Performance Of Diffusion Kurtosis Imaging Research Articles

A comparative study of diffusion kurtosis imaging and diffusion tensor imaging in detecting corticospinal tract impairment in diffuse glioma patients.

This study aimed to investigate the diagnostic performance of diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) in identifying aberrations in the corticospinal tract (CST), whilst elucidating the relationship between abnormalities of CST and patients' motor function. Altogether 21 patients with WHO grade II or grade IV glioma were enrolled and divided into Group 1 and Group 2, according to the presence or absence of preoperative paralysis. DKI and DTI metrics were generated and projected onto the CST. Histograms of the CST along x, y, and z axes were developed based on DKI and DTI metrics, and compared subsequently to determine regions of aberrations on the fibers. The receiver operating characteristic curve was performed to investigate the diagnostic efficacy of DKI and DTI metrics. In Group 1, a significantly lower fractional anisotropy, radial kurtosis and mean kurtosis, and a higher mean diffusivity were found in the ipsilateral CST as compared to the contralateral CST. Significantly higher relative axial diffusivity, relative radial diffusivity, and relative mean diffusivity (rMD) were found in Group 1, as compared to Group 2. The relative volume of ipsilateral CST abnormalities higher than the maximum value of mean kurtosis combined with rMD exhibited the best diagnostic performance in distinguishing dysfunction of CST with an AUC of 0.93. DKI is sensitive in detecting subtle changes of CST distal from the tumor. The combination of DKI and DTI is feasible for evaluating the impairment of the CST.

Improved diagnostic confidence and tumor type prediction in adult-type diffuse glioma by multimodal imaging including DCE perfusion and diffusion kurtosis mapping – A standardized multicenter study

Background and purposeTo evaluate the feasibility of a multimodal approach involving dynamic contrast-enhanced (DCE) perfusion imaging and diffusion kurtosis imaging (DKI) in the preoperative imaging of brain tumors in a multicenter setting, and to evaluate the effect on diagnostic confidence and accuracy for tumor grade and type prediction. Materials and MethodsOne hundred and thirty-three patients with brain tumors were imaged in six hospitals with a standardized multimodal protocol. Standard imaging and six parameter maps derived from DCE and DKI sequences were reviewed off-site by two independent readers. Image quality and diagnostic confidence were evaluated in qualitative analyses. Quantitative analyses were performed to assess diagnostic accuracy and the performance of DKI and DCE parameters for tumor grade differentiation and molecular tumor type determination. ResultsStandardized acquisition of DCE and DKI maps was feasible with excellent image quality. Diagnostic confidence was significantly improved from 85 % to 96 % (p = 0.0005) by additional review of the DCE and DKI maps. The combination of mean kurtosis and CBV was particularly advantageous for differentiating low-grade and high-grade glioma, oligodendroglial vs. astrocytic, and IDH1/2 wild type vs. mutated tumors. ConclusionA multimodal imaging approach with DCE and DKI improves diagnostic confidence and yields higher diagnostic accuracy for predicting tumor grade and type in adult-type glioma.

Retracted: Diagnostic Performance of Diffusion Kurtosis Imaging for Benign and Malignant Breast Lesions: A Systematic Review and Meta-Analysis

Retracted: Diagnostic Performance of Diffusion Kurtosis Imaging for Benign and Malignant Breast Lesions: A Systematic Review and Meta-Analysis

Comparison of Diffusion Kurtosis Imaging and Standard Mono-Exponential Apparent Diffusion Coefficient in Diagnosis of Significant Prostate Cancer-A Correlation with Gleason Score Assessed on Whole-Mount Histopathology Specimens.

The study was undertaken to compare the diagnostic performance of diffusion kurtosis imaging (DKI) with the standard monoexponential (ME) apparent diffusion coefficient (ADC) model in the detection of significant prostate cancer (PCa), using whole-mount histopathology of radical prostatectomy specimens as a reference standard. 155 patients with prostate cancer had undergone multiparametric magnetic resonance imaging (mpMRI) at 3T before prostatectomy. Quantitative diffusion parameters-the apparent diffusion coefficient corrected for non-Gaussian behavior (Dapp), kurtosis (K), ADC1200, and ADC2000 were correlated with Gleason score and compared between cancerous and benign tissue and between GS ≤ 3 + 3 and GS ≥ 3 + 4 tumors. The mean values of all diffusion parameters (Dapp, K, ADC1200, ADC2000) were significantly different both between malignant and benign tissue and between GS ≤ 3 + 3 and GS ≥ 3 + 4 tumors. Although the kurtosis model was better fitted to DWI data, the diagnostic performance in receiver operating characteristic (ROC) analysis of DKI and the standard ADC model in the detection of significant PCa was similar in the peripheral zone (PZ) and in peripheral and transitional zones (TZ) together. In conclusion, our study was not able to demonstrate a clear superiority of the kurtosis model over standard ADC in the diagnosis of significant PCa in PZ and in both zones combined.

Non-invasive evaluation of the pathological and functional characteristics of chronic kidney disease by diffusion kurtosis imaging and intravoxel incoherent motion imaging: comparison with conventional DWI.

To explore the diagnostic performance of diffusion kurtosis imaging (DKI) and incoherent intravoxel movement (IVIM) in evaluating the clinical and pathological characteristics in chronic kidney disease (CKD) compared to conventional diffusion-weighted imaging (DWI). Forty-nine CKD patients and 24 healthy volunteers were included in this retrospective study from September 2020 to September 2021. All participants underwent MRI examinations before percutaneous renal biopsy. Coronal T2WI, axial T1WI and T2WI, and DWI (including IVIM and DKI) sequences obtained in one scan. We measured the apparent diffusion coefficient (ADC), true diffusion coefficient (Dt), pseudo-diffusion coefficient (Dp), perfusion fraction (fp), mean kurtosis (MK), and mean diffusivity (MD) values. One-way analysis of variance, correlation analysis, and receiver operating characteristic curve analysis were used in our study. Cortex and medulla ADC, MK, Dt, fp were significantly different between the healthy volunteers and CKD stages 1-2 (all p < 0.05). All diffusion parameters showed significant differences between CKD stages 1-2 and CKD stages 3-5 (all p < 0.05). Except for the uncorrelation between MDMedulla and vascular lesion score, all other diffusion parameters were low-to-moderately related to clinical and pathological indicators. fpMedulla was the best parameter to differentiate healthy volunteers from CKD stages 1-2. MKCortex was the best parameter to differentiate CKD stages 1-2 from that CKD stages 3-5. Renal cortex and medulla fp, Dt, and MK can provide more valuable information than ADC values for the evaluation of clinical and pathological characteristics of CKD patients, and thus can provide auxiliary diagnosis for fibrosis assessment and clinical management of CKD patients. IVIM and DKI can provide more diagnostic valuable information for CKD patients than conventional DWI.

Efficacy of MR diffusion kurtosis imaging for differentiating low-grade from high-grade glioma before surgery: A systematic review and meta-analysis

BackgroundAccurate discrimination and diagnosis of low-grade glioma (LGG) and high-grade glioma (HGG) before surgery is clinically important because it affects the patient’s outcome and guides the clinicians to select appropriate management. The aim of this study was to evaluate the diagnostic performance of diffusion kurtosis imaging (DKI) for differentiating LGG from HGG. MethodsA literature search of the PubMed, Web of Science, Cochrane Library and EMBASE databases was conducted up to December 15, 2020. Studies that evaluated the diagnostic performance of DKI for differentiating LGG from HGG were selected. Retrieved hits were evaluated by the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Summary sensitivity and specificity were determined, and the data analysis was performed using Stata 14.0 and Review Manager 5.3. ResultsThirteen studies with 705 patients were included. The individual sensitivity and specificity of the 13 studies varied from 71% to 100% for sensitivity and 73–100% for specificity. The pooled sensitivity of DKI was 88% (95% confidence interval [CI], 83–91%), and the pooled specificity was 91% (95% CI, 86–95%). The area under the summary receiver operating characteristic curve was 0.93 (95% CI, 0.90–0.95). The pooled diagnostic odds ratio of DKI was 64.85 (95% CI 38.52–109.19). The levels of heterogeneity for sensitivity and specificity across the included studies were high (I2 =66%) and mild (I2 =47.04%), respectively. The multiple subgroup analyses were driven by DKI technique and study region. ConclusionsDKI demonstrated a high diagnostic performance for differentiation of LGG from HGG.

Renal functional and interstitial fibrotic assessment with non-Gaussian diffusion kurtosis imaging

ObjectivesTo evaluate the application value of diffusion kurtosis imaging (DKI) for monitoring renal function and interstitial fibrosis.MethodsForty-two patients suspected of having primary nephropathy, hypertension or diabetes with impaired renal function were examined with DKI. DKI metrics of renal cortex and medulla on both sides of each patient were measured, including mean kurtosis (MK), axial kurtosis (Ka), radial kurtosis (Kr), mean diffusivity (MD) and fractional anisotropy (FA). The differences in DKI metrics between stable and impaired estimated glomerular filtration rate (eGFR) patients as well as between mild and severe interstitial fibrosis patients were compared. Correlations of DKI metrics with clinical indicators and pathology were analyzed. Diagnostic performance of DKI to assess the degree of renal dysfunction was analyzed.ResultsCortical MK, parenchymal Ka, MD and medullary FA were different in stable vs impaired eGFR patients and mild vs severe interstitial fibrosis patients (all p < .05). Negative correlation was found between Ka and eGFR (cortex: r = − 0.579; medulla: r = − 0.603), between MD and interstitial fibrosis (cortex: r = − 0.899; medulla: r = − 0.770), and positive correlation was found between MD and eGFR (cortex: r = 0.411; medulla: r = 0.344), between Ka and interstitial fibrosis (cortex: r = 0.871; medulla: r = 0.844) (all p < .05). DKI combined with mean arterial blood pressure (MAP) and urea showed good diagnostic power for assessing the degree of renal dysfunction (sensitivity: 90.5%; specificity: 89.5%).ConclusionsNoninvasive DKI has certain application value for monitoring renal function and interstitial fibrosis.

Noninvasive assessment of clinical and pathological characteristics of patients with IgA nephropathy by diffusion kurtosis imaging

ObjectivesTo explore the diagnostic performance of diffusion kurtosis imaging (DKI) in evaluating the clinical and pathological characteristics of patients with immunoglobulin A nephropathy (IgAN) compared with conventional DWI.Materials and methodsA total of 28 IgAN patients and 14 healthy volunteers prospectively underwent MRI examinations including coronal T2WI, axial T1WI, T2WI, and DWI sequences from September 2020 to August 2021. We measured mean kurtosis (MK), mean diffusivity (MD), and apparent diffusion coefficient (ADC) by using MR Body Diffusion Toolbox v1.4.0 (Siemens Healthcare). Patients were divided into three groups according to their estimated glomerular filtration rate (eGFR) (Group1, healthy volunteers without kidney disease or other diseases that affect renal function; Group2, IgAN patients with eGFR > 60 mL/min/1.73 m2; Group3, IgAN patients with eGFR < 60 mL/min/1.73 m2). One-way analysis of variance, Pearson or Spearman correlation, and receiver operating characteristic curves were applied in our statistical analysis.ResultsMKCortex and ADCCortex showed significant differences between the Group1 and Group2. MKCortex, MDCortex, ADCCortex, MKMedulla, and ADCMedulla showed significant differences between Group2 and Group3. MKCortex had the highest correlation with CKD stages (r = 0.749, p < 0.001), and tubulointerstitial lesion score (r = 0.656, p < 0.001). MDCortex had the highest correlation with glomerular lesion score (r = − 0.475, p = 0.011). MKCortex had the highest AUC (AUC = 0.923) for differentiating Group1 from Group2, and MDCortex had the highest AUC (AUC = 0.924) for differentiating Group2 from Group3, followed by MKMedulla (AUC = 0.923).ConclusionsDKI is a feasible and reliable technique that can assess the clinical and pathological characteristics of IgAN patients and can provide more valuable information than conventional DWI, especially MKCortex.

Integration of quantitative diffusion kurtosis imaging and prostate specific antigen in differential diagnostic of prostate cancer.

This study aimed to evaluate the diagnostic performance of diffusion kurtosis imaging (DKI) and prostate-specific antigen (PSA) biomarkers in differentiating prostate cancer (PCa) and benign prostatic hyperplasia (BPH).A total of 43 cases of prostate diseases verified by pathology were enrolled in the present study. These cases were assigned to the BPH group (n = 20, 68.85±10.81 years old) and PCa group (n = 23, 74.13 ± 7.37 years old). All patients underwent routine prostate magnetic resonance imaging and DKI examinations, and the mean diffusivity (MD), mean kurtosis (MK), and fractional anisotropy (FA) values were calculated. Three serum indicators (PSA, free PSA [fPSA], and f/t PSA) were collected. We used univariate logistic regression to analyze the above quantitative parameters between the 2 groups, and the independent factors were further incorporated into the multivariate logistic regression model. The area under the receiver operating characteristic curve (AUC) was used to evaluate the diagnostic efficacy of the single indicator and combined model.The difference in PSA, f/t PSA, MK, and FA between PCa and BPH was statistically significant (P < .05). The AUC for the combined model (f/t PSA, MK, and FA) of 0.972 (95% confidence interval [CI]: 0.928, 1.000) was higher than the AUC of 0.902 (95% CI: 0.801, 1.000) for f/t PSA, 0.833 (95% CI: 0.707, 0.958) for MK, and 0.807 (95% CI: 0.679, 0.934) for FA.The MK and FA values for DKI and f/t PSA effectively identify PCa and BPH, compared to the PSA indicators. Combining DKI and PSA derivatives can further improve the diagnosis efficiency and might help in the clinical setting.

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.

Application of magnetic resonance diffusion kurtosis imaging for distinguishing histopathologic subtypes and grades of rectal carcinoma

BackgroundTo evaluate the diagnostic performance of diffusion kurtosis imaging (DKI) for distinguishing different histopathological subtypes and grades of rectal carcinoma and to compare DKI with conventional diffusion-weighted imaging (DWI).MethodsThis prospective study involved 132 patients with rectal carcinoma, comprising 116 with adenocarcinoma not otherwise specified (AC) and 16 with mucinous carcinoma (MC). High spatial resolution magnetic resonance (MR) and DKI sequences (b values of 0, 600, 1000, 1500 and 2000 s/mm2) were performed for pretreatment evaluation. The mean kurtosis (MK) and mean diffusivity (MD) from DKI and the apparent diffusion coefficient (ADC) from DWI were measured by two experienced radiologists. The Mann-Whitney U test was used to evaluate different histopathological subtypes and grades. Receiver operating characteristic (ROC) curve analyses were performed to compare the diagnostic ability of different quantitative parameters.ResultsThe MD and ADC values were significantly higher for MC than for AC (1.94 ± 0.51 vs. 1.33 ± 0.02 and 1.26 ± 0.64 vs. 0.92 ± 0.01, respectively; P < 0.001). The MK values were significantly lower for MC than for AC (0.66 ± 0.02 vs. 0.93 ± 0.09, P < 0.001). The MK and MD values demonstrated higher sensitivity (94%, both) and specificity (96, 93%, respectively) than the ADC values. However, all the parameters derived from both DKI and DWI showed no significant differences between different histological grades.Conclusions DKI is a more valuable imaging biomarker than conventional DWI for differentiating MC from AC. However, it is still debatable whether DKI is useful for distinguishing different histological grades.

Performance of Diffusion Kurtosis Imaging Versus Diffusion Tensor Imaging in Discriminating Between Benign Tissue, Low and High Gleason Grade Prostate Cancer

To investigate the performance of diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) in discriminating benign tissue, low- and high-grade prostate adenocarcinoma (PCa). Forty-eight patients with biopsy-proven PCa of different Gleason grade (GG), who provided written informed consent, were enrolled. All subjects underwent 3T DWI examinations by using b values 0, 500, 1000, 1500, 2000, and 2500 s/mm2 and six gradient directions. Mean diffusivity, fractional anisotropy (FA), apparent kurtosis (K), apparent kurtosis-derived diffusivity (D), and proxy fractional kurtosis anisotropy (KFA) maps were obtained. Regions of interest were selected in PCa, in the contralateral benign zone, and in the peritumoral area. Histogram analysis was performed by measuring mean, 10th, 25th, and 90th (p90) percentile of the whole-lesion volume. Kruskal-Wallis test with Bonferroni correction was used to assess significant differences between different regions of interest. The correlation between diffusion metrics and GG and between DKI and DTI parameters was evaluated with Pearson's test. ROC curve analysis was carried out to analyze the ability of histogram variables to differentiate low- and high-GG PCa. All metrics significantly discriminated PCa from benign and from peritumoral tissue (except for K, KFAp90, and FA). Kp90 showed the highest correlation with GG and the best diagnostic ability (area under the curve = 0.84) in discriminating low- from high-risk PCa. Compared to DTI, DKI provides complementary and additional information about prostate cancer tissue, resulting more sensitive to PCa-derived modifications and more accurate in discriminating low- and high-risk PCa.

Evaluation of diffusion kurtosis imaging and its combination with diffusion weighted imaging and proton MR spectroscopy in differentiation of breast lesions

Objective To evaluate the diagnostic performance of diffusion kurtosis imaging (DKI) and its combination with DWI and proton MR spectroscopy (1H-MRS) in differentiating malignancy from benign breast lesions. Methods Fifty-three patients with 38 histopathologically confirmed malignant and 15 benign breast lesions were retrospectively studied. The patients were examined by breast MRI at 3.0 T prior to operation, including conventional T1WI, fat-suppression imaging, DWI, DKI and 1H-MRS. The shape and margin of breast lesions, and their corresponding mean values for ADC, mean kurtosis (MK) and mean diffusivity (MD) were determined by two blinded radiologists in consensus. The presence or absence of choline (Cho) peak was identified using LCModel software. Independent-samples t test or χ2 test was performed for the comparison of clinical characteristics, shape and margin of lesions, and imaging parameters between malignancy and benign lesions. ROC analysis was performed to evaluate the diagnostic accuracy of DKI, DWI and 1H-MRS alone or in combination, in comparison with the histopathologic findings. Results The onset age of breast malignancy was higher than that of benign ones, and the difference has statistical significant (P 0.05). The mean ADC,MD and MK of benign lesions were (1.464±0.348) ×10-3mm2/s, (1.726±0.268) ×10-3mm2/s and (0.692±0.227) , the mean ADC,MD and MK of malignancy were (0.963±0.170) ×10-3mm2/s, (1.158±0.262) ×10-3mm2/s and (1.311±0.218) , respectively. Significant differences were obtained between benign and malignant lesions for all parameters (P<0.05) .The area under the ROC curve (AUC) of ADC, MD and MK for differentiating malignancy from benign lesions was 0.913, 0.933 and 0.968, respectively. Taken the maximum Youden's index of MK (1.110) as the ROC optimal cut-off point, MK exhibited better diagnostic sensitivity, specificity and accuracy for distinguishing malignancy from benign lesions [89.5% (34/38) ,93.3% (14/15) and 90.6% (48/53) , respectively], compared with MD and ADC. Multiparametric imaging with combination of DKI, DWI and 1H-MRS improves the diagnostic specificity (with the highest as 100.0%) but decreases the sensitivity (with the highest as 81.6% and lowest as 71.1%), compare with the single parametric imaging. Conclusions MK generated from DKI enables differentiation of breast lesions with a higher diagnostic sensitivity and specificity than DWI and 1H-MRS. DKI combined with DWI and 1H-MRS increase specificity but decrease sensitivity for breast cancer characterization. Key words: Breast neoplasms; Magnetic resonance imaging; Comparative study

In vivo molecular profiling of human glioma using diffusion kurtosis imaging.

The purpose of this study is to assess the diagnostic performance of diffusion kurtosis imaging (DKI) for in vivo molecular profiling of human glioma. Normalized mean kurtosis (MKn) and mean diffusivity (MDn) metrics from DKI were assessed in 50 patients with histopathologically confirmed glioma. The results were compared in regard to the WHO-based histological findings and molecular characteristics leading to integrated diagnosis (Haarlem Consensus): isocitrate-dehydrogenase (IDH1/2) mutation status, alpha-thalassemia/mental retardation syndrome X-linked (ATRX) expression, chromosome 1p/19q loss of heterozygosity (LOH), and O6-methylguanine DNA methyltransferase (MGMT) promoter methylation status. MKn was significantly lower in tumors with IDH1/2 mutation (0.43 ± 0.09) and ATRX loss of expression (0.41 ± 0.11) than in those with IDH1/2 wild type (0.57 ± 0.09, p < 0.001) and ATRX maintained expression (0.51 ± 0.10, p = 0.004), respectively. Regarding the integrated molecular diagnosis, MKn was significantly higher in primary glioblastoma (0.57 ± 0.10) than in astrocytoma (0.39 ± 0.11, p < 0.001) and oligodendroglioma (0.47 ± 0.05, p = 0.003). MK may be used to provide insight into the human glioma molecular profile regarding IDH1/2 mutation status and ATRX expression. Considering the diagnostic and prognostic significance of these molecular markers, MK appears to be a promising in vivo biomarker for glioma. The diagnostic performance of MK seems to fit more with the integrated molecular approach than the conventional histological findings of the current WHO 2007 classification.

Histogram analysis of diffusion kurtosis magnetic resonance imaging in differentiation of pathologic Gleason grade of prostate cancer

ObjectiveTo investigate diagnostic performance of diffusion kurtosis imaging with histogram analysis for stratifying pathologic Gleason grade of prostate cancer (PCa). Materials and methodsThis retrospective study was approved by the institutional review board, and written informed consent was waived. A total of 110 patients pathologically confirmed as having PCa (diameter>0.5cm) underwent preoperative diffusion-weighted magnetic resonance imaging (b value of 0–2,100s/mm2) at 3T. Data were postprocessed by monoexponential and diffusion kurtosis models for quantitation of apparent diffusion coefficients (ADCs), apparent diffusion for Gaussian distribution (Dapp), and apparent kurtosis coefficient (Kapp). The measurement was based on an entire-tumor histogram analysis approach. The ability of imaging indices for differentiating low-grade (LG) PCa (Gleason score [GS]≤6) from intermediate-/high-grade (HG: GS>6) PCa was analyzed by receiver operating characteristic regression. ResultsThere were 49 LG tumors and 77 HG tumors at pathologic findings. HG-PCa had significantly lower ADCs, lower diffusion kurtosis diffusivity (Dapp), and higher kurtosis coefficient (Kapp) in mean, median, 10th, and 90th percentile, with higher Dapp in skewness and kurtosis than LG-PCa (P< 0.05). The 10th ADCs, the 10th Dapp, and the 90th Kapp showed relatively higher area under receiver operating characteristic curve (Az), Youden index, and positive likelihood ratio in stratifying aggressiveness of PCa against other indices. The 90th Kapp showed relatively higher correlation (ρ>0.6) with ordinal GS of PCa; significantly higher Az, sensitivity, and specificity (0.889, 74.1%, and 93.9%, respectively) than the 10th Dapp did (0.765, 61.0%, and 79.6%, respectively; P<0.05); and higher Az and specificity than the 10th ADCs did (0.738 and 71.4%, respectively; P<0.05) in differentiating LG-PCa from HG-PCa. ConclusionsIt demonstrated a good reliability of histogram diffusion kurtosis imaging for stratifying pathologic GS of PCa. The 90th Kapp had better diagnostic performance in differentiating LG-PCa from HG-PCa.