Systematic Review ARTICLE
Diagnostic Performance of Diffusion Tensor Imaging for Characterizing Breast Tumors: A Comprehensive Meta-analysis
- 1Shanxi Dayi Hospital Affiliated to Shanxi Medical University, China
- 2State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, China
- 3First Affiliated Hospital of Jinan University, China
Rationale and Objectives: Controversy still exists on the diagnosibility of diffusion tensor imaging (DTI) for breast lesions characterization across published studies. The clinical guideline of DTI used in the breast has not been established. This meta-analysis aims to pool relevant evidences and evaluate the diagnostic performance of DTI in the differential diagnosis of malignant and benign breast lesions.
Materials and Methods: The studies which assessed the diagnostic performance of DTI parameters in the breast were searched in Embase, PubMed and Cochrane Library between January 2010 and August 2019. Standardized mean differences and 95% confidence intervals of fractional anisotropy (FA), mean diffusivity (MD) and three diffusion eigenvalues (λ1, λ2 and λ3) were calculated using Review Manager 5.2. The pooled sensitivity, specificity, and area under the curve (AUC) were calculated with a bivariate model. Publication bias and heterogeneity between studies were also assessed using Stata 12.0.
Results: Sixteen eligible studies incorporating 1636 patients were included. The standardized mean differences indicated that breast cancers had a significant higher FA but lower MD, λ1, λ2 and λ3 than benign lesions (all P values < 0.05). Subgroup analysis indicated that invasive breast carcinoma (IBC) had a significant lower MD value than ductal carcinoma in situ (DCIS) (P=0.02). λ1 showed the best diagnostic accuracy with pooled sensitivity, specificity, and AUC of 93%, 92% and 0.97, followed by MD (AUC=0.93, sensitivity=88%, specificity=85%) and FA (AUC=0.76, sensitivity=70%, specificity=70%) in the differential diagnosis of breast lesions.
Conclusion: DTI with multiple quantitative parameters was adequate to differentiate breast cancers from benign lesions based on their biological characteristics. MD can further distinguish IBC from DCIS. The parameters, especially λ1 and MD, should attract our attentions in clinical practice.
Keywords: Diffusion Tensor Imaging, Breast, standardized mean difference; diagnostic performance, Magnetic Resonance Imaging, Meta-analysis
Received: 23 Aug 2019;
Accepted: 28 Oct 2019.
Copyright: © 2019 Wang, Li, Wu, Zheng, Zeng, E and Liang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Dr. Sihui Zeng, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, China, email@example.com
Dr. Linning E, Shanxi Dayi Hospital Affiliated to Shanxi Medical University, Taiyuan, Shanxi Province, China, firstname.lastname@example.org
Dr. Jianye Liang, First Affiliated Hospital of Jinan University, Guangzhou, Guangdong Province, China, email@example.com