Association between DCE-MRI Features and Molecular Subtypes in Breast Cancer
Wang Shijian1 Fan Ming1 Zhang Juan2 Shao Guoliang2 Wang Xiaojia3 Li Lihua1*
1 College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, Hangzhou 310018, China 2Department of Radiology, Zhejiang Cancer Hospital, Hangzhou 310010, China 3 Department of Medical OncologyBreast, Zhejiang Cancer Hospital, Hangzhou 310010, China
Abstract：In this work, we investigated the correlation between DCE-MRI features and molecular subtypes in breast cancer. Sixty cases of malignant breast cancer patients with DCE-MRI examination before chemotherapy were retrospectively analyzed. The molecular subtype was confirmed according to the immunohistochemistry results. Firstly, 65-dimensional imaging features including statistical characteristics, morphology, textural and dynamic enhancement were extracted from DCE-MRI with computer semi-automatic methods. Then, the correlations of imaging features and molecular subtype were assessed using statistical analyses, including univariate logistic regression and multivariate logistic regression. At the same time, a multiple regression model was established based on above results. Finally, the distribution of significant image features was analyzed. The results of experiments showed that statistical characteristics of lesions were significantly correlated with luminal A, dynamic enhancement of lesions and background were significantly related to luminal B, HER2 and basal like subtype, in which P values were all lower than 0.05 using univariate logistic regression-adjusted method. Multi-variable logistic regression analysis showed that imaging features were significantly associated with molecular subtypes with P value equaled to 0.004 73 for luminal A, 0.002 77 for HER2 and 0.011 7 for basal like. The results suggested DCE-MR imaging features as important candidate marker to divide breast cancer into molecular subtypes.
王世健, 范明, 张娟, 邵国良, 王晓稼, 厉力华. 乳腺癌DCE-MRI影像特征与分子分型的关联性研究[J]. 中国生物医学工程学报, 2016, 35(5): 533-540.
Wang Shijian Fan Ming Zhang Juan Shao Guoliang Wang Xiaojia Li Lihua. Association between DCE-MRI Features and Molecular Subtypes in Breast Cancer. Chinese Journal of Biomedical Engineering, 2016, 35(5): 533-540.
[1] 张承杰. MRI乳腺病灶分割研究[D]. 杭州:杭州电子科技大学,2013. [2] Goldhirsch A, Wood WC, Coates AS, 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[J]. Ann Oncol,2011,22(8):1736-1747. [3] Mazurowski MA, Zhang J, Grimm J, 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. [4] 王娇,温健,涂巍,等. ER和PR及HER-2表达与乳腺癌分子分型及临床特征和预后的关系[J]. 中国现代普通外科进展,2014,17(2):99-103. [5] Schnitt SJ. Will molecular classification replace traditional breast pathology?[J]. International Journal of Surgical Pathology,010,18(Suppl3):162S-166S. [6] 赵燕,徐卫云. 乳腺癌分子分型及临床意义的研究进展[J]. 中国现代普通外科进展,2014,17(11):921-924. [7] Spitale A, Mazzola P, Soldini D, et al. Breast cancer classification according to immunohistochemical markers: clinicopathologic features and short-term survival analysis in a population-based study from the South of Switzerland[J]. Annals of Oncology,2009,20(4):628-635. [8] 张峰,罗立民,鲍旭东. 常用影像方法在乳腺癌辅助诊断中的性能对比[J]. 中国生物医学工程学报,2012,31(2):276-284. [9] 王荣福. 乳腺癌影像诊断技术应用进展[J]. 中国医学影像技术,2009(05):905-907. [10] 刘全良,马捷,龚静山,等. 乳腺癌MRI动态增强扫描非肿块样强化特点与ER PR及C-erbB-2因子表达的相关性研究[J]. 河北医学,2015(2):185-189. [11] Teifke A, Behr O, Schmidt M, et al. Dynamic MR imaging of breast lesions: correlation with microvessel distribution pattern and histologic characteristics of prognosis[J]. Radiology,2006,239(2):351-360. [12] 汤光宇. 动态增强MRI对乳腺癌生物学行为评价[D]. 上海:第二军医大学,2008. [13] 杨骞. 基于DCE-MRI影像的乳腺癌早期诊断研究[D]. 杭州:杭州电子科技大学,2013. [14] Segal E, Sirlin CB, Ooi C, et al. Decoding global gene expression programs in liver cancer by noninvasive imaging[J]. Nature Biotechnology,2007,25(6):675-680. [15] 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]. Journal of Magnetic Resonance Imaging,2015,42(4):902-907. [16] Sutton EJ, Oh JH, Dashevsky BZ, et al. Breast cancer subtype intertumor heterogeneity: MRI-based features predict results of a genomic assay[J]. Journal of Magnetic Resonance Imaging,2015,42(5):1398-1406. [17] Yang Q, Li L, Zhang J, et al. Computer-Aided Diagnosis of Breast DCE-MRI Images Using Bilateral Asymmetry of Contrast Enhancement Between Two Breasts[J]. Journal of Digital Imaging,2014,27(1):152-160. [18] 张承杰,厉力华. 基于空间FCM与MRF方法的乳腺MRI序列三维病灶分割研究[J]. 中国生物医学工程学报,2014,33(2):202-211. [19] Qian Y, Lihua L, Juan Z, et al. A computerized global MR image feature analysis scheme to assist diagnosis of breast cancer: a preliminary assessment.[J]. European Journal of Radiology,2014,7(7):1086-1091. [20] Lam SW, Jimenez CR, Boven E. Breast cancer classification by proteomic technologies: Current state of knowledge[J]. Cancer Treatment Reviews, 2014, 40(1): 129-138. [21] Kyndi M, Sorensen FB, Knudsen H, et al. Estrogen receptor, proges-terone receptor, HER\|2, and response to postmastectomy radiother-apy in high-risk breast cancer: the Danish Breast Cancer CooperativeGroup [J]. J Clin Oncol 2008,26(9):1419-1426.
