A Meta-Analysis Study Of Quantitative Intravoxel Incoherent Motion (DWI) And Dynamic Contrast-Enhanced MRI To Evaluate Neoadjuvant Chemotherapy In Breast Cancer

Main Article Content

Mohanad Ahmed Sahib
Arian Arvin
Nasrin Ahmadinejad
Raad Ajeel Bustan

Keywords

Early prediction, DCE-MRI, IVIM , Neoadjuvant chemotherapy, Breast cancer.

Abstract

Objective: The aim of the current study was to evaluate the diagnostic value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and intravoxel coherent motion (DWI) in predicting breast cancer patient response to neoadjuvant chemotherapy (NAC).
Materials and Methods: We searched international databases including PubMed, Medline, Embase, and Science direct with appropriate keywords. The variance of each studies were calculated that assessed the use of Non-Gaussian DWI model (Intravoxel Incoherent Motion; perfusion fraction ‘f’ ; real diffusivity ‘D’ and pseudo-diffusivity ‘D*’) and dynamic contrast-enhanced of prediction of response of breast cancer. Pooling the sensitivity, specificity, and area under the curve were used to organize and summarize the studies. And the data were analyzed using STATA version 14. Finally, the results of the studies were entered into the random-effects meta-analysis.
Results: twenty one studies comprising 2161 patients were involved in the present study. The sensitivity and specificity of DCE-MRI were 0.693 (95% CI 0.560-0.826), and 0.754 (95% CI 0.605-0.903), respectively. The results showed a pooled PPV, and NPV based on the random effect model of 0.458 (95% CI 0.339-0.577), and 0.901 (95% CI 0.829-0.972) respectively. The pooled DCE-MRI accuracy to predict pCR to neoadjuvant chemotherapy was 0.768 (95% CI 0.720-0.817).
Conclusion: According to our results IVIM parameters and DCE-MRI is play a potential role in early prediction of response to NAC in BC. The superior sensitivity and specificity for diffusion-weighted advanced (IVIM) imaging and DCE parameter means that these approaches can be used as a suitable method in early prediction of response to breast tumors.

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