:10.3969/j.issn.0258-8021.2021.04.014

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (5890 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要在基因表达水平上,乳腺癌分子亚型的分析对评价乳腺癌的恶性程度及制定个体化治疗方案具有重要的临床应用价值。除免疫组织化学染色和基因芯片技术外,影像组学的兴起也为乳腺癌分子分型提供新的预测方式。综述基于磁共振成像的乳腺癌分子分型的研究现状。在概述乳腺磁共振成像原理和特点的基础上,分别从统计学分析及机器学习方法的角度,阐述乳腺癌的磁共振影像学表现与各分子亚型之间的关联性研究成果及预测算法,最后对乳腺癌分子分型研究做出总结并对未来的研究进行展望。基于磁共振成像(MRI)的乳腺癌分子分型的研究已取得了一些实质性进展,未来可借助更高阶的算法进一步深入挖掘乳腺癌MRI的形态学、背景实质增强、统计量及纹理等成像特征,使影像学特征作为潜在生物标记物,为乳腺癌的精准医疗提供帮助。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	孙榕
	聂生东
	魏珑

关键词 ：乳腺癌, 磁共振成像(MRI), 分子分型, 影像组学

Abstract：At the level of gene expression, molecular subtyping of breast cancers has important clinical practical value in the application of evaluating the malignant degree of breast cancers and formulating individualized therapeutic programs. In addition to microarray and immunohistochemical (IHC) staining, a new molecular classification method of breast cancers has been provided by radiomics. Herein, the domestic and international developments aboutmagnetic resonance imaging (MRI)-based molecular identification of breast cancers were summarized in this paper. After introducing the principles and characteristics of breast MRI, we reviewed the associated study achievements between molecular subtype information and breast MRI features from the aspect of statistics. Moreover, we highlighted the various prediction algorithms for breast cancer molecular identification from the perspective of machine learning. Finally, the development prospect and existing problems of the technology were pointed out. With aid of higher-order algorithms, it is advisable to further explore MRI imaging features of breast cancers as potential markers, such as morphology, background parenchymal enhancement and statistical texture, dedicating to precision diagnosis and treatment of breast cancers in future.

Key words： breast cancers magnetic resonance imaging(MRI) molecular typing radiomics

收稿日期: 2020-06-22

PACS:

R318

基金资助:国家自然科学基金(81830052);上海市科技创新行动计划项目(18441900500)

通讯作者: * E-mail: weilong_2046@163.com

作者简介: ^#中国生物医学工程学会会员(Member, Chinese Society of Biomedical Engineering)

引用本文:

孙榕, 聂生东, 魏珑. 基于磁共振图像的乳腺癌分子分型研究进展[J]. 中国生物医学工程学报, 2021, 40(4): 493-502.
Sun Rong, Nie Shengdong, Wei Long. Progress on MRI-Based Molecular Subtyping of Breast Cancer. Chinese Journal of Biomedical Engineering, 2021, 40(4): 493-502.

链接本文:

http://cjbme.csbme.org/CN/10.3969/j.issn.0258-8021.2021.04.014 或 http://cjbme.csbme.org/CN/Y2021/V40/I4/493

[1] 郑荣寿,孙可欣,张思维,等. 2015年中国恶性肿瘤流行情况分析 [J]. 中华肿瘤杂志, 2019(1):19-28.
[2] Carey LA, Perou CM, Livasy CA, et al. Race, breast cancer subtypes, and survival in the carolina breast cancer study [J]. Jama-Jam Med Assoc, 2006, 295(21): 2492-2502.
[3] Perou CM, Sorlie T, Eisen MB, et al. Molecular portraits of human breast tumours [J]. Nature, 2000, 406(6797): 747-752.
[4] Sorlie T, Tibshirani R, Parker J, et al. Repeated observation of breast tumor subtypes in independent gene expression data sets [J]. Proc Natl Acad Sci USA, 2003, 100(14): 8418-8423.
[5] Goldhirsch A, Winer EP, Coates AS, et al. Personalizing the treatment of women with early breast cancer: Highlights of the st gallen international expert consensus on the primary therapy of early breast cancer 2013 [J]. Ann Oncol, 2013, 24(9): 2206-2223.
[6] Cheang MCU, Chia SK, Voduc D, et al. Ki67 index, HER 2 status, and prognosis of patients with luminal B breast cancer [J]. Jnci-J Natl Cancer I, 2009, 101(10): 736-750.
[7] 黄三钱,钟晶敏,李晶,等. 乳腺癌分子分型的研究进展 [J]. 现代生物医学进展, 2016, 16(22): 4384-4387, 4269.
[8] Weiss A, King TA, Hunt KK, et al. Incorporating biologic factors into the american joint committee on cancer breast cancer staging system review of the supporting evidence [J]. Surg Clin North Am, 2018, 98(4): 687-702.
[9] 李双,范红敏,肖菲菲,等. 不同分子分型及临床病理特征与乳腺癌术后患者预后的关系 [J]. 临床与实验病理学杂志, 2016, 32(1): 39-44.
[10] 马建萍, 马芬兰. 不同分子分型乳腺癌的临床病理特征及预后的关系 [J]. 实用癌症杂志, 2017, 32(12): 2041-2044, 2048.
[11] Kim I, Choi HJ, Ryu JM, et al. Prognostic validation of the american joint committee on cancer 8th staging system in 24,014 Korean patients with breast cancer [J]. J Breast Cancer, 2018, 21(2): 173-181.
[12] Wong RX, Wong FY, Lim J, et al. Validation of the ajcc 8th prognostic system for breast cancer in an Asian healthcare setting [J]. Breast, 2018, 40: 38-44.
[13] Ye Jingming, Wang Wenjun, Xu Lin, et al. A retrospective prognostic evaluation analysis using the 8th edition of American joint committee on cancer (AJCC) cancer staging system for luminal A breast cancer [J]. Chinese J Cancer Res, 2017, 29(4): 351-360.
[14] Kelcz F, Furman-Haran E, Grobgeld D, et al. Clinical testing of high-spatial-resolution parametric contrast-enhanced MR imaging of the breast [J]. Am J Roentgenol, 2002, 179(6): 1485-1492.
[15] Vilar LN, German SPA, Garcia RM, et al. MR imaging findings in molecular subtypes of breast cancer according to BI-RADS system [J]. Breast J, 2017, 23(4): 421-428.
[16] Jeh SK, Kim SH, Kim HS, et al. Correlation of the apparent diffusion coefficient value and dynamic magnetic resonance imaging findings with prognostic factors in invasive ductal carcinoma [J]. J Magn Reson Imaging, 2011, 33(1): 102-109.
[17] Gulben K, Berberoglu U, Aydogan O, et al. Subtype is a predictive factor of nonsentinel lymph node involvement in sentinel node-positive breast cancer patients [J]. J Breast Cancer, 2014, 17(4): 370-375.
[18] Bronger H, Karge A, Dreyer T, et al. Induction of cathepsin B by the CXCR3 chemokines CXCL9 and CXCL10 in human breast cancer cells [J]. Oncol Lett, 2017, 13(6): 4224-4230.
[19] Ma Wenjuan, Zhao Yumei, Ji Yu, et al. Breast cancer molecular subtype prediction by mammographic radiomic features [J]. Acad Radiol, 2019, 26(2): 196-201.
[20] Nie Zhong, Wang Jian, Ji Xiaochun. Microcalcification-associated breast cancer: HER2-enriched molecular subtype is associated with mammographic features [J]. Brit J Radiol, 2018,2018: 20170942.
[21] Celebi F, Pilanci KN, Ordu C, et al. The role of ultrasonographic findings to predict molecular subtype, histologic grade, and hormone receptor status of breast cancer [J]. Diagn Interv Radiol, 2015, 21(6): 448-453.
[22] Yamamoto S, Maki DD, Korn RL, et al. Radiogenomic analysis of breast cancer using MRI: A preliminary study to define the landscape [J]. Am J Roentgenol, 2012, 199(3): 654-663.
[23] 刘静,季宇,李芳,等. 浸润性乳腺癌X线影像学表现与其分子分型的相关性研究 [J]. 中国肿瘤临床, 2018, 45(20): 1049-1052.
[24] Holli-Helenius K, Salminen A, Rinta-Kiikka I, et al. MRI texture analysis in differentiating luminal A and luminal B breast cancer molecular subtypes - A feasibility study[J]. BMC Med Imaging. 2017, 17(1):69.
[25] Chas M, Boivin L, Arbion F, et al. Clinicopathologic predictors of lymph node metastasis in breast cancer patients according to molecular subtype [J]. J Gynecol Obstet Hum, 2018, 47(1): 9-15.
[26] Babyshkina N, Malinovskaya E, Patalyak S, et al. Neoadjuvant chemotherapy for different molecular breast cancer subtypes: A retrospective study in Russian population [J]. Med Oncol, 2014, 31(9): 165.
[27] Gangi A, Mirocha J, Leong T, et al. Triple-negative breast cancer is not associated with increased likelihood of nodal metastases [J]. Ann Surg Oncol, 2014, 21(13): 4098-4103.
[28] Howland NK, Driver TD, Sedrak MP, et al. Lymph node involvement in immunohistochemistry-based molecular classifications of breast cancer [J]. J Surg Res, 2013, 185(2): 697-703.
[29] 季晓亮,谢小红,顾锡冬. 乳腺癌不同分子分型与磁共振成像特点的关系研究 [J]. 现代实用医学, 2015, 27(9): 1225-1227.
[30] Kato F, Kudo K, Yamashita H, et al. Differences in morphological features and minimum apparent diffusion coefficient values among breast cancer subtypes using 3-tesla MRI [J]. Eur J Radiol, 2016, 85(1): 96-102.
[31] Yu Yanming, Jiang Quan, Miao Yanwei, et al. Quantitative analysis of clinical dynamic contrast-enhanced MR imaging for evaluating treatment response in human breast cancer [J]. Radiology, 2010, 257(1): 47-55.
[32] Kim JY, Kim SH, Kim YJ, et al. Enhancement parameters on dynamic contrast enhanced breast MRI: Do they correlate with prognostic factors and subtypes of breast cancers? [J]. Magn Reson Imaging, 2015, 33(1): 72-80.
[33] 邓丹琼,梁碧玲. 乳腺癌患者临床病理特征与磁共振成像强化率关系 [J]. 中国公共卫生, 2016, 32(9): 1255-1257.
[34] Mazurowski MA, Zhang J, Grimm LJ, et al. Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging [J]. Radiology, 2014, 273(2): 365-372.
[35] Grimm LJ, Zhang J, Mazurowski MA. Computational approach to radiogenomics of breast cancer: Luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms [J]. J Magn Reson Imaging, 2015, 42(4): 902-907.
[36] Fan Ming, Li Hui, Wang Shijian, et al. Radiomic analysis reveals DCE-MRI features for prediction of molecular subtypes of breast cancer [J]. PLoS ONE, 2017, 12(2): e0171683.
[37] Wang J, Kato F, Oyama-Manabe N, et al. Identifying triple-negative breast cancer using background parenchymal enhancement heterogeneity on dynamic contrast-enhanced MRI: A pilot radiomics study [J]. PLoS ONE, 2015, 10(11): e0143308.
[38] Agner SC, Rosen MA, Englander S, et al. Computerized image analysis for identifying triple-negative breast cancers and differentiating them from other molecular subtypes of breast cancer on dynamic contrast-enhanced MR images: A feasibility study [J]. Radiology, 2014, 272(1): 91-99.
[39] Li Wei, Yu Kun, Feng Chaolu, et al. Molecular subtypes recognition of breast cancer in dynamic contrast-enhanced breast magnetic resonance imaging phenotypes from radiomics data [J]. Comput Math Methods M, 2019,2019: 6978650.
[40] Zhao Xiaomei, Wu Yilong, Song Guidong, et al. A deep learning model integrating FCNNs and CRFs for brain tumor segmentation [J]. Med Image Anal, 2018, 43: 98-111.
[41] Zhang Peng, Xu Xinnan, Wang Hongwei, et al. Computer-aided lung cancer diagnosis approaches based on deep learning [J]. Journal of Computer Aided Design & Computer Graphics, 2018, 30(1): 90-99.
[42] Samala RK, Chan HP, Hadjiiski LM, et al. Multi-task transfer learning deep convolutional neural network: Application to computer-aided diagnosis of breast cancer on mammograms [J]. Phys Med Biol, 2017, 62(23): 8894-8908.
[43] Nishio M, Sugiyama O, Yakami M, et al. Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning [J]. PLoS ONE, 2018, 13(7): e0200721.
[44] Hu Zilong, Tang Jinshan, Wang Ziming, et al. Deep learning for image-based cancer detection and diagnosis - A survey [J]. Pattern Recogn, 2018, 83: 134-149.
[45] Zhu Z, Albadawy E, Saha A, et al. Deep learning for identifying radiogenomic associations in breast cancer [J]. Comput Biol Med, 2019, 109: 85-90.
[46] Ha R, Mutasa S, Karcich J, et al. Predicting breast cancer molecular subtype with MRI dataset utilizing convolutional neural network algorithm [J]. J Digit Imaging, 2019, 32(2): 276-282.
[47] Zhang Yang, Chen JH, Lin Yezhi, et al. Prediction of breast cancer molecular subtypes on DCE-MRI using convolutional neural network with transfer learning between two centers [J]. Eur Radiol, 1 Oct, 2020 [Epub ahead of print].
[48] Greff K, Srivastava RK, Koutnik J, et al. Lstm: A search space odyssey [J]. IEEE T Neur Net Lear, 2017, 28(10): 2222-2232.
[49] Cho K, van Merriënboer B, Gulcehre C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation [EB/OL]. https://arxiv.org/abs/1406.1078, 2014-09-03/ 2020-06-20.
[50] Graves A, Fernandez S, Schmidhuber J. Bidirectional LSTM networks for improved phoneme classification and recognition [C] // Lecture Notes in Computer Science. Warsaw: Springer-Verlag, 2005: 799-804.
[51] Shi Xingjian, Chen Zhourong, Wang Hao, et al. Convolutional LSTM network: A machine learning approach for precipitation nowcasting [C] // Advances in Neural Information Processing Systems. Montreal: NIPS, 2015:28-28.