Literature DB >> 30810823

Correlation Analysis of Breast Cancer DWI Combined with DCE-MRI Imaging Features with Molecular Subtypes and Prognostic Factors.

Congru Yuan1, Feng Jin2, Xiuling Guo2, Sheng Zhao2, Wei Li3, Haidong Guo4.   

Abstract

This study aimed to deeply analyze the application of DWI and DCE-MRI imaging in breast cancer, the correlation between the imaging characteristics of DWI and DCE-MRI and the molecular subtypes and prognostic factors of breast cancer was studied. Firstly, DWI and DCE-MRI scans of all patients before interventional therapy were performed, and relevant information of the subjects was introduced in turn. Secondly, molecular subtypes were determined according to immunohistochemical results and gene amplification. Siemens 3.0 T post-processing workstation was used for image post-processing. The time signal curve (TIC), early enhancement rate (EER) and ADC values were measured, morphological characteristics were recorded, and the correlation between each image feature and molecular subtypes, prognostic factors (tumor size, pathological grade, lymph node metastasis, ER, PR, HER2, Ki67) was analyzed. The results showed that parameters such as ADC value, EER, lobulation sign, burr sign, enhancement way and TIC type were correlated with prognostic factors and molecular subtypes. And Bayesian model discriminant analysis showed that the above imaging parameters couldn't well predict the expression of immunohistochemical factors and molecular subtypes. However, the above characteristics had a good effect on the prediction of pathological grade, with a false diagnosis rate of 9.69%.

Entities:  

Keywords:  Breast cancer; DCE-MRI; Diffusion-weighted imaging; Molecular subtype; Prognostic factor

Mesh:

Substances:

Year:  2019        PMID: 30810823     DOI: 10.1007/s10916-019-1197-5

Source DB:  PubMed          Journal:  J Med Syst        ISSN: 0148-5598            Impact factor:   4.460


  8 in total

1.  Preliminary study on discriminating HER2 2+ amplification status of breast cancers based on texture features semi-automatically derived from pre-, post-contrast, and subtraction images of DCE-MRI.

Authors:  Lirong Song; Hecheng Lu; Jiandong Yin
Journal:  PLoS One       Date:  2020-06-17       Impact factor: 3.240

2.  Predicting pathologic response to neoadjuvant chemotherapy in patients with locally advanced breast cancer using multiparametric MRI.

Authors:  Nannan Lu; Jie Dong; Xin Fang; Lufang Wang; Wei Jia; Qiong Zhou; Lingyu Wang; Jie Wei; Yueyin Pan; Xinghua Han
Journal:  BMC Med Imaging       Date:  2021-10-23       Impact factor: 1.930

3.  Application of MRI Image Based on Computer Semiautomatic Segmentation Algorithm in the Classification Prediction of Breast Cancer Histology.

Authors:  Aizhu Sheng; Aijing Li; Jianbi Xia; Yizhai Ye
Journal:  J Healthc Eng       Date:  2021-11-24       Impact factor: 2.682

4.  XGboost Prediction Model Based on 3.0T Diffusion Kurtosis Imaging Improves the Diagnostic Accuracy of MRI BiRADS 4 Masses.

Authors:  Wan Tang; Han Zhou; Tianhong Quan; Xiaoyan Chen; Huanian Zhang; Yan Lin; Renhua Wu
Journal:  Front Oncol       Date:  2022-03-17       Impact factor: 6.244

Review 5.  Breast Cancer Subtypes and Quantitative Magnetic Resonance Imaging: A Systemic Review.

Authors:  Toshiki Kazama; Taro Takahara; Jun Hashimoto
Journal:  Life (Basel)       Date:  2022-03-28

6.  Readout-Segmented Echo-Planar Diffusion-Weighted MR Imaging Improves the Differentiation of Breast Cancer Receptor Statuses Compared With Conventional Diffusion-Weighted Imaging.

Authors:  Minghao Zhong; Zhiqi Yang; Xiaofeng Chen; Ruibin Huang; Mengzhu Wang; Weixiong Fan; Zhuozhi Dai; Xiangguang Chen
Journal:  J Magn Reson Imaging       Date:  2022-01-17       Impact factor: 5.119

7.  Computer-Aided Diagnosis Evaluation of the Correlation Between Magnetic Resonance Imaging With Molecular Subtypes in Breast Cancer.

Authors:  Wei Meng; Yunfeng Sun; Haibin Qian; Xiaodan Chen; Qiujie Yu; Nanding Abiyasi; Shaolei Yan; Haiyong Peng; Hongxia Zhang; Xiushi Zhang
Journal:  Front Oncol       Date:  2021-06-23       Impact factor: 6.244

8.  Correlations between apparent diffusion coefficient values of WB-DWI and clinical parameters in multiple myeloma.

Authors:  Bei Zhang; Bingyang Bian; Zhiwei Zhao; Fang Lin; Zining Zhu; Mingwu Lou
Journal:  BMC Med Imaging       Date:  2021-06-08       Impact factor: 1.930

  8 in total

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