Diffusion-tensor imaging as an adjunct to dynamic contrast-enhanced MRI for improved accuracy of differential diagnosis between breast ductal carcinoma in situ and invasive breast carcinoma

Diffusion tensor imaging: survival analysis prediction in breast cancer patients

PURPOSE

We aimed to explore the performance of diffusion-tensor imaging (DTI) and apparent diffusion coefficient (ADC) parameters in evaluating disease-free survival (DFS) and overall survival (OS) in patients with invasive breast cancer.

MATERIAL AND METHODS

A total of 49 women with invasive breast cancer who were diagnosed between 2017 and 2022 were included. All patients underwent breast magnetic resonance imaging (MRI) with DTI and diffusion-weighted imaging (DWI) features, with examiners blinded to the clinical data. Volume anisotropy (VA), fractional anisotropy (FA), and ADC values were measured to assess intratumoral measured heterogeneity. Correlations and differences in diffusion metrics according to OS and DFS status of the cases were analyzed. The discriminative ability of the quantitative findings was assessed by receiver operating characteristic (ROC) curve analyses and validated in the independent cohort.

RESULTS

We evaluated patients with metastases (n = 13, 36.5%) and those without metastases (n = 36, 73.5%). Differences in the ADC, FA, and VA values were observed. The results of Cox regression survival analysis for all the patients included in the survival analysis revealed that DTI metrics contributed to the prediction of overall survival (OS) in the emerging models (p < 0.05). Both FA and VA were associated with OS (p = 0.037 and p = 0.038, respectively). However, ADC was not associated with OS (p = 0.177) or DFS (p = 0.252).

CONCLUSION

To the best of our knowledge, this is the first study to assess the prognostic value of DTI-MRI in breast cancer with statistical survival analysis techniques. We believe that DTI measurements can be used as a biomarker for OS analysis in breast cancer given the available data.

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

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.

Blood Oxygenation Level-dependent Magnetic Resonance Imaging of Breast Cancer: Correlation with Carbonic Anhydrase IX and Vascular Endothelial Growth Factor

Background:

Blood oxygenation level-dependent magnetic resonance imaging (BOLD-MRI) is a functional MRI technique which involves using the paramagnetic properties of deoxyhemoglobin to image the local tissue oxygen concentration. The purpose of this study was to investigate whether BOLD-MRI could evaluate hypoxia and angiogenesis of breast invasive ductal carcinoma (IDC).

Methods:

Ninety-eight female patients with IDC were retrospectively included in this research. All patients underwent breast BOLD-MRI at 3.0 T before surgery. R2* values of BOLD-MRI were measured. The expression of carbonic anhydrase IX (CA IX) and vascular endothelial growth factor (VEGF) was analyzed by immunohistochemistry. Spearman's correlation analysis was used to correlate R2* value with CA IX and VEGF levels.

Results:

Heterogeneous intensity on BOLD-MRI images was the main finding of IDCs. The mean R2* value was 52.8 ± 18.6 Hz. The R2* values in patients with axillary lymph node metastasis were significantly higher than the R2* values in patients without axillary lymph node metastasis (t = 2.882, P = 0.005). R2* values increased with CA IX level and positively correlated with the level of CA IX (r = 0.616, P < 0.001); however, R2* value had no significantly correlation with the level of VEGF (r = 0.110, P = 0.281).

Conclusion:

BOLD-MRI could noninvasively evaluate chronic hypoxia of IDC, but not angiogenesis.

Diffusion-weighted breast MRI: Clinical applications and emerging techniques

Diffusion-weighted MRI (DWI) holds potential to improve the detection and biological characterization of breast cancer. DWI is increasingly being incorporated into breast MRI protocols to address some of the shortcomings of routine clinical breast MRI. Potential benefits include improved differentiation of benign and malignant breast lesions, assessment and prediction of therapeutic efficacy, and noncontrast detection of breast cancer. The breast presents a unique imaging environment with significant physiologic and inter-subject variations, as well as specific challenges to achieving reliable high quality diffusion-weighted MR images. Technical innovations are helping to overcome many of the image quality issues that have limited widespread use of DWI for breast imaging. Advanced modeling approaches to further characterize tissue perfusion, complexity, and glandular organization may expand knowledge and yield improved diagnostic tools.

LEVEL OF EVIDENCE

5 J. Magn. Reson. Imaging 2016 J. Magn. Reson. Imaging 2017;45:337-355.

Diffusion weighted imaging: Technique and applications

Diffusion weighted imaging (DWI) is a method of signal contrast generation based on the differences in Brownian motion. DWI is a method to evaluate the molecular function and micro-architecture of the human body. DWI signal contrast can be quantified by apparent diffusion coefficient maps and it acts as a tool for treatment response evaluation and assessment of disease progression. Ability to detect and quantify the anisotropy of diffusion leads to a new paradigm called diffusion tensor imaging (DTI). DTI is a tool for assessment of the organs with highly organised fibre structure. DWI forms an integral part of modern state-of-art magnetic resonance imaging and is indispensable in neuroimaging and oncology. DWI is a field that has been undergoing rapid technical evolution and its applications are increasing every day. This review article provides insights in to the evolution of DWI as a new imaging paradigm and provides a summary of current role of DWI in various disease processes.