Breast Cancer Related LVI Status Prediction Based on Active Contour Model and Radiomics
FENG Bao1,2, LI Chang-lin3, LI Zhi2, LIU Zhuang-sheng3
1. School of Biomedical Engineering, Sun Yat-sen University, Guangzhou 511400, China; 2. School of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin 541004, China; 3. Department of Radiology, Affiliated Jiangmen Hospital of Sun Yat-sen University, Jiangmen 529000, China.
FENG Bao, LI Chang-lin, LI Zhi, LIU Zhuang-sheng. Breast Cancer Related LVI Status Prediction Based on Active Contour Model and Radiomics[J]. Journal of Northeastern University Natural Science, 2020, 41(2): 193-199.
[1]Fitzmaurice C,Allen C,Barber R M,et al.Global,regional,and national cancer incidence,mortality,years of life lost,years lived with disability,and disability-adjusted life-years for 32 cancer groups,1990 to 2015 [J].JAMA Oncology,2017,3(4):524-548. [2]Dong Y,Feng Q,Yang W,et al.Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI[J].European Radiology,2017,28(2):582-591. [3]Torre L A,Bray F,Siegel R L,et al.Global cancer statistics,2012[J].CA:A Cancer Journal for Clinicians,2015,65(2):87-108. [4]Aleskandarany M A,Sonbul S N,Mukherjee A,et al.Molecular mechanisms underlying lymphovascular invasion in invasive breast cancer[J].Pathobiology,2015,82(3/4):113-123. [5]Paduch R.The role of lymphangiogenesis and angiogenesis in tumor metastasis[J].Cellular Oncology,2016,39(5):397-410. [6]Bae M S,Moon H G,Han W,et al.Early stage triple-negative breast cancer:imaging and clinical-pathologic factors associated with recurrence[J].Radiology,2016,278(2):356-364. [7]Uematsu T,Kasami M,Watanabe J,et al.Is lymphovascular invasion degree one of the important factors to predict neoadjuvant chemotherapy efficacy in breast cancer ?[J].Breast Cancer,2011,18(4):309-313. [8]Ryu J M,Lee S K,Kim J Y,et al.Predictive factors for non-sentinel lymph node metastasis in patients with positive sentinel lymph nodes after neoadjuvant chemotherapy:nomogram for predicting nonsentinel lymph node metastasis[J].Clinical Breast Cancer,2017,17(7):550-558. [9]Kumar V,Gu Y,Basu S,et al.Radiomics:the process and the challenges[J].Magnetic Resonance Imaging,2012,30(9):1234-1248. [10]刘桐桐,李佳伟,胡雨舟,等.基于影像组学预测乳腺癌雌激素受体表达情况的可行性分析[J].生物医学工程学杂志,2017,34(4):597-601.(Liu Tong-tong,Li Jia-wei,Hu Yu-zhou,et al.Feasibility analysis of predicting expression of estrogen receptor in breast cancer based on radiomics[J].Journal of Biomedical Engineering,2017,34(4):597-601.) [11]吴佩琪,刘再毅,梁长虹.基于MRI的影像组学特征在鉴别乳腺浸润性导管癌病理分级中的价值[J].医学研究生学报,2018,31(9):938-942.(Wu Pei-qi,Liu Zai-yi,Liang Chang-hong.MRI-based radiomic features in histological grading of breast invasive ductal carcinoma[J].Journal of Medical Postgraduates,2018,31(9):938-942.) [12]Huang Y H,Chang Y C,Huang C S,et al.Computerized breast mass detection using multi-scale hessian-based analysis for dynamic contrast-enhanced MRI[J].Journal of Digital Imaging,2014,27(5):649-660. [13]Fan M,He T,Zhang P,et al.Diffusion-weighted imaging features of breast tumours and the surrounding stroma reflect intrinsic heterogeneous characteristics of molecular subtypes in breast cancer[J].NMR in Biomedicine,2018,31(2):e0189302. [14]孙利雷,徐勇.基于深度学习的乳腺X射线影像分类方法研究[J].计算机工程与应用,2018,54(21):13-19.(Sun Li-lei,Xu Yong.Research on classification method of mammography based on deep learning[J].Computer Engineering and Applications,2018,54(21):13-19.) [15]Boné B,Szabó B K,Perbeck L G,et al.Can contrast-enhanced MR imaging predict survival in breast cancer[J].Acta Radiologica,2015,44(4):373-378. [16]Achuthan A,Rajeswari M,Ramachandram D,et al.Wavelet energy-guided level set-based active contour:a segmentation method to segment highly similar regions[J].Computers in Biology and Medicine,2010,40(7):608-620. [17]Gianluca M,Matthias E,Katharina M,et al.Vessel suppressed chest computed tomography for semi-automated volumetric measurements of solid pulmonary nodules[J].European Journal of Radiology,2018,101(5):97-102. [18]Kokkinos I,Maragos P.Synergy between object recognition and image segmentation using the expectation maximization algorithm[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(8):1486-1501. [19]Kwedlo W.A new random approach for initialization of the multiple restart EM algorithm for Gaussian model-based clustering[J].Pattern Analysis & Applications,2015,18(4):757-770. [20]Dong Y,Feng Q,Yang W,et al.Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI[J].European Radiology,2018,28(2):582-591. [21]Rose C J,Mills S,O’Connor J P B,et al.Quantifying heterogeneity in dynamic contrast-enhanced MRI parameter maps[J].Medical Image Computing and Computer-Assisted Intervention,2010,62(2):488-499. [22]Fan M,Wu G,Cheng H,et al.Radiomic analysis of DCE-MRI for prediction of response to neoadjuvant chemotherapy in breast cancer patients[J].European Journal of Radiology,2017,94(2):140-147. [23]张文华,陈韬,张明慧,等.基于放射组学的胃肠道间质瘤分类模型[J].南方医科大学学报,2018,38(1):55-61.(Zhang Wen-hua,Chen Tao,Zhang Ming-hui,et al.A radiomics-based model for differentiation between benign and malignant gastrointestinal stromal tumors[J].Journal of Southern Medical University,2018,38(1):55-61.) [24]李祥霞,李彬,田联房,等.基于放射影像组学和随机森林算法的肺结节良恶性分类[J].华南理工大学学报,2018,46(8):78-86.(Li Xiang-xia,Li Bin,Tian Lian-fang,et al.Classification of benign and malignant pulmonary nodules based on radiomics and random forests algorithm[J].Journal of South China University of Technology,2018,46(8):78-86.) [25]Dougherty E,Hua J,Sima C.Performance of feature selection methods[J].Current Genomics,2009,10(6):365-374. [26]Pripp A H,Staniic′ M.Association between biomarkers and clinical characteristics in chronic subdural hematoma patients assessed with lasso regression[J].PloS One,2017,12(11):e0186838. [27]Breiman L.Random forest[J].Machine Learning,2001,45(1):5-32. [28]郑强,董恩清.一种新的基于二值水平集和形态学的局部分割方法[J].电子与信息学报,2012,34(2):375-381.(Zheng Qiang,Dong En-qing.A new local segmentation method based on binary level set and morphological operation[J].Journal of Electronics & Information Technology,2012,34(2):375-381.) [29]陈明,赵云,范能胜,等.医学图像分割中的主动轮廓模型研究现状[J].北京生物医学工程,2010,29(4):426-431.(Chen Ming,Zhao Yun,Fan Neng-sheng,et al.A review of the active contour models in medical image segmentation[J].Beijing Biomedical Engineering,2010,29(4):426-431.)