Abstract
Rationale and Objectives: Diffusion kurtosis imaging (DKI) is a promising imaging technique, but the results regarding the diagnostic performance of DKI in the characterization and classification of breast tumors are inconsistent among published studies. This study aimed to pool all published results to provide more robust evidence of the differential diagnosis between malignant and benign breast tumors using DKI.Methods: Studies on the differential diagnosis of breast tumors using DKI-derived parameters were systemically retrieved from PubMed, Embase, and Web of Science without a time limit. Review Manager 5.3 was used to calculate the standardized mean differences (SMDs) and 95% confidence intervals of the mean kurtosis (MK), mean diffusivity (MD), and apparent diffusion coefficient (ADC). Stata 12.0 was used to pool the sensitivity, specificity, and diagnostic odds ratio (DOR) as well as the publication bias and heterogeneity of each parameter. Fagan's nomograms were plotted to predict the post-test probabilities.Results: Thirteen studies including 867 malignant and 460 benign breast lesions were analyzed. Most of the included studies showed a low to unclear risk of bias and low concerns regarding applicability. Breast cancer showed a higher MK (SMD = 1.23, P < 0.001) but a lower MD (SMD = −1.29, P < 0.001) and ADC (SMD = −1.21, P < 0.001) than benign tumors. The MK (SMD = −1.36, P = 0.006) rather than the MD (SMD = 0.29, P = 0.20) or ADC (SMD = 0.26, P = 0.24) can further differentiate invasive ductal carcinoma from ductal carcinoma in situ. The DKI-derived MK (sensitivity = 90%, specificity = 88%, DOR = 66) and MD (sensitivity = 86% and specificity = 88%, DOR = 46) demonstrated superior diagnostic performance and post-test probability (65, 64, and 56% for MK, MD, and ADC) in differentiating malignant from benign breast lesions, with a higher sensitivity and specificity than the DWI-derived ADC (sensitivity = 85% and specificity = 83%, DOR = 29).Conclusion: The DKI-derived MK and MD demonstrate a comparable diagnostic performance in the discrimination of breast tumors based on their microstructures and non-Gaussian characteristics. The MK can further differentiate invasive ductal carcinoma from ductal carcinoma in situ.
Highlights
Breast cancer has become the most common cancer in females and accounted for 30% of estimated new cases in 2020
Another meta-analysis further suggested that breast magnetic resonance imaging (MRI) should be considered for BI-RADS 4 rather than 3 and 5 mammographic microcalcifications, and the presence or absence of enhancement helps to rule out malignancy in mammographic microcalcifications at breast MRI (4)
We developed a bivariate regression model to pool the diagnostic performance with the sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) using Stata version 12.0
Summary
Breast cancer has become the most common cancer in females and accounted for 30% of estimated new cases in 2020. Differentiating breast cancer from benign lesions is important and challenging for clinicians using ultrasound or conventional mammography, especially in dense fibroglandular breasts (2). A meta-analysis by Bennani-Baiti et al (3) included studies applying DCE-MRI as an adjunct to conventional imaging (mammography or ultrasound) to clarify equivocal findings without microcalcifications. The results demonstrate breast MRI as an excellent diagnostic performance with a pooled sensitivity of 99% and specificity of 89%. Another meta-analysis further suggested that breast MRI should be considered for BI-RADS 4 rather than 3 and 5 mammographic microcalcifications, and the presence or absence of enhancement helps to rule out malignancy in mammographic microcalcifications at breast MRI (4). The false-positive findings may cause additional examinations or unnecessary surgery (5)
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