Characterization of breast tumors using diffusion kurtosis imaging (DKI)

Enhanced preoperative prediction of breast lesion pathology, prognostic biomarkers, and molecular subtypes using multiple models diffusion-weighted MR imaging

This study aims to comprehensively evaluate the clinical utility of five diffusion models, including conventional mono-exponential (Mono), intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), stretched exponential (SEM), and continuous-time random-walk (CTRW), for preoperatively predicting of breast lesion pathology, prognostic biomarkers, and molecular subtypes. We retrospectively analyzed 132 patients with pathologically verified breast lesions (41 benign and 91 malignant) who underwent a full protocol preoperative breast MRI protocol, including a diffusion-weighted imaging (DWI) sequence with nine b values (0 to 2000 s/mm2) on a 3.0T MR scanner. The diffusion parameters from each model-Mono (ADC), IVIM (D, D*, f), DKI (MD, MK), SEM (DDC, α) and CTRW (Dm, α, β)-were quantitatively calculated and compared between benign and malignant breast lesions, as well as across different prognostic biomarker statuses in breast cancer, using Mann-Whitney U-tests. For molecular subtypes comparisons, we employed the Kruskal-Wallis test followed by Bonferroni. All parameters, except IVIM-D*, significantly differentiated benign from malignant lesions. Notably, IVIM-D and DKI-MK values were significantly different between estrogen receptor (ER)-positive and ER-negative tumors. Progesterone receptor (PR)-positive cancers exhibited lower Mono-ADC, IVIM-D, DKI-MD, SEM-DDC, CTRW-Dm, and CTRW-α values, alongside higher DKI-MK value compared to PR-negative cancers (p < 0.05). Significant differences in IVIM-D, IVIM-D*, and DKI-MK values were observed between human epidermal growth factor receptor 2 (HER2)-negative and HER2-positive tumors. Furthermore, higher SEM-α and CTRW-β values, along with lower DKI-MD and SEM-DDC values, were noted in the high Ki-67 expression group compared to the low Ki-67 group (p < 0.05). All five diffusion models proved valuable for breast cancer diagnosis, with the CTRW model exhibiting the highest diagnostic performance, although the difference was not statistically significant. The diffusion parameters derived from these models can effectively assist in distinguishing prognostic factors and molecular subtypes of breast cancer.

Biomarkers Predictive of Distant Disease-free Survival Derived from Diffusion-weighted Imaging of Breast Cancer

PURPOSE

To investigate whether intravoxel incoherent motion (IVIM) and/or non-Gaussian diffusion parameters are associated with distant disease-free survival (DDFS) in patients with invasive breast cancer.

METHODS

From May 2013 to March 2015, 101 patients (mean age 60.0, range 28-88) with invasive breast cancer were evaluated prospectively. IVIM parameters (flowing blood volume fraction [fIVIM] and pseudodiffusion coefficient [D*]) and non-Gaussian diffusion parameters (theoretical apparent diffusion coefficient [ADC] at a b value of 0 s/mm2 [ADC0] and kurtosis [K]) were estimated using a diffusion-weighted imaging series of 16 b values up to 2500 s/mm2. Shifted ADC values (sADC200-1500) and standard ADC values (ADC0-800) were also calculated. The Kaplan-Meier method was used to generate survival analyses for DDFS, which were compared using the log-rank test. Univariable Cox proportional hazards models were used to assess any associations between each parameter and distant metastasis-free survival.

RESULTS

The median observation period was 80 months (range, 35-92 months). Among the 101 patients, 12 (11.9%) developed distant metastasis, with a median time to metastasis of 79 months (range, 10-92 months). Kaplan-Meier analysis showed that DDFS was significantly shorter in patients with K > 0.98 than in those with K ≤ 0.98 (P = 0.04). Cox regression analysis showed a marginal statistical association between K and distant metastasis-free survival (P = 0.05).

CONCLUSION

Non-Gaussian diffusion may be associated with prognosis in invasive breast cancer. A higher K may be a marker to help identify patients at an elevated risk of distant metastasis, which could guide subsequent treatment.

Combining b2500 diffusion-weighted imaging with BI-RADS improves the specificity of breast MRI

PURPOSE

The purpose of this study was to evaluate the diagnostic performance of visual assessment of diffusion-weighted images (DWI) obtained with a b value of 2500 s/mm2 in addition to a conventional magnetic resonance imaging (MRI) protocol to characterize breast lesions.

MATERIALS AND METHODS

This single-institution retrospective study included participants who underwent clinically indicated breast MRI and breast biopsy from May 2017 to February 2020. The examination included a conventional MRI protocol including DWI obtained with a b value of 50 s/mm2 (b50DWI) and a b value of 800 s/mm2 (b800DWI) and DWI obtained with a b value of 2500 s/mm2 (b2500DWI). Lesions were classified using Breast Imaging Reporting and Data Systems (BI-RADS) categories. Three independent radiologists assessed qualitatively the signal intensity within the breast lesions relative to breast parenchyma on b2500DW and b800DWI and measured the b50-b800-derived apparent diffusion coefficient (ADC) value. The diagnostic performances of BI-RADS, b2500DWI, b800DWI, ADC and of a model combining b2500DWI and BI-RADS were evaluated using receiver operating characteristic (ROC) curves analysis.

RESULTS

A total of 260 patients with 212 malignant and 100 benign breast lesions were included. There were 259 women and one man with a median age of 53 years (Q1, Q3: 48, 66 years). b2500DWI was assessable in 97% of the lesions. Interobserver agreement for b2500DWI was substantial (Fleiss kappa = 0.77). b2500DWI yielded larger area under the ROC curve (AUC, 0.81) than ADC with a 1 × 10-3 mm2/s threshold (AUC, 0.58; P = 0.005) and than b800DWI (AUC, 0.57; P = 0.02). The AUC of the model combining b2500DWI and BI-RADS was 0.84 (95% CI: 0.79-0.88). Adding b2500DWI to BI-RADS resulted in a significant increase in specificity from 25% (95% CI: 17-35) to 73% (95% CI: 63-81) (P < 0.001) with a decrease in sensitivity from 100% (95% CI: 97-100) to 94% (95% CI: 90-97), (P < 0.001).

CONCLUSION

Visual assessment of b2500DWI has substantial interobserver agreement. Visual assessment of b2500DWI offers better diagnostic performance than ADC and b800DWI. Adding visual assessment of b2500DWI to BI-RADS improves the specificity of breast MRI and could avoid unnecessary biopsies.

A review of quantitative diffusion-weighted MR imaging for breast cancer: Towards noninvasive biomarker

Quantitative diffusion-weighted imaging (DWI) is an important adjunct to conventional breast MRI and shows promise as a noninvasive biomarker of breast cancer in multiple clinical scenarios, from the discrimination of benign and malignant lesions, prediction, and evaluation of treatment response to a prognostic assessment of breast cancer. Various quantitative parameters are derived from different DWI models based on special prior knowledge and assumptions, have different meanings, and are easy to confuse. In this review, we describe the quantitative parameters derived from conventional and advanced DWI models commonly used in breast cancer and summarize the promising clinical applications of these quantitative parameters. Although promising, it is still challenging for these quantitative parameters to become clinically useful noninvasive biomarkers in breast cancer, as multiple factors may result in variations in quantitative parameter measurements. Finally, we briefly describe some considerations regarding the factors that cause variations.

Advances in The Application and Research of Magnetic Resonance Diffusion Kurtosis Imaging in The Musculoskeletal System

Magnetic resonance diffusion kurtosis imaging (DKI) is an emerging magnetic resonance imaging (MRI) technique that can reflect microstructural changes in tissue through non-Gaussian diffusion of water molecules. Compared to traditional diffusion weighted imaging (DWI), the DKI model has shown greater sensitivity for diagnosis of musculoskeletal diseases and can help formulate more reasonable treatment plans. Moreover, DKI is an important auxiliary examination for evaluation of the motor function of the musculoskeletal system. This article briefly introduces the basic principles of DKI and reviews the application and research of DKI in the evaluation of disorders of the musculoskeletal system (including bone tumors, soft tissue tumors, spinal lesions, chronic musculoskeletal diseases, musculoskeletal trauma, and developmental disorders) as well as the normal musculoskeletal tissues. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: 1.

Evaluation of apparent diffusion coefficient of two-dimensional BLADE turbo gradient- and spin-echo diffusion-weighted imaging with a breast phantom

This study aimed to evaluate the reliability of apparent diffusion coefficient (ADC) values generated with two-dimensional turbo gradient- and spin-echo with BLADE trajectory diffusion-weighted imaging (TGSE-BLADE-DWI) sequence using a breast diffusion phantom. TGSE-BLADE-DWI and single-shot spin-echo echo-planar imaging (SS-EPI-DWI) were performed using a 3.0 T magnetic resonance imaging scanner. Concordance rates of ADC values and the signal-to-noise ratio (SNR) were compared between TGSE-BLADE-DWI and SS-EPI-DWI. TGSE-BLADE-DWI provided a higher concordance rate for ADC values than SS-EPI-DWI when b-values > 2000s/mm² and a slice thickness of 1 mm were used. TGSE-BLADE-DWI showed less image distortion than SS-EPI-DWI. The SNR of TGSE-BLADE-DWI was higher than that of SS-EPI-DWI, except at a number of excitations of 7 and a slice thickness of 1 mm. In conclusion, TGSE-BLADE-DWI can offer a better SNR, less distortion, and more reliable ADC measurements than SS-EPI-DWI in a breast phantom.

The road to breast cancer screening with diffusion MRI

Breast cancer is the leading cause of cancer in women with a huge medical, social and economic impact. Mammography (MMG) has been the gold standard method until now because it is relatively inexpensive and widely available. However, MMG suffers from certain limitations, such as exposure to X-rays and difficulty of interpretation in dense breasts. Among other imaging methods, MRI has clearly the highest sensitivity and specificity, and breast MRI is the gold standard for the investigation and management of suspicious lesions revealed by MMG. Despite this performance, MRI, which does not rely on X-rays, is not used for screening except for a well-defined category of women at risk, because of its high cost and limited availability. In addition, the standard approach to breast MRI relies on Dynamic Contrast Enhanced (DCE) MRI with the injection of Gadolinium based contrast agents (GBCA), which have their own contraindications and can lead to deposit of gadolinium in tissues, including the brain, when examinations are repeated. On the other hand, diffusion MRI of breast, which provides information on tissue microstructure and tumor perfusion without the use of contrast agents, has been shown to offer higher specificity than DCE MRI with similar sensitivity, superior to MMG. Diffusion MRI thus appears to be a promising alternative approach to breast cancer screening, with the primary goal of eliminating with a very high probability the existence of a life-threatening lesion. To achieve this goal, it is first necessary to standardize the protocols for acquisition and analysis of diffusion MRI data, which have been found to vary largely in the literature. Second, the accessibility and cost-effectiveness of MRI examinations must be significantly improved, which may become possible with the development of dedicated low-field MRI units for breast cancer screening. In this article, we will first review the principles and current status of diffusion MRI, comparing its clinical performance with MMG and DCE MRI. We will then look at how breast diffusion MRI could be implemented and standardized to optimize accuracy of results. Finally, we will discuss how a dedicated, low-cost prototype of breast MRI system could be implemented and introduced to the healthcare market.

Diagnostic performance of mono-exponential DWI versus diffusion kurtosis imaging in breast lesions: A meta-analysis

BACKGROUND

This meta-analysis aimed to explore the diagnostic value of diffusion kurtosis imaging (DKI) compared to mono-exponential diffusion weighted imaging (DWI) in the diagnosis of breast cancer.

METHODS

A systematic electronic literature search (up to September 2020) was conducted for published English-language studies comparing the diagnostic values of DKI and DWI for the detection of breast cancer. The data of mean kurtosis (MK), mean diffusivity (MD), and apparent diffusion coefficient (ADC) were extracted to construct 2 × 2 contingency tables. The pooled sensitivities, specificities, and areas under the receiver operating characteristic curve (AUCs) were compared between DKI and DWI in the diagnosis of breast cancer.

RESULTS

Eight studies were finally included, with a total of 771 patients in the same population. Pooled sensitivities were 82.0% [95% confidence interval (95% CI), 78.2-85.3%] for ADC, 87.3% (95% CI, 83.9-90.1%) for MK, and 83.9% (95% CI, 80.2-87.1%) for MD. Pooled specificities were 81.1% (95% CI, 76.7-84.9%) for ADC, 85.1% (95% CI, 81.1-88.5%) for MK, and 83.2% (95% CI, 79.0-86.8%) for MD. According to the summary receiver operator characteristic curve analyses, the AUCwas 0.901 for ADC, 0.930 for MK, and 0.918 for MD (ADC vs MK, P = .353; ADC vs MD, P = .611). No notable publication bias was found, while significant heterogeneity was observed.

CONCLUSIONS

Although DKI is feasible for identifying breast cancer, MD and MK offer similar diagnostic performance to ADC values. Thus, we recommend that DKI should not be included in the routine evaluation of breast lesions now.

Differentiating Benign and Malignant Breast Lesions in Diffusion Kurtosis MRI: Does the Averaging Procedure Matter?

BACKGROUND

Diffusion kurtosis imaging (DKI) is used to differentiate between benign and malignant breast lesions. DKI fits are performed either on voxel-by-voxel basis or using volume-averaged signal.

PURPOSE

Investigate and compare DKI parameters' diagnostic performance using voxel-by-voxel and volume-averaged signal fit approach.

STUDY TYPE

Retrospective.

STUDY POPULATION

A total of 104 patients, aged 24.1-86.4 years.

FIELD STRENGTH/SEQUENCE

A 3 T Spin-echo planar diffusion-weighted sequence with b-values: 50 s/mm² , 750 s/mm² , and 1500 s/mm² . Dynamic contrast enhanced (DCE) sequence.

ASSESSMENT

Lesions were manually segmented by M.P. under supervision of S.O. (2 and 5 years of experience in breast MRI). DKI fits were performed on voxel-by-voxel basis and with volume-averaged signal. Diagnostic performance of DKI parameters D K (kurtosis corrected diffusion coefficient) and kurtosis K was compared between both approaches.

STATISTICAL TESTS

Receiver operating characteristics analysis and area under the curve (AUC) values were computed. Wilcoxon rank sum and Students t-test tested DKI parameters for significant (P <0.05) difference between benign and malignant lesions. DeLong test was used to test the DKI parameter performance for significant fit approach dependency. Correlation between parameters of the two approaches was determined by Pearson correlation coefficient.

RESULTS

DKI parameters were significantly different between benign and malignant lesions for both fit approaches. Median benign vs. malignant values for voxel-by-voxel and volume-averaged approach were 2.00 vs. 1.28 ( D K in μm² /msec), 2.03 vs. 1.26 ( D K in μm² /msec), 0.54 vs. 0.90 ( K ), 0.55 vs. 0.99 ( K ). AUC for voxel-by-voxel and volume-averaged fit were 0.9494 and 0.9508 ( D K ); 0.9175 and 0.9298 ( K ). For both, AUC did not differ significantly (P = 0.20). Correlation of values between the two approaches was very high (r = 0.99 for D K and r = 0.97 for K ).

DATA CONCLUSION

Voxel-by-voxel and volume-averaged signal fit approach are equally well suited for differentiating between benign and malignant breast lesions in DKI.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 3.

Diffusion Breast MRI: Current Standard and Emerging Techniques

The role of diffusion weighted imaging (DWI) as a biomarker has been the subject of active investigation in the field of breast radiology. By quantifying the random motion of water within a voxel of tissue, DWI provides indirect metrics that reveal cellularity and architectural features. Studies show that data obtained from DWI may provide information related to the characterization, prognosis, and treatment response of breast cancer. The incorporation of DWI in breast imaging demonstrates its potential to serve as a non-invasive tool to help guide diagnosis and treatment. In this review, current technical literature of diffusion-weighted breast imaging will be discussed, in addition to clinical applications, advanced techniques, and emerging use in the field of radiomics.

Clinical experience of tensor-valued diffusion encoding for microstructure imaging by diffusional variance decomposition in patients with breast cancer

BACKGROUND

Diffusion-weighted imaging plays a key role in magnetic resonance imaging (MRI) of breast tumors. However, it remains unclear how to interpret single diffusion encoding with respect to its link with tissue microstructure. The purpose of this retrospective cross-sectional study was to use tensor-valued diffusion encoding to investigate the underlying microstructure of invasive ductal carcinoma (IDC) and evaluate its potential value in a clinical setting.

METHODS

We retrospectively reviewed biopsy-proven breast cancer patients who underwent preoperative breast MRI examination from July 2020 to March 2021. We reviewed the MRI of 29 patients with 30 IDCs, including analysis by diffusional variance decomposition enabled by tensor-valued diffusion encoding. The diffusion parameters of mean diffusivity (MD), total mean kurtosis (MKT), anisotropic mean kurtosis (MKA), isotropic mean kurtosis (MKI), macroscopic fractional anisotropy (FA), and microscopic fractional anisotropy (µFA) were estimated. The parameter differences were compared between IDC and normal fibroglandular breast tissue (FGBT), as well as the association between the diffusion parameters and histopathologic items.

RESULTS

The mean value of MD in IDCs was significantly lower than that of normal FGBT (1.07±0.27 vs. 1.34±0.29, P<0.001); however, MKT, MKA, MKI, FA, and µFA were significantly higher (P<0.005). Among all the diffusion parameters, MKI was positively correlated with the tumor size on both MRI and pathological specimen (rs=0.38, P<0.05 vs. rs=0.54, P<0.01), whereas MKT had a positive correlation with the tumor size in the pathological specimen only (rs=0.47, P<0.02). In addition, the lymph node (LN) metastasis group had significantly higher MKT, MKA, and µFA compared to the metastasis negative group (P<0.05).

CONCLUSIONS

Tensor-valued diffusion encoding enables a useful non-invasive method for characterizing breast cancers with information on tissue microstructures. Particularly, µFA could be a potential imaging biomarker for evaluating breast cancers prior to surgery or chemotherapy.

XGboost Prediction Model Based on 3.0T Diffusion Kurtosis Imaging Improves the Diagnostic Accuracy of MRI BiRADS 4 Masses

BACKGROUND

The malignant probability of MRI BiRADS 4 breast lesions ranges from 2% to 95%, leading to unnecessary biopsies. The purpose of this study was to construct an optimal XGboost prediction model through a combination of DKI independently or jointly with other MR imaging features and clinical characterization, which was expected to reduce false positive rate of MRI BiRADS 4 masses and improve the diagnosis efficiency of breast cancer.

METHODS

120 patients with 158 breast lesions were enrolled. DKI, Diffusion-weighted Imaging (DWI), Proton Magnetic Resonance Spectroscopy (¹H-MRS) and Dynamic Contrast-Enhanced MRI (DCE-MRI) were performed on a 3.0-T scanner. Wilcoxon signed-rank test and χ2 test were used to compare patient's clinical characteristics, mean kurtosis (MK), mean diffusivity (MD), apparent diffusion coefficient (ADC), total choline (tCho) peak, extravascular extracellular volume fraction (V_e), flux rate constant (K_ep) and volume transfer constant (K^trans). ROC curve analysis was used to analyze the diagnostic performances of the imaging parameters. Spearman correlation analysis was performed to evaluate the associations of imaging parameters with prognostic factors and breast cancer molecular subtypes. The Least Absolute Shrinkage and Selectionator operator (lasso) and the area under the curve (AUC) of imaging parameters were used to select discriminative features for differentiating the breast benign lesions from malignant ones. Finally, an XGboost prediction model was constructed based on the discriminative features and its diagnostic efficiency was verified in BiRADS 4 masses.

RESULTS

MK derived from DKI performed better for differentiating between malignant and benign lesions than ADC, MD, tCho, K_ep and K^trans (p < 0.05). Also, MK was shown to be more strongly correlated with histological grade, Ki-67 expression and lymph node status. MD, MK, age, shape and menstrual status were selected to be the optimized feature subsets to construct an XGboost model, which exhibited superior diagnostic ability for breast cancer characterization and an improved evaluation of suspicious breast tumors in MRI BiRADS 4.

CONCLUSIONS

DKI is promising for breast cancer diagnosis and prognostic factor assessment. An optimized XGboost model that included DKI, age, shape and menstrual status is effective in improving the diagnostic accuracy of BiRADS 4 masses.

Breast imaging: Beyond the detection

Breast cancer is a heterogeneous disease nowadays, including different biological subtypes with a variety of possible treatments, which aim to achieve the best outcome in terms of response to therapy and overall survival. In recent years breast imaging has evolved considerably, and the ultimate goal is to predict these strong phenotypic differences noninvasively. Indeed, breast cancer multiparametric studies can highlight not only qualitative imaging parameters, as the presence/absence of a likely malignant finding, but also quantitative parameters, suggesting clinical-pathological features through the evaluation of imaging biomarkers. A further step has been the introduction of artificial intelligence and in particular radiogenomics, that investigates the relationship between breast cancer imaging characteristics and tumor molecular, genomic and proliferation features. In this review, we discuss the main techniques currently in use for breast imaging, their respective fields of use and their technological and diagnostic innovations.

The predictive value of DKI in breast cancer: Does tumour subtype affect pathological response evaluations?

PURPOSE

To explore the differences in quantitative parameters based on diffusion weighted imaging (DWI) and diffusion kurtosis imaging (DKI) between different immunohistochemical indicator statuses and their predictive value for neoadjuvant chemotherapy (NAC) among different phenotypes of breast cancer.

METHODS

Eighty-one breast cancer patients who underwent NAC were enrolled in this retrospective study. Correlations between diffusion parameters and immunohistochemical indicators were determined using Spearman's test, and receiver operating characteristic (ROC) curves were constructed to assess the apparent diffusion coefficient (ADC), mean diffusivity (MD), and mean kurtosis (MK) in predicting the pathologic complete response (PCR).

RESULTS

Correlations were observed between MK values and hormone receptor (HR) expression (oestrogen receptor (ER): r = 0.315 and progesterone receptor (PR): r = 0.268). The parameters ADC_(0,1000), MK, and MD all showed correlations with Ki67 expression (r = 0.276, 0.316 and - 0.224, respectively). ER and Ki67 expression and the parameters MD and MK were significantly different between the PCR and non-PCR groups (AUC = 0.783, 0.688, 0.649 and 0.684, respectively). After splitting patients into subgroups, no significant differences were observed between the PCR and non-PCR groups with human epidermal growth factor receptor 2 (HER2) + and triple-negative (TN) breast cancer. However, we were surprised to find that ADC_{(0, 1000)}, MD, and MK were significantly different between different remission groups with HR+/HER2+ subtypes, and the AUCs of each parameter reached 0.794, 0.825, and 0.712, respectively.

CONCLUSION

MK was correlated with HR expression. ADC_{(0, 1000)} and DKI were correlated with Ki67 expression. ADC_{(0, 1000)} and the non-Gaussian diffusion model are suitable for predicting PCR in patients with HR+/HER2+ breast cancer before NAC.

Toward more robust and reproducible diffusion kurtosis imaging

PURPOSE

The general utility of diffusion kurtosis imaging (DKI) is challenged by its poor robustness to imaging artifacts and thermal noise that often lead to implausible kurtosis values.

THEORY AND METHODS

A robust scalar kurtosis index can be estimated from powder-averaged diffusion-weighted data. We introduce a novel DKI estimator that uses this scalar kurtosis index as a proxy for the mean kurtosis to regularize the fit.

RESULTS

The regularized DKI estimator improves the robustness and reproducibility of the kurtosis metrics and results in parameter maps with enhanced quality and contrast.

CONCLUSION

Our novel DKI estimator promotes the wider use of DKI in clinical research and potentially diagnostics by improving the reproducibility and precision of DKI fitting and, as such, enabling enhanced visual, quantitative, and statistical analyses of DKI parameters.

Different Attenuation Models of Diffusion-Weighted MR Imaging for the Differentiation of Benign and Malignant Musculoskeletal Tumors

BACKGROUND

Several functional imaging techniques, including monoexponential diffusion-weighted imaging (m-DWI), intravoxel incoherent motion (IVIM), and diffusion kurtosis (DK) imaging, have been used in differentiating benign and malignant musculoskeletal tumors. Combining all three techniques in the same study population may improve differentiation.

PURPOSE

To compare the diagnostic performance of m-DWI, IVIM, and DK models and their combinations in differentiating benign and malignant musculoskeletal tumors.

STUDY TYPE

Prospective.

POPULATION

Fifty patients with benign and malignant musculoskeletal tumors divided into nonmyxoid and nonchondroid and myxoid and/or chondroid subgroups.

FIELD STRENGTH/SEQUENCE

A 1.5 T/m-DWI, IVIM, and DK single-shot spin-echo echo-planar sequences.

ASSESSMENT

Minimum and volumetric values of apparent diffusion coefficient (ADC), pure molecular diffusion (D_ivim ), pseudodiffusion (D*), perfusion fraction (f), diffusion coefficient for kurtosis model (D_K ), and Kurtosis (K) were compared between all benign and malignant tumors. Subgroup analysis was also performed for nonmyxoid and nonchondroid and myxoid and/or chondroid tumors.

STATISTICAL TESTS

Independent samples t-test, Mann-Whitney U test, intraclass correlation coefficient, ROC analysis, and logistic regression analysis. A P value < 0.05 was considered statistically significant.

RESULTS

ADC_min , D_ivim-min , D*_vol , D_K-min, K_vol, and K_min values showed statistically significant differences between all benign and malignant tumors and nonmyxoid and nonchondroid tumor subgroup. K_min showed the highest diagnostic performance in differentiating benign and malignant tumors with AUCs of 0.760 for "all tumors" and 0.825 for the nonmyxoid and nonchondroid tumor subgroup. No significant differences were detected in m-DWI-, IVIM-, and DK-derived parameters for differentiating benign and malignant myxoid and/or chondroid tumors. Only three of 63 combinations of prediction models demonstrated a higher diagnostic performance than K_min ; however, improvements were not significantly different.

DATA CONCLUSION

ADC_min , D_ivim-min , D*_vol , D_K-min , K_vol , and K_min values can be used to differentiate benign and malignant musculoskeletal tumors. Our findings suggest that the added value of multiparametric approach in such differentiation is not significant.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 2.

Multidimensional Diffusion Magnetic Resonance Imaging for Characterization of Tissue Microstructure in Breast Cancer Patients: A Prospective Pilot Study

Diffusion-weighted imaging is a non-invasive functional imaging modality for breast tumor characterization through apparent diffusion coefficients. Yet, it has so far been unable to intuitively inform on tissue microstructure. In this IRB-approved prospective study, we applied novel multidimensional diffusion (MDD) encoding across 16 patients with suspected breast cancer to evaluate its potential for tissue characterization in the clinical setting. Data acquired via custom MDD sequences was processed using an algorithm estimating non-parametric diffusion tensor distributions. The statistical descriptors of these distributions allow us to quantify tissue composition in terms of metrics informing on cell densities, shapes, and orientations. Additionally, signal fractions from specific cell types, such as elongated cells (bin1), isotropic cells (bin2), and free water (bin3), were teased apart. Histogram analysis in cancers and healthy breast tissue showed that cancers exhibited lower mean values of "size" (1.43 ± 0.54 × 10-3 mm2/s) and higher mean values of "shape" (0.47 ± 0.15) corresponding to bin1, while FGT (fibroglandular breast tissue) presented higher mean values of "size" (2.33 ± 0.22 × 10-3 mm2/s) and lower mean values of "shape" (0.27 ± 0.11) corresponding to bin3 (p < 0.001). Invasive carcinomas showed significant differences in mean signal fractions from bin1 (0.64 ± 0.13 vs. 0.4 ± 0.25) and bin3 (0.18 ± 0.08 vs. 0.42 ± 0.21) compared to ductal carcinomas in situ (DCIS) and invasive carcinomas with associated DCIS (p = 0.03). MDD enabled qualitative and quantitative evaluation of the composition of breast cancers and healthy glands.

Discrimination of Breast Cancer from Healthy Breast Tissue Using a Three-component Diffusion-weighted MRI Model

PURPOSE

Diffusion-weighted MRI (DW-MRI) is a contrast-free modality that has demonstrated ability to discriminate between predefined benign and malignant breast lesions. However, how well DW-MRI discriminates cancer from all other breast tissue voxels in a clinical setting is unknown. Here we explore the voxelwise ability to distinguish cancer from healthy breast tissue using signal contributions from the newly developed three-component multi-b-value DW-MRI model.

EXPERIMENTAL DESIGN

Patients with pathology-proven breast cancer from two datasets (n = 81 and n = 25) underwent multi-b-value DW-MRI. The three-component signal contributions C 1 and C 2 and their product, C 1 C 2, and signal fractions F 1, F 2, and F 1 F 2 were compared with the image defined on maximum b-value (DWI max), conventional apparent diffusion coefficient (ADC), and apparent diffusion kurtosis (K app). The ability to discriminate between cancer and healthy breast tissue was assessed by the false-positive rate given a sensitivity of 80% (FPR80) and ROC AUC.

RESULTS

Mean FPR80 for both datasets was 0.016 [95% confidence interval (CI), 0.008-0.024] for C 1 C 2, 0.136 (95% CI, 0.092-0.180) for C 1, 0.068 (95% CI, 0.049-0.087) for C 2, 0.462 (95% CI, 0.425-0.499) for F 1 F 2, 0.832 (95% CI, 0.797-0.868) for F 1, 0.176 (95% CI, 0.150-0.203) for F 2, 0.159 (95% CI, 0.114-0.204) for DWI max, 0.731 (95% CI, 0.692-0.770) for ADC, and 0.684 (95% CI, 0.660-0.709) for K app. Mean ROC AUC for C 1 C 2 was 0.984 (95% CI, 0.977-0.991).

CONCLUSIONS

The C 1 C 2 parameter of the three-component model yields a clinically useful discrimination between cancer and healthy breast tissue, superior to other DW-MRI methods and obliviating predefining lesions. This novel DW-MRI method may serve as noncontrast alternative to standard-of-care dynamic contrast-enhanced MRI.

Predicting the effects of radiotherapy based on diffusion kurtosis imaging in a xenograft mouse model of esophageal carcinoma

The aim of the present study was to assess the predictive value of diffusion kurtosis imaging (DKI) on the effects of radiotherapy in a xenograft model of esophageal cancer. A total of 40 tumor-bearing mice, established by injection of Eca-109 cells in nude mice, were used. The experimental group (n=24) received a single dose of 15 Gy (6 MV by X-ray), and the control group (n=16) did not receive any treatment. Tumor volume, apparent diffusion coefficient (ADC), mean kurtosis (MK) and mean diffusivity (MD) of the two groups were compared, and the expression of aquaporin (AQP) 3 and necrosis ratio at matched time points in xenografts were also observed. There was a significant difference between the two groups from the 7th day of radiotherapy onwards; the xenograft volume of the experimental group was significantly smaller compared with the control group (P<0.05). On the 3rd day, the ADC and MD of the experimental group was significantly higher compared with the control group, and MK was significantly lower compared with the control group (P<0.05). On the 3rd day, AQP3 expression in the experimental group was lower compared with the control group, and the proportion of necrotic cells was higher compared with the control group (P<0.05). Single large fraction dose radiotherapy inhibited the growth of a xenografted esophageal tumor. Changes in ADC, MK and MD were observed prior to morphological changes in the tumor. The change in AQP3 expression and necrosis ratio was in also agreement with the DKI parameters assessed. DKI may thus provide early predictive ability on the effect of radiotherapy in esophageal carcinoma.

The Diagnostic Performance of Diffusion Kurtosis Imaging in the Characterization of Breast Tumors: A Meta-Analysis

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.

Influence of residual fat signal on diffusion kurtosis MRI of suspicious mammography findings

Recent studies showed the potential of diffusion kurtosis imaging (DKI) as a tool for improved classification of suspicious breast lesions. However, in diffusion-weighted imaging of the female breast, sufficient fat suppression is one of the main factors determining the success. In this study, the data of 198 patients examined in two study centres was analysed using standard diffusion and kurtosis evaluation methods and three DKI fitting approaches accounting phenomenologically for fat-related signal contamination of the lesions. Receiver operating characteristic curve analysis showed the highest area under the curve (AUC) for the method including fat correction terms (AUC = 0.85, p < 0.015) in comparison to the values obtained with the standard diffusion (AUC = 0.77) and kurtosis approach (AUC = 0.79). Comparing the two study centres, the AUC value improved from 0.77 to 0.86 (p = 0.036) using a fat correction term for the first centre, while no significant difference with no adverse effects was observed for the second centre (AUC 0.89 vs. 0.90, p = 0.95). Contamination of the signal in breast lesions with unsuppressed fat causing a reduction of diagnostic performance of diffusion kurtosis imaging may potentially be counteracted by proposed adapted evaluation methods.

Diffusion MRI of the breast: Current status and future directions

Diffusion-weighted imaging (DWI) is increasingly being incorporated into routine breast MRI protocols in many institutions worldwide, and there are abundant breast DWI indications ranging from lesion detection and distinguishing malignant from benign tumors to assessing prognostic biomarkers of breast cancer and predicting treatment response. DWI has the potential to serve as a noncontrast MR screening method. Beyond apparent diffusion coefficient (ADC) mapping, which is a commonly used quantitative DWI measure, advanced DWI models such as intravoxel incoherent motion (IVIM), non-Gaussian diffusion MRI, and diffusion tensor imaging (DTI) are extensively exploited in this field, allowing the characterization of tissue perfusion and architecture and improving diagnostic accuracy without the use of contrast agents. This review will give a summary of the clinical literature along with future directions. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;52:70-90.

Characterization of breast lesions using diffusion kurtosis model-based imaging: An initial experience

OBJECTIVE

To investigate the characterization of breast lesions using diffusion kurtosis model-based imaging.

METHODS

This prospective study included 120 consecutive patients underwent preoperative DCE-MRI examinations and multi-b-value diffusion-weighted imaging (DWI). Among them, 88 malignant lesions and 44 benign lesions were detected, 56 normal fibroglandular breast tissue were selected as normal control. Conventional apparent diffusion coefficient (ADC), DKI-based parameters mean kurtosis (MK) and mean diffusivity (MD) were analyzed by lesions types and histological subtypes using one-way ANOVA and receiver operating characteristic (ROC) curve.

RESULTS

(1) The malignant group showed significantly lower ADC and MD (1.07±0.32×10-3 mm2/s and 1.30±0.40×10-3 mm2/s, respectively) and higher MK (0.87±0.18) than those in the benign group (1.29±0.26×10-3 mm2/s, 1.62±0.31×10-3 mm2/s and 0.67±0.18) and control group (1.67±0.33×10-3 mm2/s, 2.24±0.28×10-3 mm2/s and 0.52±0.08) with all P < 0.001. (2) Areas under ROC curve (AUC) for diagnosing malignant lesions were 0.936 for MD, 0.911 for MK and 0.897 for ADC, respectively. AUC for MD was significantly higher than that for ADC (P = 0.015). The optimal cut-off value, sensitivity, specificity, positive predictive value, negative predictive value and accuracy were as follow: ADC = 1.18×10-3mm2/s, 78.3%, 93.2%, 81.2%, 81.6%, 81.4%; MD = 1.48×10-3mm2/s, 82.2%, 98.3%, 84.4%, 87.8%, 86.2%; MK = 0.78, 91.5%, 85.3%, 89.0%, 85.8%, 87.2%. (3) Invasive ductal carcinoma (IDC), ductal carcinoma in situ (DCIS) and mucinous adenocarcinoma also showed significant differences among ADC, MD and MK (with P < 0.05).

CONCLUSIONS

MR-DKI parameters enable to improve breast lesion characterization and have diagnostic potential applying to different pathological subtypes of breast cancers.

Diffusion-weighted imaging of the breast-a consensus and mission statement from the EUSOBI International Breast Diffusion-Weighted Imaging working group

The European Society of Breast Radiology (EUSOBI) established an International Breast DWI working group. The working group consists of clinical breast MRI experts, MRI physicists, and representatives from large vendors of MRI equipment, invited based upon proven expertise in breast MRI and/or in particular breast DWI, representing 25 sites from 16 countries. The aims of the working group are (a) to promote the use of breast DWI into clinical practice by issuing consensus statements and initiate collaborative research where appropriate; (b) to define necessary standards and provide practical guidance for clinical application of breast DWI; (c) to develop a standardized and translatable multisite multivendor quality assurance protocol, especially for multisite research studies; (d) to find consensus on optimal methods for image processing/analysis, visualization, and interpretation; and (e) to work collaboratively with system vendors to improve breast DWI sequences. First consensus recommendations, presented in this paper, include acquisition parameters for standard breast DWI sequences including specifications of b values, fat saturation, spatial resolution, and repetition and echo times. To describe lesions in an objective way, levels of diffusion restriction/hindrance in the breast have been defined based on the published literature on breast DWI. The use of a small ROI placed on the darkest part of the lesion on the ADC map, avoiding necrotic, noisy or non-enhancing lesion voxels is currently recommended. The working group emphasizes the need for standardization and quality assurance before ADC thresholds are applied. The working group encourages further research in advanced diffusion techniques and tailored DWI strategies for specific indications.

Key Points

• The working group considers breast DWI an essential part of a multiparametric breast MRI protocol and encourages its use.

• Basic requirements for routine clinical application of breast DWI are provided, including recommendations on b values, fat saturation, spatial resolution, and other sequence parameters.

• Diffusion levels in breast lesions are defined based on meta-analysis data and methods to obtain a reliable ADC value are detailed.

Histogram analysis of diffusion kurtosis imaging based on whole-volume images of breast lesions

BACKGROUND

Breast diffusion kurtosis imaging (DKI) is a novel MRI technique to assess breast cancer but the effectivity still remains to be improved.

PURPOSE

To investigate the performance of whole-volume histogram parameters derived from a DKI model for differentiating benign and malignant breast lesions.

STUDY TYPE

Retrospective.

POPULATION

In all, 120 patients with breast lesions (62 malignant, 58 benign).

SEQUENCE

DKI sequence with seven b-values (0, 500, 1000, 1500, 2000, 2500, and 3000 s/mm² ) and DWI sequence with two b-values (0 and 1000 s/mm² ) on 3.0T MRI.

ASSESSMENT

Histogram parameters of the DKI model (K and D) and the DWI model (ADC), including the minimum, maximum, mean, percentile values (25th, 50th, 75th, and 95th), standard deviation, kurtosis and skewness, were calculated by two radiologists for the whole lesion volume.

STATISTICAL TESTS

Student's t-test was used to compare malignant and benign lesions. The diagnostic performances were evaluated by receiver operating characteristic (ROC) analysis.

RESULTS

K_max , D_min , and ADC_min had the highest area under the curve (AUC) (0.875, 0.830, and 0.847, respectively), sensitivity (85.5%, 74.2%, and 77.4%, respectively), and accuracy (85.0%, 79.2%, and 81.7%, respectively) in their individual histogram parameter groups, and K_max was found to outperform D_min and ADC_min . ADC histogram parameters (from ADC_min to ADC_sd ) were significantly lower than D histogram parameters in all groups.

DATA CONCLUSION

K_max , D_min , and ADC_min were found to be better metrics than the corresponding average values for differentiating benign from malignant tumors. Histogram parameters derived from the DKI model provided more information and had better diagnostic performance than ADC parameters derived from the DWI model.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:627-634.

Diffusional kurtosis imaging for differentiation of additional suspicious lesions on preoperative breast MRI of patients with known breast cancer

PURPOSE

To investigate the potential of diffusional kurtosis imaging (DKI) and conventional diffusion-weighted imaging (DWI) in the evaluation of additional suspicious lesions at preoperative breast magnetic resonance imaging (MRI) in patients with breast cancer.

MATERIALS AND METHODS

Fifty-three additional suspicious lesions in 45 patients with breast cancer, which were detected on preoperative breast MRI, were examined with a 3-T MR system. DKI and DWI data were obtained using a spin-echo single-shot echo-planar imaging sequence with b-values of 0, 50, 600, 1000, and 3000 s/mm². Histogram parameters (mean, standard deviation, minimum, maximum, 10th, 25th, 50th, 75th, 90th percentiles, kurtosis, skewness and entropy) of ADC from DWI and diffusivity (D), kurtosis (K) from DKI were calculated after postprocessing. Parameters were compared between benign vs. ductal carcinoma in situ (DCIS) vs. invasive breast lesions and diagnostic performances were evaluated by receiver operating characteristic (ROC) analysis. Correlation between the mean values of D and K was analyzed according to lesion type.

RESULTS

Multiple histogram parameters of D (mean, 25th, 50th percentile, 75th percentile, and entropy) differed between benign and invasive breast lesions (all P < 0.005), but none differed between benign vs. DCIS. D-90th percentile differed between DCIS vs. invasive cancer (P = 0.040). K-10th percentile differed between benign vs. DCIS (P = 0.015). ADC-75th percentile differed between benign vs. invasive cancer and ADC-75th percentile, ADC-90th percentile differed between DCIS vs. invasive cancer, respectively (all P < 0.005). ROC curve analysis showed high specificity for discrimination between benign and invasive cancer. D-mean and K-mean showed strong correlation in benign (r_s = -0.813) and invasive lesions (r_s = -0.853), but no significant correlation in DCIS.

CONCLUSION

DKI may aid in the differentiation of additional suspicious lesions at preoperative breast MRI. Both ADC and DKI may have lower potential in differentiating DCIS from benign lesions.

Threshold Isocontouring on High b-Value Diffusion-Weighted Images in Magnetic Resonance Mammography

OBJECTIVES

Motivated by the similar appearance of malignant breast lesions in high b-value diffusion-weighted imaging (DWI) and positron emission tomography, the purpose of this work was to evaluate the applicability of a threshold isocontouring approach commonly used in positron emission tomography to analyze DWI data acquired from female human breasts with minimal interobserver variability.

METHODS

Twenty-three female participants (59.4 ± 10.0 years) with 23 lesions initially classified as suggestive of cancers in x-ray mammography screening were subsequently imaged on a 1.5-T magnetic resonance imaging scanner. Diffusion-weighted imaging was performed prior to biopsy with b values of 0, 100, 750, and 1500 s/mm. Isocontouring with different threshold levels was performed on the highest b-value image to determine the voxels used for subsequent evaluation of diffusion metrics. The coefficient of variation was computed by specifying 4 different regions of interest drawn around the lesion. Additionally, a receiver operating statistical analysis was performed.

RESULTS

Using a relative threshold level greater than or equal to 0.85 almost completely suppresses the intra-individual and inter-individual variability. Among 4 studied diffusion metrics, the diffusion coefficients from the intravoxel incoherent motion model returned the highest area under curve value of 0.9. The optimal cut-off diffusivity was found to be 0.85 μm/ms with a sensitivity of 87.5% and specificity of 90.9%.

CONCLUSION

Threshold isocontouring on high b-value maps is a viable approach to reliably evaluate DWI data of suspicious focal lesions in magnetic resonance mammography.

Diffusion Kurtosis at 3.0T as an in vivo Imaging Marker for Breast Cancer Characterization: Correlation With Prognostic Factors

BACKGROUND

Diffusion-kurtosis imaging (DKI) has preliminarily shown promise as a relatively new MRI technique to provide useful information regarding breast lesions, but the diagnostic performance of DKI has not been fully evaluated.

PURPOSE

To compare the diagnostic accuracy of DKI, diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE)-MRI) and proton MR spectroscopy (¹ H-MRS) in differentiating malignant from benign breast lesions independently or jointly, and explore the correlation between DKI-derived parameters and prognostic factors.

STUDY TYPE

Prospective.

SUBJECTS

Seventy-one patients with breast lesions (50 malignant, 26 benign).

SEQUENCE

DKI, DWI, DCE-MRI, and ¹ H-MRS were performed at 3.0T.

ASSESSMENT

Mean kurtosis (MK), mean diffusivity (MD), apparent diffusion coefficient (ADC), BI-RADS category, and choline peaks were analyzed by two experienced radiologists.

STATISTICAL TESTS

Student's t-test was used for continuous variables; receiver operating characteristic (ROC) analysis for assessing the diagnostic accuracy of imaging parameters; Spearman or Pearson correlations for assessing the associations between imaging parameters and prognostic factors.

RESULTS

MK exhibited higher area under the curves (AUCs) for differentiating malignant from benign lesions than did MD, ADC, DCE, and tCho (0.979 vs. 0.928, 0.911, 0.777, and 0.833, respectively, P < 0.05). MK showed a positive association with Ki-67 expression (r = 0.508) and histologic grades (r = 0.551), whereas MD and ADC were negatively correlated with Ki-67 expression (r = -0.416 and r = -0.458) and histologic grades (r = -0.411 and r = -0.319). Moreover, MK showed relatively higher AUCs compared with MD and ADC in detecting breast cancers with lymph nodal involvement, histologic grades, and Ki-67 expression.

DATA CONCLUSION

MK has higher diagnostic accuracy compared with ADC, DCE, and tCho regarding detection of breast cancer. Moreover, DKI shows promise as a quantitative imaging technique for characterizing breast lesions, highlighting the potential utility of MK as a promising imaging marker for predicting tumor aggressiveness.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:845-856.

Differentiation between malignant and benign musculoskeletal tumors using diffusion kurtosis imaging

OBJECTIVE

The purpose of this study was to evaluate differences in parameters of diffusion kurtosis imaging (DKI) and minimum apparent diffusion coefficient (ADC_min) between benign and malignant musculoskeletal tumors.

MATERIALS AND METHODS

In this prospective study, 43 patients were scanned using a DKI protocol on a 3-T MR scanner. Eligibility criteria were: non-fatty, non-cystic soft tissue or osteolytic tumors; > 2 cm; location in the retroperitoneum, pelvis, leg, or neck; and no prior treatment. They were clinically or histologically diagnosed as benign (n = 27) or malignant (n = 16). In the DKI protocol, diffusion-weighted imaging was performed using four b values (0-2000 s/mm²) and 21 diffusion directions. Mean kurtosis (MK) values were calculated on the MR console. A recently developed software application enabling reliable calculation was used for DKI analysis.

RESULTS

MK showed a strong correction with ADC_min (Spearman's rs = 0.95). Both MK and ADC_min values differed between benign and malignant tumors (p < 0.01). For benign and malignant tumors, the mean MK values (± SD) were 0.49 ± 0.17 and 1.14 ± 0.30, respectively, and ADC_min values were 1.54 ± 0.47 and 0.49 ± 0.17 × 10^-3 mm²/s, respectively. At cutoffs of MK = 0.81 and ADC_min = 0.77 × 10^-3 mm²/s, the specificity and sensitivity for diagnosis of malignant tumors were 96.3 and 93.8% for MK and 96.3 and 93.8% for ADC_min, respectively. The areas under the curve were 0.97 and 0.99 for MK and ADC_min, respectively (p = 0.31).

CONCLUSIONS

MK and ADC_min showed high diagnostic accuracy and strong correlation, reflecting the accuracy of MK. However, no clear added value of DKI could be demonstrated in differentiating musculoskeletal tumors.

Diffusional kurtosis imaging in head and neck cancer: On the use of trace-weighted images to estimate indices of non-Gaussian water diffusion

PURPOSE

While previous studies have demonstrated the feasibility and potential usefulness of quantitative non-Gaussian diffusional kurtosis imaging (DKI) of the brain, more recent research has focused on oncological application of DKI in various body regions such as prostate, breast, and head and neck (HN). Given the need to minimize scan time during most routine magnetic resonance imaging (MRI) acquisitions of body regions, diffusion-weighted imaging (DWI) with only three orthogonal diffusion weighting directions (x, y, z) is usually performed. Moreover, as water diffusion within malignant tumors is generically thought to be almost isotropic, DWI with only three diffusion weighting directions is considered sufficient for oncological application and it represents the de facto standard in body DKI. In this context, since the kurtosis tensor and diffusion tensor cannot be obtained, the averages of the three directional (K_x , K_y , K_z ) and (D_x , D_y , D_z ) - namely K and D, respectively - represent the best-possible surrogates of directionless DKI-derived indices of kurtosis and diffusivity, respectively. This would require fitting the DKI model to the diffusion-weighted images acquired along each direction (x, y, z) prior to averaging. However, there is a growing tendency to perform only a single fit of the DKI model to the geometric means of the images acquired with diffusion-sensitizing gradient along (x, y, z), referred to as trace-weighted (TW) images. To the best of our knowledge, no in vivo studies have evaluated how TW images affect estimates of DKI-derived indices of K and D. Thus, the aim of this study was to assess the potential bias and error introduced in estimated K and D by fitting the DKI model to the TW images in HN cancer patients.

METHODS

Eighteen patients with histologically proven malignant tumors of the HN were enrolled in the study. They underwent pretreatment 3 T MRI, including DWI (b-values: 0, 500, 1000, 1500, 2000 s/mm² ). Some patients had multiple lesions, and thus a total of 34 lesions were analyzed. DKI-derived indices were estimated, voxel-by-voxel, using single diffusion-weighted images along (x, y, z) as well as TW images. A comparison between the two estimation methods was performed by calculating the percentage error in D (D_err ) and K (K_err ). Also, diffusivity anisotropy (D_anis ) and diffusional kurtosis anisotropy (K_anis ) were estimated. Agreements between the two estimation methods were assessed by Bland-Altman plots. The Spearman rank correlation test was used to study the correlations between K_err /D_err and D_anis /K_anis. RESULTS: The median (95% confidence interval) K_err and D_err were 5.1% (0.8%, 32.6%) and 1.7% (-2.5%, 5.3%), respectively. A significant relationship was observed between K_err  and D_anis (correlation coefficient R = 0.694, P < 0.0001), as well as between K_err and K_anis (R = 0.848, P < 0.0001).

CONCLUSIONS

In HN cancer, the fit of the DKI model to TW images can introduce bias and error in the estimation of K and D, which may be non-negligible for single lesions, and should hence be adopted with caution.

A biomimetic tumor tissue phantom for validating diffusion-weighted MRI measurements

PURPOSE

To develop a biomimetic tumor tissue phantom which more closely reflects water diffusion in biological tissue than previously used phantoms, and to evaluate the stability of the phantom and its potential as a tool for validating diffusion-weighted (DW) MRI measurements.

METHODS

Coaxial-electrospraying was used to generate micron-sized hollow polymer spheres, which mimic cells. The bulk structure was immersed in water, providing a DW-MRI phantom whose apparent diffusion coefficient (ADC) and microstructural properties were evaluated over a period of 10 months. Independent characterization of the phantom's microstructure was performed using scanning electron microscopy (SEM). The repeatability of the construction process was investigated by generating a second phantom, which underwent high resolution synchrotron-CT as well as SEM and MR scans.

RESULTS

ADC values were stable (coefficients of variation (CoVs) < 5%), and varied with diffusion time, with average values of 1.44 ± 0.03 µm2 /ms (Δ = 12 ms) and 1.20 ± 0.05 µm2 /ms (Δ = 45 ms). Microstructural parameters showed greater variability (CoVs up to 13%), with evidence of bias in sphere size estimates. Similar trends were observed in the second phantom.

CONCLUSION

A novel biomimetic phantom has been developed and shown to be stable over 10 months. It is envisaged that such phantoms will be used for further investigation of microstructural models relevant to characterizing tumor tissue, and may also find application in evaluating acquisition protocols and comparing DW-MRI-derived biomarkers obtained from different scanners at different sites. Magn Reson Med 80:147-158, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

MR diffusion kurtosis imaging for cancer diagnosis: A meta-analysis of the diagnostic accuracy of quantitative kurtosis value and diffusion coefficient

PURPOSE

To perform a meta-analysis for assessing the accuracy of diffusion kurtosis imaging (DKI)-derived quantitative parameters (kurtosis values, K; and corrected diffusion coefficients non-Gaussian bias, D) in separating malignant cancers from benign lesions.

METHODS

Relevant studies were searched in PubMed and Cochrane Library databases and were analyzed by Meta-DiSc software.

RESULTS

Fourteen eligible studies involving 1847 lesions in 1107 patients (895 were benign and 952 were malignant) were included. Pooled analysis showed the sensitivity, specificity, positive likelihood ratio (LR), and negative LR were respectively 0.83 (95% CI, 0.79-0.85), 0.83 (95% CI, 0.80-0.86), 4.61 (95% CI, 2.98-7.14), and 0.22 (95% CI, 0.18-0.28) for K, with the overall area under curve (AUC) of 0.89. The sensitivity, specificity, positive LR, and negative LR were 0.85 (95% CI, 0.80-0.88), 0.85 (95% CI, 0.79-0.89), 6.39 (95% CI, 3.14-12.99), and 0.18 (95% CI, 0.14-0.23) for D, with the overall AUC of 0.92. The sensitivity, specificity, positive LR, and negative LR for apparent diffusion coefficient (ADC) derived from standard diffusion-weighted imaging (DWI) were 0.82 (95% CI, 0.79-0.84), 0.85 (95% CI, 0.82-0.88), 4.75 (95% CI, 3.38-6.68), and 0.24 (95% CI, 0.19-0.29), with the overall AUC of 0.89. The superiority of D to K and ADC was also confirmed by the subgroup analysis of prostate cancer.

CONCLUSION

Our findings suggest that DKI should be added to the routine imaging protocol for screening cancer, with the highest diagnostic accuracy of diffusion coefficients.

Use of diffusion kurtosis imaging and quantitative dynamic contrast-enhanced MRI for the differentiation of breast tumors

BACKGROUND

Breast MRI is a sensitive imaging technique to assess breast cancer but its effectiveness still remains to be improved.

PURPOSE

To evaluate the diagnostic performance of diffusion-weighted imaging (DWI), diffusion kurtosis imaging (DKI), and quantitative dynamic contrast-enhanced (DCE)-MRI in differentiating malignant from benign breast lesions independently or jointly and to explore whether correlations exist among these parameters.

STUDY TYPE

Retrospective.

POPULATION

In all, 106 patients with breast lesions (47 malignant, 59 benign).

SEQUENCE

DKI sequence with seven b values and quantitative DCE sequence on 3.0T MRI.

ASSESSMENT

Diffusion parameters (mean diffusivity [MD], mean diffusivity [MK], and apparent diffusion coefficient [ADC]) from DKI and DWI and perfusion parameters from DCE (K^trans , k_ep , v_e , and v_p ) were calculated by two experienced radiologists after postprocessing. Disagreement between the two observers was resolved by consensus.

STATISTICAL TESTS

The parameters in benign and malignant lesions were compared by Student's t-test. The diagnostic performances of DKI and quantitative DCE, either alone or in combination, were evaluated by receiver operating characteristic (ROC) analysis. The Spearman correlation test was used to evaluate correlations among the diffusion parameters and perfusion parameters.

RESULTS

MK, MD, ADC, K^trans , and k_ep values were significantly different between breast cancer and benign lesions (P < 0.05). MK from DKI demonstrated the highest AUC of 0.849, which is significantly higher than ADC derived from conventional DWI (z = 3.345, P = 0.0008). The specificity of DCE-MRI-derived parameters was improved when combining diffusion parameters, such as ADC and MK. The highest diagnostic specificity (93.2%) was obtained when k_ep and ADC were combined. k_ep was correlated moderately positively with MK (r = 0.516) and moderately negatively with MD (r = -0.527). K^trans was weakly positively correlated with MK with an r of 0.398 and weakly negatively correlated with MD with an r of -0.450.

DATA CONCLUSION

DKI is more valuable than conventional DWI in distinguishing between benign and malignant breast lesions. DKI exhibits promise as a quantitative technique to augment quantitative DCE-MRI. Diffusion parameters derived from DKI were statistically correlated with perfusion parameters from quantitative DCE-MRI.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1358-1366.

Variability of non-Gaussian diffusion MRI and intravoxel incoherent motion (IVIM) measurements in the breast

We prospectively examined the variability of non-Gaussian diffusion magnetic resonance imaging (MRI) and intravoxel incoherent motion (IVIM) measurements with different numbers of b-values and excitations in normal breast tissue and breast lesions. Thirteen volunteers and fourteen patients with breast lesions (seven malignant, eight benign; one patient had bilateral lesions) were recruited in this prospective study (approved by the Internal Review Board). Diffusion-weighted MRI was performed with 16 b-values (0–2500 s/mm² with one number of excitations [NEX]) and five b-values (0–2500 s/mm², 3 NEX), using a 3T breast MRI. Intravoxel incoherent motion (flowing blood volume fraction [fIVIM] and pseudodiffusion coefficient [D*]) and non-Gaussian diffusion (theoretical apparent diffusion coefficient [ADC] at b value of 0 sec/mm² [ADC₀] and kurtosis [K]) parameters were estimated from IVIM and Kurtosis models using 16 b-values, and synthetic apparent diffusion coefficient (sADC) values were obtained from two key b-values. The variabilities between and within subjects and between different diffusion acquisition methods were estimated. There were no statistical differences in ADC₀, K, or sADC values between the different b-values or NEX. A good agreement of diffusion parameters was observed between 16 b-values (one NEX), five b-values (one NEX), and five b-values (three NEX) in normal breast tissue or breast lesions. Insufficient agreement was observed for IVIM parameters. There were no statistical differences in the non-Gaussian diffusion MRI estimated values obtained from a different number of b-values or excitations in normal breast tissue or breast lesions. These data suggest that a limited MRI protocol using a few b-values might be relevant in a clinical setting for the estimation of non-Gaussian diffusion MRI parameters in normal breast tissue and breast lesions.

Radiomics Based on Adapted Diffusion Kurtosis Imaging Helps to Clarify Most Mammographic Findings Suspicious for Cancer

Purpose To evaluate a radiomics model of Breast Imaging Reporting and Data System (BI-RADS) 4 and 5 breast lesions extracted from breast-tissue-optimized kurtosis magnetic resonance (MR) imaging for lesion characterization by using a sensitivity threshold similar to that of biopsy. Materials and Methods This institutional study included 222 women at two independent study sites (site 1: training set of 95 patients; mean age ± standard deviation, 58.6 years ± 6.6; 61 malignant and 34 benign lesions; site 2: independent test set of 127 patients; mean age, 58.2 years ± 6.8; 61 malignant and 66 benign lesions). All women presented with a finding suspicious for cancer at x-ray mammography (BI-RADS 4 or 5) and an indication for biopsy. Before biopsy, diffusion-weighted MR imaging (b values, 0-1500 sec/mm²) was performed by using 1.5-T imagers from different MR imaging vendors. Lesions were segmented and voxel-based kurtosis fitting adapted to account for fat signal contamination was performed. A radiomics feature model was developed by using a random forest regressor. The fixed model was tested on an independent test set. Conventional interpretations of MR imaging were also assessed for comparison. Results The radiomics feature model reduced false-positive results from 66 to 20 (specificity 70.0% [46 of 66]) at the predefined sensitivity of greater than 98.0% [60 of 61] in the independent test set, with BI-RADS 4a and 4b lesions benefiting from the analysis (specificity 74.0%, [37 of 50]; 60.0% [nine of 15]) and BI-RADS 5 lesions showing no added benefit. The model significantly improved specificity compared with the median apparent diffusion coefficient (P < .001) and apparent kurtosis coefficient (P = .02) alone. Conventional reading of dynamic contrast material-enhanced MR imaging provided sensitivity of 91.8% (56 of 61) and a specificity of 74.2% (49 of 66). Accounting for fat signal intensity during fitting significantly improved the area under the curve of the model (P = .001). Conclusion A radiomics model based on kurtosis diffusion-weighted imaging performed by using MR imaging machines from different vendors allowed for reliable differentiation between malignant and benign breast lesions in both a training and an independent test data set. ^© RSNA, 2018 Online supplemental material is available for this article.

Application of non-Gaussian water diffusional kurtosis imaging in the assessment of uterine tumors: A preliminary study

OBJECTIVES

To evaluate the interobserver reliability and value of diffusional kurtosis imaging (DKI) in the assessment of uterine tumors compared with those of conventional diffusion-weighted imaging (DWI).

METHODS

This retrospective study was approved by our institutional review board, which waived the requirement for informed consent. Fifty-eight women (mean age: 55.0 ± 13.6 years; range: 30-89 years) with suspected malignant uterine tumors underwent 3-T magnetic resonance imaging using DKI and DWI. Twelve had coexisting leiomyoma. Two observers analyzed region-of-interest measurements of diffusivity (D), kurtosis (K), and the apparent diffusion coefficient (ADC) of uterine lesions and healthy adjacent tissues. Interobserver agreement was evaluated using the intra-class correlation coefficient (ICC). The mean values were compared using one-way analysis of variance with a post-hoc Tukey's honestly significant difference test. The diagnostic accuracy of D and ADC in differentiating malignant tumors from benign leiomyomas was analyzed using receiver operating characteristic (ROC) analysis.

RESULTS

The ICCs between the two observers in evaluating D, K, and the ADC of the malignant tumors were higher than 0.84, suggesting excellent interobserver agreements. The mean D (×10-3 mm2/s) of uterine cancers (1.05 ± 0.41 and 1.09 ± 0.40 for observers 1 and 2, respectively) were significantly lower than those of leiomyoma (1.40 ± 0.37 and 1.56 ± 0.33, respectively; P < 0.05), healthy myometrium (1.72 ± 0.27 and 1.69 ± 0.30, respectively; P < 0.001), and healthy endometrium (1.53 ± 0.35 and 1.42 ± 0.37, respectively; P < 0.005). There was no significant difference in the area under the ROC curve between D and ADC. The mean K of uterine cancers (0.88 ± 0.28 and 0.90 ± 0.23, respectively) were higher than those of myometrium (0.72 ± 0.10 and 0.73 ± 0.10, respectively; P < 0.001), healthy endometrium (0.65 ± 0.13 and 0.60 ± 0.18, respectively; P < 0.001), and leiomyoma (0.76 ± 0.14 and 0.77 ± 0.16, respectively; not significant, P > 0.1).

CONCLUSIONS

Interobserver agreements in evaluating D, K, and ADC were moderate to excellent. D performed equally to conventional DWI in differentiating between benign and malignant uterine lesions. The mean K of malignant uterine lesions was significantly higher than that of non-tumorous myometrium or endometrium.

Intravoxel Incoherent Motion and Quantitative Non-Gaussian Diffusion MR Imaging: Evaluation of the Diagnostic and Prognostic Value of Several Markers of Malignant and Benign Breast Lesions

Purpose To investigate the performance of integrated approaches that combined intravoxel incoherent motion (IVIM) and non-Gaussian diffusion parameters compared with the Breast Imaging and Reporting Data System (BI-RADS) to establish multiparameter thresholds scores or probabilities by using Bayesian analysis to distinguish malignant from benign breast lesions and their correlation with molecular prognostic factors. Materials and Methods Between May 2013 and March 2015, 411 patients were prospectively enrolled and 199 patients (allocated to training [n = 99] and validation [n = 100] sets) were included in this study. IVIM parameters (flowing blood volume fraction [fIVIM] and pseudodiffusion coefficient [D*]) and non-Gaussian diffusion parameters (theoretical apparent diffusion coefficient [ADC] at b value of 0 sec/mm² [ADC₀] and kurtosis [K]) by using IVIM and kurtosis models were estimated from diffusion-weighted image series (16 b values up to 2500 sec/mm²), as well as a synthetic ADC (sADC) calculated by using b values of 200 and 1500 (sADC_200-1500) and a standard ADC calculated by using b values of 0 and 800 sec/mm² (ADC_0-800). The performance of two diagnostic approaches (combined parameter thresholds and Bayesian analysis) combining IVIM and diffusion parameters was evaluated and compared with BI-RADS performance. The Mann-Whitney U test and a nonparametric multiple comparison test were used to compare their performance to determine benignity or malignancy and as molecular prognostic biomarkers and subtypes of breast cancer. Results Significant differences were found between malignant and benign breast lesions for IVIM and non-Gaussian diffusion parameters (ADC₀, K, fIVIM, fIVIM · D*, sADC_200-1500, and ADC_0-800; P < .05). Sensitivity and specificity for the validation set by radiologists A and B were as follows: sensitivity, 94.7% and 89.5%, and specificity, 75.0% and 79.2% for sADC_200-1500, respectively; sensitivity, 94.7% and 96.1%, and specificity, 75.0% and 66.7%, for the combined thresholds approach, respectively; sensitivity, 92.1% and 92.1%, and specificity, 83.3% and 66.7%, for Bayesian analysis, respectively; and sensitivity and specificity, 100% and 79.2%, for BI-RADS, respectively. The significant difference in values of sADC_200-1500 in progesterone receptor status (P = .002) was noted. sADC_200-1500 was significantly different between histologic subtypes (P = .006). Conclusion Approaches that combined various IVIM and non-Gaussian diffusion MR imaging parameters may provide BI-RADS-equivalent scores almost comparable to BI-RADS categories without the use of contrast agents. Non-Gaussian diffusion parameters also differed by biologic prognostic factors. ^© RSNA, 2017 Online supplemental material is available for this article.

DWI in the Assessment of Breast Lesions

Diffusion-weighted imaging (DWI) holds promise to address some of the shortcomings of routine clinical breast magnetic resonance imaging (MRI) and to expand the capabilities of imaging in breast cancer management. DWI reflects tissue microstructure, and provides unique information to aid in characterization of breast lesions. Potential benefits under investigation include improving diagnostic accuracy and guiding treatment decisions. As a result, DWI is increasingly being incorporated into breast MRI protocols and multicenter trials are underway to validate single-institution findings and to establish clinical guidelines. Advancements in DWI acquisition and modeling approaches are helping to improve image quality and extract additional biologic information from breast DWI scans, which may extend diagnostic and prognostic value.

On a fractional order calculus model in diffusion weighted breast imaging to differentiate between malignant and benign breast lesions detected on X-ray screening mammography

OBJECTIVE

To evaluate a fractional order calculus (FROC) model in diffusion weighted imaging to differentiate between malignant and benign breast lesions in breast cancer screening work-up using recently introduced parameters (βFROC, DFROC and μFROC).

MATERIALS AND METHODS

This retrospective analysis within a prospective IRB-approved study included 51 participants (mean 58.4 years) after written informed consent. All patients had suspicious screening mammograms and indication for biopsy. Prior to biopsy, full diagnostic contrast-enhanced MRI examination was acquired including diffusion-weighted-imaging (DWI, b = 0,100,750,1500 s/mm2). Conventional apparent diffusion coefficient Dapp and FROC parameters (βFROC, DFROC and μFROC) as suggested further indicators of diffusivity components were measured in benign and malignant lesions. Receiver operating characteristics (ROC) were calculated to evaluate the diagnostic performance of the parameters.

RESULTS

29/51 patients histopathologically revealed malignant lesions. The analysis revealed an AUC for Dapp of 0.89 (95% CI 0.80-0.98). For FROC derived parameters, AUC was 0.75 (0.60-0.89) for DFROC, 0.59 (0.43-0.75) for βFROC and 0.59 (0.42-0.77) for μFROC. Comparison of the AUC curves revealed a significantly higher AUC of Dapp compared to the FROC parameters DFROC (p = 0.009), βFROC (p = 0.003) and μFROC (p = 0.001).

CONCLUSION

In contrast to recent description in brain tumors, the apparent diffusion coefficient Dapp showed a significantly higher AUC than the recently proposed FROC parameters βFROC, DFROC and μFROC for differentiating between malignant and benign breast lesions. This might be related to the intrinsic high heterogeneity within breast tissue or to the lower maximal b-value used in our study.

Accuracy of diffusion kurtosis imaging in characterization of breast lesions

OBJECTIVE

The aim of this study was to evaluate the accuracy of diffusion kurtosis in the characterization and differentiation of breast lesions.

METHODS

49 females with 53 breast lesions underwent breast MRI. The MRI magnetic field is 1.5 T, and the protocol is standard MRI sequences, dynamic sequences pre- and post-contrast agent administration and diffusion images. Diffusion kurtosis imaging (DKI) was applied as part of our standard breast MRΙ protocol. Two experienced radiologists on breast MRI, blinded to the final diagnosis, reviewed the parametric maps and placed a volume of interest on all slices including each lesion. Kurtosis [K apparent (K_app)] and corrected apparent diffusion coefficient [D apparent (D_app)] median values were then calculated from the whole-lesion histogram analysis. Receiver-operating characteristic analysis was used to determine the most effective cut-off values for the differentiation between benign and malignant pathologies. Histological analysis of the breast lesions was performed, and further comparative analysis of the results was performed to investigate the accuracy of the method.

RESULTS

Benign (n = 19) and malignant lesions (n = 34) had mean diameters of 20.8 mm (10.1-31.5 mm) and 26.4 mm (10.5-42.3 mm), respectively. The lowest and the highest kurtosis values (K_app) of malignant lesions were significantly higher than those of benign lesions. A cut-off of 0.71 provided specificity of 93.7% and sensitivity 97.1%, and the area under the curve (AUC) was 0.976 (p < 0.0001). The lowest and the highest D_app values of malignant lesions were lower than those of benign lesions. A cut-off value of 1.57 × 10^-3 mm² s^-1 provided specificity of 93.7% and sensitivity of 91.2% with AUC of 0.949 (p < 0.0001).

CONCLUSION

DKI is an accurate additional tool for the characterization and differentiation of breast lesions with high K_app and D_app sensitivity and specificity rates. Advances in knowledge: DKI is able to distinguish benign from malignant breast pathologies. DKI increases the specificity of breast MRI.

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

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.

The potential of multiparametric MRI of the breast

MRI is an essential tool in breast imaging, with multiple established indications. Dynamic contrast-enhanced MRI (DCE-MRI) is the backbone of any breast MRI protocol and has an excellent sensitivity and good specificity for breast cancer diagnosis. DCE-MRI provides high-resolution morphological information, as well as some functional information about neoangiogenesis as a tumour-specific feature. To overcome limitations in specificity, several other functional MRI parameters have been investigated and the application of these combined parameters is defined as multiparametric MRI (mpMRI) of the breast. MpMRI of the breast can be performed at different field strengths (1.5-7 T) and includes both established (diffusion-weighted imaging, MR spectroscopic imaging) and novel MRI parameters (sodium imaging, chemical exchange saturation transfer imaging, blood oxygen level-dependent MRI), as well as hybrid imaging with positron emission tomography (PET)/MRI and different radiotracers. Available data suggest that multiparametric imaging using different functional MRI and PET parameters can provide detailed information about the underlying oncogenic processes of cancer development and progression and can provide additional specificity. This article will review the current and emerging functional parameters for mpMRI of the breast for improved diagnostic accuracy in breast cancer.