Assessing Detection, Discrimination, and Risk of Breast Cancer According to Anisotropy Parameters of Diffusion Tensor Imaging

Jiang, Ruisheng; Zeng, Xiangmin; Sun, Shihang; Ma, Zhuo; Wang, Ximing

doi:10.12659/msm.895755

Cited by 17 publications

(14 citation statements)

References 30 publications

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“…Three studies for MD (9, 12, 18) and four studies for FA values (9, 12, 18, 24) used in the differentiation between two subtypes of breast cancer were further pooled. The SMD of MD was −0.76 (−1.40, −0.12), P = 0.02, which indicated that IBC had a lower MD value than that of DCIS.…”

Section: Resultsmentioning

confidence: 99%

“…It is worth noting that a larger area of liquefactive necrosis more easily occurs in a highly aggressive and large tumor, which may result in a loss of organization of the breast parenchyma in cancerous tissues, and in reverse reduce the anisotropy to a certain extent in breast cancer, as well as the difference of FA between breast cancer and benign lesions. It may be the reason that FA reported in some of the included studies cannot distinguish breast cancer from benign lesions (9, 18, 19). FA also performed a moderate diagnostic value, with the lowest AUC of 0.76 among the three parameters.…”

Section: Discussionmentioning

confidence: 99%

“…As a result, the water molecule moves mainly along the primary axis in the extracellular space and demonstrates anisotropy. As a number of diffusion gradients were applied in DTI, it can uniquely determine a specific three-dimensional ellipsoid tensor unit in each pixel, within which the diffusion ability in any direction can be accurately calculated (18). Therefore, DTI-derived λ1, which were calculated from three-dimensional tissue volumes, displayed superior diagnostic performance compared to MD.…”

Section: Discussionmentioning

confidence: 99%

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Diagnostic Performance of Diffusion Tensor Imaging for Characterizing Breast Tumors: A Comprehensive Meta-Analysis

Wang

et al. 2019

Front. Oncol.

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Rationale and Objectives: Controversy still exists on the diagnosability 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 that assessed the diagnostic performance of DTI parameters in the breast were searched in Embase, PubMed, and Cochrane Library between January 2010 and September 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 1,636 patients were included. The standardized mean differences indicated that breast cancers had a significantly higher FA but lower MD, λ1, λ2, and λ3 than those of benign lesions (all P < 0.05). Subgroup analysis indicated that invasive breast carcinoma (IBC) had a significantly lower MD value than that of 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 = 0.92, sensitivity = 87%, specificity = 83%) 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 attention in clinical practice.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Diagnostic Performance of Diffusion Tensor Imaging for Characterizing Breast Tumors: A Comprehensive Meta-Analysis

Wang

et al. 2019

Front. Oncol.

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“…Whereas it is suggested that reduced structuring in malignant breast lesions should be reflected by a reduced diffusion anisotropy (66), anisotropy measures achieved a mixed diagnostic utility; some studies find no significant difference in fractional anisotropy between malignant and benign lesions (40,57). The number of diffusion directions used ranged from six to 64, though use of the b value pair 0 and 1000 sec/mm 2 was the most common (27,40,57,66).…”

Section: Discussionmentioning

confidence: 99%

A Meta-analysis of the Diagnostic Performance of Diffusion MRI for Breast Lesion Characterization

et al. 2019

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Radiology 2019; 00:1-11 • https://doi.org/10.1148/radiol.2019182510 • Content code:Background: Various techniques are available to assess diffusion properties of breast lesions as a marker of malignancy at MRI. The diagnostic performance of these diffusion markers has not been comprehensively assessed. Purpose:To compare by meta-analysis the diagnostic performance of parameters from diffusion-weighted imaging (DWI), diffusion-tensor imaging (DTI), and intravoxel incoherent motion (IVIM) in the differential diagnosis of malignant and benign breast lesions. Materials and Methods:PubMed and Embase databases were searched from January to March 2018 for studies in English that assessed the diagnostic performance of DWI, DTI, and IVIM in the breast. Studies were reviewed according to eligibility and exclusion criteria. Publication bias and heterogeneity between studies were assessed. Pooled summary estimates for sensitivity, specificity, and area under the curve were obtained for each parameter by using a bivariate model. A subanalysis investigated the effect of MRI parameters on diagnostic performance by using a Student t test or a one-way analysis of variance. Results:From 73 eligible studies, 6791 lesions (3930 malignant and 2861 benign) were included. Publication bias was evident for studies that evaluated apparent diffusion coefficient (ADC). Significant heterogeneity (P , .05) was present for all parameters except the perfusion fraction (f ). The pooled sensitivity, specificity, and area under the curve for ADC was 89%, 82%, and 0.92, respectively. The highest performing parameter for DTI was the prime diffusion coefficient (l 1 ), and pooled sensitivity, specificity, and area under the curve was 93%, 90%, and 0.94, respectively. The highest performing parameter for IVIM was tissue diffusivity (D), and the pooled sensitivity, specificity, and area under the curve was 88%, 79%, and 0.90. Choice of MRI parameters had no significant effect on diagnostic performance. Conclusion:Diffusion-weighted imaging, diffusion-tensor imaging, and intravoxel incoherent motion have comparable diagnostic accuracy with high sensitivity and specificity. Intravoxel incoherent motion is comparable to apparent diffusion coefficient. Diffusion-tensor imaging is potentially promising but to date the number of studies is limited.

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“…28,29 Kurtosis can also be combined with IVIM. 30 Diffusion tensor (DT) modelling has produced inconsistent results, generally demonstrating lower fractional anisotropy (FA) in cancers compared with normal tissues, 31,32 but not always showing a distinction from benign lesions; 15,33,34 other anisotropy metrics may be more sensitive. 35 Furthermore, the source of anisotropy is uncertain: a preclinical breast cancer model observed lower FA in hypoxic regions with lower collagen fibre content, 36 but the differences between normoxic and hypoxic FAs were small, approximately 0.03.…”

Section: Introductionmentioning

confidence: 99%

Microstructural models for diffusion MRI in breast cancer and surrounding stroma: an ex vivo study

et al. 2016

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The diffusion signal in breast tissue has primarily been modelled using apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM) and diffusion tensor (DT) models, which may be too simplistic to describe the underlying tissue microstructure. Formalin‐fixed breast cancer samples were scanned using a wide range of gradient strengths, durations, separations and orientations. A variety of one‐ and two‐compartment models were tested to determine which best described the data. Models with restricted diffusion components and anisotropy were selected in most cancerous regions and there were no regions in which conventional ADC or DT models were selected. Maps of ADC generally related to cellularity on histology, but maps of parameters from more complex models suggest that both overall cell volume fraction and individual cell size can contribute to the diffusion signal, affecting the specificity of ADC to the tissue microstructure. The areas of coherence in diffusion anisotropy images were small, approximately 1 mm, but the orientation corresponded to stromal orientation patterns on histology.

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Assessing Detection, Discrimination, and Risk of Breast Cancer According to Anisotropy Parameters of Diffusion Tensor Imaging

Cited by 17 publications

References 30 publications

Diagnostic Performance of Diffusion Tensor Imaging for Characterizing Breast Tumors: A Comprehensive Meta-Analysis

Diagnostic Performance of Diffusion Tensor Imaging for Characterizing Breast Tumors: A Comprehensive Meta-Analysis

A Meta-analysis of the Diagnostic Performance of Diffusion MRI for Breast Lesion Characterization

Microstructural models for diffusion MRI in breast cancer and surrounding stroma: an ex vivo study

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