Diffusion kurtosis imaging does not improve differentiation performance of breast lesions in a short clinical protocol

Stability of Radiomic Features against Variations in Lesion Segmentations Computed on Apparent Diffusion Coefficient Maps of Breast Lesions

Diffusion-weighted imaging (DWI) combined with radiomics can aid in the differentiation of breast lesions. Segmentation characteristics, however, might influence radiomic features. To evaluate feature stability, we implemented a standardized pipeline featuring shifts and shape variations of the underlying segmentations. A total of 103 patients were retrospectively included in this IRB-approved study after multiparametric diagnostic breast 3T MRI with a spin-echo diffusion-weighted sequence with echoplanar readout (b-values: 50, 750 and 1500 s/mm2). Lesion segmentations underwent shifts and shape variations, with >100 radiomic features extracted from apparent diffusion coefficient (ADC) maps for each variation. These features were then compared and ranked based on their stability, measured by the Overall Concordance Correlation Coefficient (OCCC) and Dynamic Range (DR). Results showed variation in feature robustness to segmentation changes. The most stable features, excluding shape-related features, were FO (Mean, Median, RootMeanSquared), GLDM (DependenceNonUniformity), GLRLM (RunLengthNonUniformity), and GLSZM (SizeZoneNonUniformity), which all had OCCC and DR > 0.95 for both shifting and resizing the segmentation. Perimeter, MajorAxisLength, MaximumDiameter, PixelSurface, MeshSurface, and MinorAxisLength were the most stable features in the Shape category with OCCC and DR > 0.95 for resizing. Considering the variability in radiomic feature stability against segmentation variations is relevant when interpreting radiomic analysis of breast DWI data.

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.

Diffusion kurtosis imaging and intravoxel incoherent motion imaging parameters in breast lesions: Effect of radiologists' experience and region-of-interest selection

PURPOSE

To investigate the influence of ROI placement methods and radiologists' experience on diffusion kurtosis imaging (DKI) and intravoxel incoherent motion (IVIM) parameters' diagnostic performance in differentiating benign and malignant lesions based on the mass and non-mass enhancement (NME).

METHODS

We evaluated 138 lesions in 131 patients retrospectively. The IVIM and DKI parameter values were measured by three radiologists with different experiences independently using two different ROI placement methods. IVIM parameters include diffusion coefficient (ADCstand), true diffusion coefficient (ADCslow), pseudo-diffusion coefficient (ADCfast) and perfusion fraction (f). DKI parameters include mean diffusivity (MD) and mean kurtosis (MK). Each radiologist measured the lesions twice with a 3-month interval. We utilized intra-class correlation (ICC) to determine the inter- and intra-reader agreement for mass and NME, respectively. ROC analysis compared the diagnostic performance of parameters between different radiologists, ROI methods, and between mass and NME.

RESULTS

In mass lesions, inter- and intra-observer agreement were perfect for all parameters (ICC: 0.800-989). In NME, the inter-observer agreement was substantial to perfect for all parameters(ICC: 0.703-877), the intra-observer agreement of the senior and intermediate radiologists was substantial to perfect(ICC: 0.748-931) and the intra-observer agreement of the junior radiologist was moderate to substantial(ICC: 0.569-784). The diagnostic performance of ADCslow (Z = 2.209, P = 0.023), MD (mean diffusivity) (Z = 2.887, P = 0.004), and MK (mean kurtosis) (Z = 2.080, P = 0.038) in the small ROI measured by the senior radiologist was better than that of the junior radiologist for NME. The diagnostic performance of ADCslow in the large ROI measured by the senior radiologist (Z = 2.281, P = 0.023) and intermediate radiologist (Z = 2.867, P = 0.0041) was better than the junior radiologist for mass lesions. The diagnostic performance of ADCslow, ADCstand, MD, and MK did not show a significant difference between the two ROI placement methods (P > 0.05).

CONCLUSION

The observers' experience can influence the ROI selection and the diagnostic performance of ADCslow, ADCstand, MD, and MK measured using different methods show equal diagnostic performance.

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.

DWI of the Breast - Possibilities and Limitations

BACKGROUND

 The MRI of the breast is of great importance in the diagnosis of disorders of the breast. This can be stated for the primary diagnosis as well as the follow up. Of special interest is diffusion weighted imaging (DWI), which has an increasingly important role. The present review provides results regarding the diagnostic and prognostic relevance of DWI for disorders of the breast.

METHODS

 Under consideration of the recently published literature, the clinical value of DWI of the breast is discussed. Several diagnostic applications are shown, especially for the primary diagnosis of unclear tumors of the breast, the prediction of the axillary lymph node status and the possibility of a native screening. Moreover, correlations between DWI and histopathology features and treatment prediction with DWI are provided.

RESULTS

 Many studies have shown the diagnostic value of DWI for the primary diagnosis of intramammary lesions. Benign lesions of the breast have significantly higher apparent diffusion coefficients (ADC values) compared to malignant tumors. This can be clinically used to reduce unnecessary biopsies in clinical routine. However, there are inconclusive results for the prediction of the histological subtype of the breast cancer. DWI can aid in the prediction of treatment to neoadjuvant chemotherapy.

CONCLUSION

 DWI is a very promising imaging modality, which should be included in the standard protocol of the MRI of the breast. DWI can provide clinically value in the diagnosis as well as for prognosis in breast cancer.

KEY POINTS

  · DWI can aid in the discrimination between benign and malignant tumors of the breast and therefore avoiding unnecessary biopsies.. · The ADC value cannot discriminate between immunhistochemical subtypes of the breast cancer. · The ADC value of breast cancer increases under neoadjuvant chemotherapy and can by this aid in treatment prediction.. · There is definite need of standardisation for clinical translation.

CITATION FORMAT

· Meyer HJ, Martin M, Denecke T. DWI of the Breast - Possibilities and Limitations. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1775-8572.

Evaluation of suspicious breast lesions with diffusion kurtosis MR imaging and connection with prognostic factors

PURPOSE

To investigate the additional value of DKI in discriminating suspicious breast lesions on DCE-MRI, as compared with conventional DWI; and to explore connection between DKI-parameters and prognostic factors of breast cancers.

METHODS

The institutional review board approved this retrospective study and written informed consent was waived. Totally, 300 women (mean age, 43.2 ± 10.4 years) with suspicious breast lesions on DCE-MRI were enrolled from November 2014 to September 2019. With pathology as reference, performance of ADC, K_app and D_app in discriminating suspicious breast lesions were analyzed by receiver operating characteristic (ROC) analysis with area under ROC curve (AUC). The specificities of parameters were compared by Chi-square test. The ADC, K_app and D_app of breast cancers with different receptor status were compared using Student's t or Mann-Whitney U or Kruskal-Wallis test.

RESULTS

There were 344 suspicious breast lesions (220 malignant, 124 benign) in 300 women. No significant differences were found for AUCs of ADC and DKI-parameters in discriminating suspicious breast lesions (0.882 vs. 0.888, p = 0.480). The specificities were significantly higher with ADC and D_app than that with DCE-MRI (p = 0.003 and 0.005). The ADC, K_app and D_app were correlated with HER2 expression and lymph node status, and ADC and K_app differed between ER-positive and negative tumors (all p < 0.05). Except K_app, DKI/DWI-parameters showed relation with Ki-67 expression. None of the DKI/DWI-parameters showed relation with lesion grade (all p > 0.05).

CONCLUSION

The more complicated and time-consuming DKI is not superior to conventional DWI in differentiating suspicious breast lesions and reflecting prognostic information of breast cancer.

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.

Ultra-High b-Value Diffusion-Weighted Imaging-Based Abbreviated Protocols for Breast Cancer Detection

OBJECTIVES

Contrast-enhanced (CE) magnetic resonance imaging (MRI) is the most effective imaging modality for breast cancer detection. A contrast agent-free examination technique would be desirable for breast MRI screening. The purpose of this study was to evaluate the capability to detect and characterize suspicious breast lesions with an abbreviated, non-contrast-enhanced MRI protocol featuring ultra-high b-value diffusion-weighted imaging (DWI) compared with CE images.

MATERIALS AND METHODS

The institutional review board-approved prospective study included 127 female subjects with different clinical indications for breast MRI. Magnetic resonance imaging examinations included DWI sequences with b-values of 1500 s/mm2 (b1500) and 2500 s/mm2 (b2500), native T1- and T2-weighted images, and CE sequences at 1.5 T and 3 T scanners. Two reading rounds were performed, including either the b1500 or the b2500 DWI in consecutive assessment steps: (A) maximum intensity projections (MIPs) of DWI, (B) DWI and apparent diffusion coefficient maps, (C) as (B) but with additional native T1- and T2-weighted images, and (D) as (C) but with additional CE images (full-length protocol). Two readers independently determined the presence of a suspicious lesion. Histological confirmation was obtained for conspicuous lesions, whereas the full MRI data set was obtained for inconspicuous and clearly benign lesions. Statistical analysis included calculation of diagnostic accuracy and interrater agreement via the intraclass correlation coefficient.

RESULTS

The cohort comprised 116 cases with BI-RADS 1 findings and 138 cases with BI-RADS ≥2 findings, including 38 histologically confirmed malignancies. For (A), breasts without pathological findings could be recognized with high diagnostic accuracy (negative predictive value, ≥97.0%; sensitivity, ≥92.1% for both readers), but with a limited specificity (≥58.3%; positive predictive value, ≥28.6%). Within the native readings, approach (C) with b2500 performed best (negative predictive value, 99.5%; sensitivity, 97.4%; specificity, 88.4%). The intraclass correlation coefficient was between 0.683 (MIP b1500) and 0.996 (full protocol).

CONCLUSIONS

A native abbreviated breast MRI protocol with advanced high b-value DWI might allow nearly equivalent diagnostic accuracy as CE breast MRI and seems to be well suited for lesion detection purposes.

Radiomic Features of T2-weighted Imaging and Diffusion Kurtosis Imaging in Differentiating Clinicopathological Characteristics of Cervical Carcinoma

RATIONALE AND OBJECTIVES

Clinicopathological characteristics including histological subtypes, tumour grades and International Federation of Gynecology and Obstetrics (FIGO) stages are crucial factors in the clinical decision for cervical carcinoma (CC). The purpose of this study was to evaluate the ability of T2-weighted imaging (T2WI) and diffusion kurtosis imaging (DKI) radiomics in differentiating clinicopathological characteristics of CC.

MATERIALS AND METHODS

One hundred and seventeen histologically confirmed CC patients (mean age 56.5 ± 14.0 years) with pre-treatment magnetic resonance imaging were retrospectively reviewed. DKI was acquired with 4 b-values (0-1500 s/mm²). Volumes of interest were contoured around the tumours on T2WI and DKI. Radiomic features including shape, first-order and grey-level co-occurrence matrix with wavelet transforms were extracted. Intraclass correlation coeffient between 2 radiologists was used for features reduction. Feature selection was achieved by elastic net and minimum redundancy maximum relevance. Selected features were used to build random forest (RF) models. The performances for differentiating histological subtypes, tumour grades and FIGO stages were assessed by receiver operating characteristic analysis.

RESULTS

Area under the curves (AUCs) for T2WI-only RF models for discriminating histological subtypes, tumour grades and FIGO stages were 0.762, 0.686, and 0.719. AUCs for DWI-only models were 0.663, 0.645, and 0.868, respectively. AUCs of the combined T2WI and DKI models were 0.823, 0.790, and 0.850, respectively.

CONCLUSION

T2WI and DKI radiomic features could differentiate the clinicopathological characteristics of CC. A combined model showed excellent diagnostic discrimination for histological subtypes, while a DKI-only model presented the best performance in differentiating FIGO stages.

Distinguishing between benign and malignant breast lesions using diffusion weighted imaging and intravoxel incoherent motion: A systematic review and meta-analysis

PURPOSE

We sought to evaluate the diagnostic performance of diffusion weighted imaging (DWI) and intravoxel incoherent motion (IVIM) for distinguishing between benign and malignant breast tumors by performing a meta-analysis.

METHODS

We comprehensively searched the electronic databases PubMed and Embase from January 2000 to April 2020 for studies in English. Studies were included if they reported the sensitivity and specificity for identifying benign and malignant breast lesions using DWI or IVIM. Studies were reviewed according to QUADAS-2. The data inhomogeneity and publication bias were also assessed. In order to explore the influence of different field strengths and different b values on diagnostic efficiency, we conducted subgroup analysis.

RESULTS

We analyzed 79 studies, which included a total of 6294 patients with 4091 malignant lesions and 2793 benign lesions. Overall, the pooled sensitivity and specificity of ADC for detecting malignant breast tumors were 0.87 (0.86-0.88) and 0.80 (0.78-0.81), respectively. The PLR was 5.09 (4.16-6.24); the NLR was 0.15 (0.13-0.18); and the DOR was 38.95 (28.87-52.54). The AUC value was 0.9297. The highest performing parameter for IVIM was tissue diffusivity (D), and the pooled sensitivity and specificity was 0.85 (0.82-0.88) and 0.87(0.83-0.90), respectively; the PLR was 5.65 (3.91-8.18); the NLR was 0.17 (0.12-0.26); and the DOR was 38.44 (23.57-62.69). The AUC value was 0.9265. Most of parameters demonstrated considerable statistically significant heterogeneity (P < 0.05, I²>50 %) except the pooled DOR, PLR of D and the pooled DOR and NLR of D*.

CONCLUSIONS

Our meta-analysis indicated that DWI and IVIM had high sensitivity and specificity in the differential diagnosis of breast lesions; and compared with DWI, IVIM could not further increase the diagnostic performance. There was no significant difference in diagnostic accuracy.

Diffusivity mapping of the ovaries: Variability of apparent diffusion and kurtosis variables over the menstrual cycle and influence of oral contraceptives

PURPOSE

We aimed to investigate whether quantitative diffusivity variables of healthy ovaries vary during the menstrual cycle and to evaluate alterations in women using oral contraceptives (OC).

METHODS

This prospective study (S-339/2016) included 30 healthy female volunteers, with (n = 15) and without (n = 15) intake of OC between 07/2017 and 09/2019. Participants underwent 3T diffusion-weighted MRI (b-values 0-2000 s/mm²) three times during a menstrual cycle (T1 = day 1-5; T2 = day 7-12; T3 = day 19-24). Both ovaries were manually three-dimensionally segmented on b = 1500 s/mm²; apparent diffusion coefficient (ADC) calculation and kurtosis fitting (D_app, K_app) were performed. Differences in ADC, D_app and K_app between time points and groups were compared using repeated measures ANOVA and t-test after Shapiro-Wilk and Brown-Forsythe test for normality and equal variance.

RESULTS

In women with a natural menstrual cycle, ADC and kurtosis variables showed significant changes in ovaries with the dominant follicle between T1 vs T2 and T1 vs T3, whilst no differences were observed between T2 vs T3: ADC ± SD for T1 1.524 ± 0.160, T2 1.737 ± 0.160, and T3 1.747 ± 0.241 μm²/ms (p = 0.01 T2 vs T1; p = 1.0 T2 vs T3, p = 0.003 T3 vs T1); D_app ± SD for T1 2.018 ± 0.140, T2 2.272 ± 0.189, and T3 2.230 ± 0.256 μm²/ms (p = 0.003 T2 vs T1, p = 1.0 T2 vs T3, p = 0.02 T3 vs T1); K_app ± SD for T1 0.614 ± 0.0339, T2 0.546 ± 0.0637, and T3 0.529 ± 0.0567 (p < 0.001 T2 vs T1, p = 0.86 T2 vs T3, p < 0.001 T3 vs T1). No significant differences were found in the contralateral ovaries or in females taking OC.

CONCLUSION

Physiological cycle-dependent changes in quantitative diffusivity variables of ovaries should be considered especially when interpreting radiomics analyses in reproductive women.

Prediction of muscle invasion of bladder cancer: A comparison between DKI and conventional DWI

OBJECTIVES

To prospectively compare the diagnostic efficacy of conventional diffusion-weighted imaging (DWI) and diffusion kurtosis imaging (DKI) in differentiating between muscle-invasive bladder cancer (MIBC) and non-muscle-invasive bladder cancer (NMIBC).

METHODS

Multiple b value DWIs were performed using a 3-T magnetic resonance (MR) imaging unit in fifty-one patients with bladder cancer including MIBC and NMIBC confirmed by histopathological findings. DWI data were postprocessed using mono-exponential and DKI models to calculate the apparent diffusion coefficient (ADC), apparent diffusional kurtosis (K_app), and kurtosis-corrected diffusion coefficient (D_app). Receiver-operating characteristic (ROC) analysis was performed to compare the diagnostic efficacy of all diffusion parameters.

RESULTS

All parameters differed significantly between MIBC and NIMBC including increased K_app, decreased D_app and ADC (all p < 0.001). Only the combination of D_app and K_app was significantly higher than ADC (p < 0.05), whilst D_app and K_app were not statistically different from ADC.

CONCLUSIONS

Both conventional DWI and DKI models are beneficial in differentiating between MIBC and NMIBC, whilst the combination of D_app and K_app can produce a more robust value than conventional ADC value in evaluating aggressiveness of bladder cancer.

Diffusivity in breast malignancies analyzed for b > 1000 s/mm2 at 1 mm in-plane resolutions: Insight from Gaussian and non-Gaussian behaviors

Diffusion-weighted imaging (DWI) can improve breast cancer characterizations, but often suffers from low image quality -particularly at informative b > 1000 s/mm² values. The aim of this study was to evaluate multishot approaches characterizing Gaussian and non-Gaussian diffusivities in breast cancer. This was a prospective study, in which 15 subjects, including 13 patients with biopsy-confirmed breast cancers, were enrolled. DWI was acquired at 3 T using echo planar imaging (EPI) with and without zoomed excitations, readout-segmented EPI (RESOLVE), and spatiotemporal encoding (SPEN); dynamic contrast-enhanced (DCE) data were collected using three-dimensional gradient-echo T₁ weighting; anatomies were evaluated with T₂ -weighted two-dimensional turbo spin-echo. Congruence between malignancies delineated by DCE was assessed against high-resolution DWI scans with b-values in the 0-1800 s/mm² range, as well as against apparent diffusion coefficient (ADC) and kurtosis maps. Data were evaluated by independent magnetic resonance scientists with 3-20 years of experience, and radiologists with 6 and 20 years of experience in breast MRI. Malignancies were assessed from ADC and kurtosis maps, using paired t tests after confirming that these values had a Gaussian distribution. Agreements between DWI and DCE datasets were also evaluated using Sorensen-Dice similarity coefficients. Cancerous and normal tissues were clearly separable by ADCs: by SPEN their average values were (1.03 ± 0.17) × 10^-3 and (1.69 ± 0.19) × 10^-3  mm² /s (p < 0.0001); by RESOLVE these values were (1.16 ± 0.16) × 10^-3 and (1.52 ± 0.14) × 10^-3 (p = 0.00026). Kurtosis also distinguished lesions (K = 0.64 ± 0.15) from normal tissues (K = 0.45 ± 0.05), but only when measured by SPEN (p = 0.0008). The best statistical agreement with DCE-highlighted regions arose for SPEN-based DWIs recorded with b = 1800 s/mm² (Sorensen-Dice coefficient = 0.67); DWI data recorded with b = 850 and 1200 s/mm² , led to lower coefficients. Both ADC and kurtosis maps highlighted the breast malignancies, with ADCs providing a more significant separation. The most promising alternative for contrast-free delineations of the cancerous lesions arose from b = 1800 s/mm² DWI. LEVEL OF EVIDENCE: 2. TECHNICAL EFFICACY STAGE: 3.

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.