Literature DB >> 31313393

Dynamic contrast-enhanced and diffusion-weighted MRI of invasive breast cancer for the prediction of sentinel lymph node status.

Eun Jung Choi1, Ji Hyun Youk2, Hyemi Choi3, Ji Soo Song1.   

Abstract

BACKGROUND: Although sentinel lymph node biopsy (SLNB) is the current standard for identifying lymph metastasis in breast cancer patients, there are complications of SLNB.
PURPOSE: To evaluate preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) of invasive breast cancer for predicting sentinel lymph node metastasis. STUDY TYPE: Retrospective. POPULATION: In all, 309 patients who underwent clinically node-negative invasive breast cancer surgery FIELD STRENGTH/SEQUENCE: 3.0T, DCE-MRI, DWI. ASSESSMENT: We collected clinicopathologic variables (age, histologic and nuclear grade, extensive intraductal carcinoma component, lymphovascular invasion, and immunohistochemical profiles) and preoperative MRI features (tumor size, background parenchymal enhancement, internal enhancement, adjacent vessel sign, whole-breast vascularity, initial enhancement pattern, kinetic curve types, quantitative kinetic parameters, tumoral apparent diffusion coefficient [ADC], peritumoral maximal ADC, and peritumoral-tumoral ADC ratio). STATISTICAL TESTS: Multivariate logistic regressions were performed to determine independent variables associated with SLN metastasis, and the area under the receiver operating characteristic curve (AUC) was analyzed for those variables.
RESULTS: 41 (13.3%) of the patients showed SLN metastasis. With MRI, tumor size (odds ratio [OR], 1.11; 95% confidence interval [CI], 1.06-1.17), heterogeneous (OR, 5.33; 95% CI, 1.71-16.58), and rim (OR, 15.54; 95% CI, 2.12-113.72) enhancement and peritumoral-tumoral ADC ratio (OR, 72.79; 95% CI, 7.15-740.82) were independently associated with SLN metastasis. Clinicopathologic variables independently associated with SLN metastasis included age (OR, 0.96; 95% CI, 0.92-0.99) and CD31 (OR, 2.90; 95% CI, 1.04-8.92). The area under the curve (AUC) of MRI features (0.80; 95% CI, 0.73-0.87) was significantly higher than for clinicopathologic variables (0.68; 95% CI, 0.60-0.77; P = 0.048) and was barely below statistical significance for combined MRI features with clinicopathologic variables (0.84; 95% CI 0.78-0.90, P = 0.057). DATA
CONCLUSION: Preoperative internal enhancement on DCE-MRI and peritumoral-tumoral ADC ratio on DWI might be useful for predicting SLN metastasis in patients with invasive breast cancer. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:615-626.
© 2019 International Society for Magnetic Resonance in Medicine.

Entities:  

Keywords:  breast neoplasm; diffusion magnetic resonance imaging; magnetic resonance imaging; sentinel lymph node

Mesh:

Year:  2019        PMID: 31313393     DOI: 10.1002/jmri.26865

Source DB:  PubMed          Journal:  J Magn Reson Imaging        ISSN: 1053-1807            Impact factor:   4.813


  6 in total

1.  "Real-world" radiomics from multi-vendor MRI: an original retrospective study on the prediction of nodal status and disease survival in breast cancer, as an exemplar to promote discussion of the wider issues.

Authors:  Simon J Doran; Santosh Kumar; Matthew Orton; James d'Arcy; Fenna Kwaks; Elizabeth O'Flynn; Zaki Ahmed; Kate Downey; Mitch Dowsett; Nicholas Turner; Christina Messiou; Dow-Mu Koh
Journal:  Cancer Imaging       Date:  2021-05-20       Impact factor: 3.909

2.  Development and Internal Validation of a Preoperative Prediction Model for Sentinel Lymph Node Status in Breast Cancer: Combining Radiomics Signature and Clinical Factors.

Authors:  Chunhua Wang; Xiaoyu Chen; Hongbing Luo; Yuanyuan Liu; Ruirui Meng; Min Wang; Siyun Liu; Guohui Xu; Jing Ren; Peng Zhou
Journal:  Front Oncol       Date:  2021-11-08       Impact factor: 6.244

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.  Preoperative prediction of lymph node metastasis using deep learning-based features.

Authors:  Renee Cattell; Jia Ying; Lan Lei; Jie Ding; Shenglan Chen; Mario Serrano Sosa; Chuan Huang
Journal:  Vis Comput Ind Biomed Art       Date:  2022-03-07

5.  The value of whole-lesion histogram analysis based on field‑of‑view optimized and constrained undistorted single shot (FOCUS) DWI for predicting axillary lymph node status in early-stage breast cancer.

Authors:  Shu Fang; Jun Zhu; Yafeng Wang; Jie Zhou; Guiqian Wang; Weiwei Xu; Wei Zhang
Journal:  BMC Med Imaging       Date:  2022-09-10       Impact factor: 2.795

6.  Radiomics Based on T2-Weighted Imaging and Apparent Diffusion Coefficient Images for Preoperative Evaluation of Lymph Node Metastasis in Rectal Cancer Patients.

Authors:  Chunli Li; Jiandong Yin
Journal:  Front Oncol       Date:  2021-05-10       Impact factor: 6.244

  6 in total

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