Association of DW/DCE-MRI features with prognostic factors in breast cancer

被引:8
|
作者
Shao, Guoliang [1 ]
Fan, Linyin [1 ]
Zhang, Juan [1 ]
Dai, Gang [1 ]
Xie, Tieming [1 ]
机构
[1] Zhejiang Canc Hosp, Dept Radiol, 38 Guangji Rd, Hangzhou 310022, Zhejiang, Peoples R China
来源
关键词
Breast cancer; Dynamic contrast-enhanced imaging; Diffusion-weighted imaging; Apparent diffusion coefficient; Molecular prognosis factors; APPARENT DIFFUSION-COEFFICIENT; WEIGHTED MAGNETIC-RESONANCE; CONTRAST-ENHANCED MRI; B-VALUES; LESIONS; SUBTYPES; DIFFERENTIATION; CLASSIFICATION; MAMMOGRAPHY; BIOMARKERS;
D O I
10.5301/jbm.5000230
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
Background: Through analyzing apparent diffusion coefficient (ADC) values and morphological evaluations, this research aimed to study how magnetic resonance imaging (MRI)-based breast lesion characteristics can enhance the diagnosis and prognosis of breast cancer. Methods: A total of 118 breast lesions, including 50 benign and 68 malignant lesions, from 106 patients were analyzed. All lesions were measured with both diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI. The average ADC of breast lesions was analyzed at b values of 600, 800 and 1,000 s/mm(2). Lesion margins, lesion enhancement patterns, and dynamic curves were also investigated. The relations between MRI-based features and molecular prognostic factors were evaluated using Spearman's rank correlation analysis. Results: A b value of 800 s/mm(2) was used to distinguish malignant from benign breast lesions, with an ADC cutoff value of 1.365 x 10(-3) mm(2)/s. The average ADC value between invasive ductal carcinoma (IDC) and ductal carcinoma in situ (DCIS) was significantly different. Malignant lesions were more likely to have spiculated margins, heterogeneous enhancement and washout curves. On the other hand, DCIS was more likely to have spiculated margins, heterogeneous/rim enhancement and plateau/washout dynamic curves. A significant negative correlation was found between progesterone receptor (PR) status and dynamic imaging (p = 0.027), while a significant positive correlation was found between Ki-67 status and lesion enhancement (p = 0.045). Conclusions: Both ADC values and MRI morphological assessment could be used to distinguish malignant breast lesions from benign ones.
引用
收藏
页码:E118 / E125
页数:8
相关论文
共 50 条
  • [31] A comprehensive hierarchical classification based on multi-features of breast DCE-MRI for cancer diagnosis
    Liu, Hui
    Wang, Jinke
    Gao, Jiyue
    Liu, Shanshan
    Liu, Xiang
    Zhao, Zuowei
    Guo, Dongmei
    Dan, Guo
    [J]. MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, 2020, 58 (10) : 2413 - 2425
  • [32] A comprehensive hierarchical classification based on multi-features of breast DCE-MRI for cancer diagnosis
    Hui Liu
    Jinke Wang
    Jiyue Gao
    Shanshan Liu
    Xiang Liu
    Zuowei Zhao
    Dongmei Guo
    Guo Dan
    [J]. Medical & Biological Engineering & Computing, 2020, 58 : 2413 - 2425
  • [33] Association between transcriptome and DCE-MRI phenotypes in invasive breast carcinoma
    Zhu, Yanhui
    Ming, Wenlong
    Liu, Yun
    Liu, Hongde
    Liu, Xiaoan
    [J]. ANNALS OF ONCOLOGY, 2022, 33 : S512 - S513
  • [34] Association between computer-derived features of the ipsilateral breast on DCE-MRI and the 70-gene signature in patients with invasive breast cancer
    van der Velden, B. H. M.
    Schmitz, A. M. T. H.
    Loo, C. E.
    Gilhuijs, K. G. A.
    [J]. CANCER RESEARCH, 2016, 76
  • [35] Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer using Radiomics Features of DCE-MRI
    Xiaoyu Cui
    Nian Wang
    Yue Zhao
    Shuo Chen
    Songbai Li
    Mingjie Xu
    Ruimei Chai
    [J]. Scientific Reports, 9
  • [36] Correlation between DCE-MRI radiomics features and Ki-67 expression in invasive breast cancer
    Juan, Ma-Wen
    Yu, Ji
    Peng, Guo-Xin
    Jun, Liu-Jun
    Feng, Sun-Peng
    Fang, Liu-Pei
    [J]. ONCOLOGY LETTERS, 2018, 16 (04) : 5084 - 5090
  • [37] Harmonization of radiomic features of breast lesions across international DCE-MRI datasets
    Whitney, Heather M.
    Li, Hui
    Ji, Yu
    Liu, Peifang
    Giger, Maryellen L.
    [J]. JOURNAL OF MEDICAL IMAGING, 2020, 7 (01)
  • [38] Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer using Radiomics Features of DCE-MRI
    Cui, Xiaoyu
    Wang, Nian
    Zhao, Yue
    Chen, Shuo
    Li, Songbai
    Xu, Mingjie
    Chai, Ruimei
    [J]. SCIENTIFIC REPORTS, 2019, 9 (1)
  • [39] A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features
    Ashirbani Saha
    Michael R. Harowicz
    Lars J. Grimm
    Connie E. Kim
    Sujata V. Ghate
    Ruth Walsh
    Maciej A. Mazurowski
    [J]. British Journal of Cancer, 2018, 119 : 508 - 516
  • [40] A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features
    Saha, Ashirbani
    Harowicz, Michael R.
    Grimm, Lars J.
    Kim, Connie E.
    Ghate, Sujata V.
    Walsh, Ruth
    Mazurowski, Maciej A.
    [J]. BRITISH JOURNAL OF CANCER, 2018, 119 (04) : 508 - 516