A meta-analysis study to comparing MR spectroscopy and Intravoxel Incoherent motion (DWI) in the differentiation of benign and malignant breast lesions

Main Article Content

Mohanad Ahmed Sahib
Arian Arvin
Raad Ajeel Bustan
Nasrin Ahmadinejad

Keywords

Diffusion-weighted imaging, Intravoxel incoherent motion (IVIM), magnetic resonance spectroscopy , Classification tumors of the breast

Abstract

Background: Advanced diffusion models are none-contrast MR imaging techniques for the assessment of breast lesions. The value of accuracy in detecting BC in the diagnosis of breast cancer has not been systematically evaluated. The study aimed, using meta-analysis approach, to examine the diagnostic ability of MRI spectroscopy and intravoxel incoherent motion (DWI) used to assess the differentiation between benign and malignant breast lesions.
Materials and Methods: The study searched PubMed, MEDLINE, Cochrane, Embase, Scopus, Google Scholar, was used to find recent original studies that assessed the use of Non-Gaussian DWI model (Intravoxel Incoherent Motion; perfusion fraction ‘f’ ; real diffusivity ‘D’ and pseudo-diffusivity ‘D*’) and spectroscopy (1H) for the detection of breast cancer. The standardized mean difference (SMD) , pooling the sensitivity, specificity, and area under the curve were used to organize and summarize the studies. The QUADAS-2 and QUIPS programs were used to assess the quality of the included studies.
Results: fifty studies were included, with IVIM and spectroscopy being the most investigated . The presence of significant heterogeneity (P˂0.05) was observed for all parameters. The results showed high differentiation ability found between malignant and benign cancer, where, malignant cancer has significantly lower D values (SMD=-1.54; P<0.0001) than benign cancer . While D*, f, and 1H values were higher than benign caner (SMD=0.08, 0.71, and 1.42; P=0.0001). The best diagnostic performance was shown for D (sensitivity=86%; specificity= 0.86; AUC≤0.92) and for 1H, f, and D* in breast tumors’ differential diagnosis (sensitivity=0.83, 0.78, 0.69; specificity= 0.76, 0.69, 0.63; AUC 0.73), respectively.
Conclusion: Our findings showed that their parameters play a potential role in differentiating breast tumours. Superior diagnostic accuracy, alongside, the high sensitivity and specificity for diffusion-weighted advanced imaging means that these approaches can be used as a suitable method in differentiating breast tumors.

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