The aim was to explore the performance of dynamic contrast-enhanced (DCE) MRI and diffusion kurtosis imaging (DKI) in differentiating the molecular subtypes of adult-type gliomas. A multicenter MRI study with standardized imaging protocols, including DCE-MRI and DKI data of 81 patients with WHO grade 2-4 gliomas, was performed at six centers. The DCE-MRI and DKI parameter values were quantitatively evaluated in ROIs in tumor tissue and contralateral normal-appearing white matter. Binary logistic regression analyses were performed to differentiate between high-grade (HGG) vs. low-grade gliomas (LGG), IDH1/2 wildtype vs. mutated gliomas, and high-grade astrocytic tumors vs. high-grade oligodendrogliomas. Receiver operating characteristic (ROC) curves were generated for each parameter and for the regression models to determine the area under the curve (AUC), sensitivity, and specificity. Significant differences between tumor groups were found in the DCE-MRI and DKI parameters. A combination of DCE-MRI and DKI parameters revealed the best prediction of HGG vs. LGG (AUC = 0.954 (0.900-1.000)), IDH1/2 wildtype vs. mutated gliomas (AUC = 0.802 (0.702-0.903)), and astrocytomas/glioblastomas vs. oligodendrogliomas (AUC = 0.806 (0.700-0.912)) with the lowest Akaike information criterion. The combination of DCE-MRI and DKI seems helpful in predicting glioma types according to the 2021 World Health Organization\'s (WHO) classification.

OBJECTIVE: Parkinson\'s disease (PD) shows small structural changes in nigrostriatal pathways, which can be sensitively captured through diffusion kurtosis imaging (DKI). However, the value of DKI and its radiomic features in the classification performance of PD still need confirmation. This study aimed to compare the diagnostic efficiency of DKI-derived kurtosis metric and its radiomic features with different machine learning models for PD classification.
METHODS: 75 people with PD and 80 healthy individuals had their brains scanned using DKI. These images were pre-processed and the standard atlas were non-linearly registered to them. With the labels in atlas, different brain regions in nigrostriatal pathways, including the caudate nucleus, putamen, pallidum, thalamus, and substantia nigra, were chosen as the region of interests (ROIs) to warped to the native space to measure the mean kurtosis (MK). Additionally, new radiomic features were developed for comparison. To handle the large amount of data, a statistical method called Z-score normalization and another method called LASSO regression were used to simplify the information. From this, a few most important features were chosen, and a combined score called Radscore was calculated using LASSO regression. For the comprehensive analyses, three different conventional machine learning models were then created: logistic regression (LR), support vector machine (SVM), and random forest (RF). To ensure the models were accurate, a process called 10-fold cross-validation was used, where the data were split into 10 parts for training and testing.
RESULTS: Using MK alone, the models achieved good results in correctly identifying PD in the validation set, with LR at 0.90, RF at 0.93, and SVM at 0.90. When the radiomic features were added, the models performed even better, with LR at 0.92, RF at 0.95, and SVM at 0.91. Additionally, a nomogram combining all the information was created to predict the likelihood of someone having PD, which had an AUC of 0.91.
CONCLUSIONS: These findings suggest that the combination of DKI measurements and radiomic features can effectively diagnose PD by providing more detailed information about the brain\'s condition and the processes involved in the disease.

OBJECTIVE: Parkinson\'s disease (PD) involves pathological alterations that include cortical impairments at levels of region and network. However, its microstructural abnormalities remain to be further elucidated via an appropriate diffusion neuroimaging approach. This study aimed to comprehensively demonstrate the microstructural patterns of PD as mapped by diffusion kurtosis imaging (DKI).
METHODS: The microstructure of grey matter in both the PD group and the matched healthy control group was quantified by a DKI metric (mean kurtosis). The intergroup difference and classification performance of global microstructural complexity were analyzed in a voxelwise manner and via a machine learning approach, respectively. The patterns of information flows were explored in terms of structural connectivity, network covariance and modular connectivity.
RESULTS: Patients with PD exhibited global microstructural impairments that served as an efficient diagnostic indicator. Disrupted structural connections between the striatum and cortices as well as between the thalamus and cortices were widely distributed in the PD group. Aberrant covariance of the striatocortical circuitry and thalamocortical circuitry was observed in patients with PD, who also showed disrupted modular connectivity within the striatum and thalamus as well as across structures of the cortex, striatum and thalamus.
CONCLUSIONS: These findings verified the potential clinical application of DKI for the exploration of microstructural patterns in PD, contributing complementary imaging features that offer a deeper insight into the neurodegenerative process.

OBJECTIVE: This study aims to assess the diagnostic capabilities of Diffusion Kurtosis Imaging (DKI) and Intravoxel Incoherent Motion (IVIM) in prostate cancer (PCa) detection and characterization.
METHODS: A comprehensive search was conducted across PubMed, Scopus, Web of Science, and the Cochrane Library for articles published up to September 10, 2023, that evaluated the diagnostic efficacy of MD, MK, Dt, f, and Dp parameters. Data were pooled using a bivariate mixed-effects regression model and analyzed with R software.
RESULTS: In total, 27 studies were included. The analysis revealed distinct diagnostic efficacies for DKI and IVIM. In the overall model, sensitivity and specificity were 0.807 and 0.797, respectively, with prospective studies showing higher specificity (0.858, p = 0.024). The detection model yielded increased sensitivity (0.845) and specificity (0.812), with DKI outperforming IVIM in both metrics (sensitivity: 0.87, p = 0.043; specificity: 0.837, p = 0.26); MD had high sensitivity (0.88) and specificity (0.82), while MK\'s specificity was significantly higher (0.854, p = 0.04); Dp\'s sensitivity was significantly lower (0.64, p = 0.016). In characterization, sensitivity and specificity were 0.708 and 0.735, respectively, with no significant differences between DKI and IVIM or Gleason Scores; MK had higher sensitivity (0.78, p = 0.039), and f\'s sensitivity was significantly lower (0.51, p = 0.019).
CONCLUSIONS: In summary, the study underscores DKI\'s enhanced diagnostic accuracy over IVIM in detecting PCa, with MK standing out for its precision. Conversely, Dp and f lag in diagnostic performance. Despite these promising results, the study highlights the imperative for standardized protocols and study designs to achieve reliable and consistent outcomes.

Diffusion Kurtosis Imaging (DKI)-derived metrics are recognized as indicators of maturation in neonates with low-grade germinal matrix and intraventricular hemorrhage (GMH-IVH). However, it is not yet known if these factors are associated with neurodevelopmental outcomes. The objective of this study was to acquire DKI-derived metrics in neonates with low-grade GMH-IVH, and to demonstrate their association with later neurodevelopmental outcomes. In this prospective study, neonates with low-grade GMH-IVH and control neonates were recruited, and DKI were performed between January 2020 and March 2021. These neonates underwent the Bayley Scales of Infant Development test at 18 months of age. Mean kurtosis (MK), radial kurtosis (RK) and gray matter values were measured. Spearman correlation analyses were conducted for the measured values and neurodevelopmental outcome scores. Forty controls (18 males, average gestational age (GA) 30 weeks ± 1.3, corrected GA at MRI scan 38 weeks ± 1) and thirty neonates with low-grade GMH-IVH (13 males, average GA 30 weeks ± 1.5, corrected GA at MRI scan 38 weeks ± 1). Neonates with low-grade GMH-IVH exhibited lower MK and RK values in the PLIC and the thalamus (P < 0.05). The MK value in the thalamus was associated with Mental Development Index (MDI) (r = 0.810, 95% CI 0.695-0.13; P < 0.001) and Psychomotor Development Index (PDI) (r = 0.852, 95% CI 0.722-0.912; P < 0.001) scores. RK value in the caudate nucleus significantly and positively correlated with MDI (r = 0.496, 95% CI 0.657-0.933; P < 0.001) and PDI (r = 0.545, 95% CI 0.712-0.942; P < 0.001) scores. The area under the curve (AUC) were used to assess diagnostic performance of MK and RK in thalamus (AUC = 0.866, 0.787) and caudate nucleus (AUC = 0.833, 0.671) for predicting neurodevelopmental outcomes. As quantitative neuroimaging markers, MK in thalamus and RK in caudate nucleus may help predict neurodevelopmental outcomes in neonates with low-grade GMH-IVH.

UNASSIGNED: Diffusion magnetic resonance imaging (dMRI) has revealed microstructural changes in white matter (WM) in Huntington\'s disease (HD).
UNASSIGNED: To compare the validities of different dMRI, i.e., diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) in HD.
UNASSIGNED: 22 mutant huntingtin (mHTT) carriers and 14 controls were enrolled. Clinical assessments and dMRI were conducted. Based on CAG-Age Product (CAP) score, mHTT carriers were categorized into high CAP (hCAP) and medium and low CAP (m& lCAP) groups. Spearman analyses were used to explore correlations between imaging parameters in brain regions and clinical assessments. Receiver operating characteristic (ROC) was used to distinguish mHTT carriers from control, and define the HD patients at advanced stage.
UNASSIGNED: Compared to controls, mHTT carriers exhibited WM changes in DKI and DTI. There were 22 more regions showing significant differences in HD detected by MK than FA. Only MK in five brain regions showed significantly difference between any two group, and negatively correlated with the disease burden (r = -0.80 to -0.71). ROC analysis revealed that MK was more sensitive and FA was more specific, while Youden index showed that the integration of FA and MK gave rise to higher authenticities, in distinguishing m& lCAP from controls (Youden Index = 0.786), and discerning different phase of HD (Youden Index = 0.804).
UNASSIGNED: Microstructural changes in WM occur at early stage of HD and deteriorate over the disease progression. Integrating DKI and DTI would provide the best accuracies for differentiating early HD from control and identifying advanced HD.

OBJECTIVE: To assess renal interstitial fibrosis (IF) using diffusion MRI approaches, and explore whether corticomedullary difference (CMD) of diffusion parameters, combination among MRI parameters, or combination with estimated glomerular filtration rate (eGFR) benefit IF evaluation.
METHODS: Forty-two patients with chronic kidney disease were included, undergoing MRI examinations. MRI parameters from apparent diffusion coefficient (ADC), intra-voxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), and diffusion-relaxation correlated spectrum imaging (DR-CSI) were obtained both for renal cortex and medulla. CMD of these parameters was calculated. Pathological IF scores (1-3) were obtained by biopsy. Patients were divided into mild (IF = 1, n = 23) and moderate-severe fibrosis (IF = 2-3, n = 19) groups. Group comparisons for MRI parameters were performed. Diagnostic performances were assessed by the receiver operator\'s curve analysis for discriminating mild from moderate-severe IF patients.
RESULTS: Significant inter-group differences existed for cortical ADC, IVIM-D, IVIM-f, DKI-MD, DR-CSI VB, and DR-CSI VC. Significant inter-group differences existed in ΔADC, ΔMD, ΔVB, ΔVC, ΔQB, and ΔQC. Among the cortical MRI parameters, VB displayed the highest AUC = 0.849, while ADC, f, and MD also showed AUC > 0.8. After combining cortical value and CMD, the diagnostic performances of the MRI parameters were slightly improved except for IVIM-D. Combining VB with f brings the best performance (AUC = 0.903) among MRI bi-variant models. A combination of cortical VB, ΔADC, and eGFR brought obvious improvement in diagnostic performance (AUC 0.963 vs 0.879, specificity 0.826 vs 0.896, and sensitivity 1.000 vs 0.842) than eGFR alone.
CONCLUSIONS: Our study shows promising results for the assessment of renal IF using diffusion MRI approaches.
UNASSIGNED: Our study explores the non-invasive assessment of renal IF, an independent and effective predictor of renal outcomes, by comparing and combining diffusion MRI approaches including compartmental, non-compartmental, and model-free approaches.
CONCLUSIONS: Significant difference exists for diffusion parameters between mild and moderate-severe IF. Generally, cortical parameters show better performance than corresponding CMD. Bi-variant model lifts the diagnostic performance for assessing IF.

To explore the potential of diffusion kurtosis imaging (DKI) for assessing the degree of liver injury in a paracetamol-induced rat model and to simultaneously investigate the effect of intravenous gadoxetate on DKI parameters.
Paracetamol was used to induce hepatoxicity in 39 rats. The rats were pathologically classified into 3 groups: normal (n=11), mild necrosis (n=18), and moderate necrosis (n=10). DKI was performed before and, 15 min, 25 min, and 45 min after gadoxetate administration. Repeated-measures ANOVA with Tukey\'s multiple comparison test was used to investigate the effect of gadoxetate on mean diffusivity (MD) and mean diffusion kurtosis (MK) and to assess the differences in MD and MK among the three groups. A receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic accuracy of the MD values when discriminating between the necrotic groups.
Gadoxetate had no significant effect on either the MD or the MK, and the effect size was small. The MD in the moderate necrosis group was significantly lower than that in the other two groups (F = 13.502, p < 0.001; η2 = 0.428 [95% CI: 0.082-0.637]), while the MK did not significantly differ among the three groups (F = 2.702, p = 0.081; η2 = 0.131 [95% CI: 0.001-0.4003]). The AUCs of MD for discriminating the moderate necrosis or normal group from the other groups were 0.921 (95% CI: 0.832-1.000) and 0.831 (95% CI: 0.701-0.961), respectively.
It would be better to measure the MD and MK before gadoxetate injection. MD showed potential for assessing the degree of liver necrosis in a paracetamol-induced liver injury rat model.

BACKGROUND: There is an urgent need to find a reliable and effective imaging method to evaluate the therapeutic efficacy of immunochemotherapy in advanced non-small cell lung cancer (NSCLC). This study aimed to investigate the capability of intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) histogram analysis based on different region of interest (ROI) selection methods for predicting treatment response to chemoimmunotherapy in advanced NSCLC.
METHODS: Seventy-two stage III or IV NSCLC patients who received chemoimmunotherapy were enrolled in this study. IVIM and DKI were performed before treatment. The patients were classified as responders group and non-responders group according to the Response Evaluation Criteria in Solid Tumors 1.1. The histogram parameters of ADC, Dslow, Dfast, f, Dk and K were measured using whole tumor volume ROI and single slice ROI analysis methods. Variables with statistical differences would be included in stepwise logistic regression analysis to determine independent parameters, by which the combined model was also established. And the receiver operating characteristic curve (ROC) were used to evaluate the prediction performance of histogram parameters and the combined model.
RESULTS: ADC, Dslow, Dk histogram metrics were significantly lower in the responders group than in the non-responders group, while the histogram parameters of f were significantly higher in the responders group than in the non-responders group (all P < 0.05). The mean value of each parameter was better than or equivalent to other histogram metrics, where the mean value of f obtained from whole tumor and single slice both had the highest AUC (AUC = 0.886 and 0.812, respectively) compared to other single parameters. The combined model improved the diagnostic efficiency with an AUC of 0.968 (whole tumor) and 0.893 (single slice), respectively.
CONCLUSIONS: Whole tumor volume ROI demonstrated better diagnostic ability than single slice ROI analysis, which indicated whole tumor histogram analysis of IVIM and DKI hold greater potential than single slice ROI analysis to be a promising tool of predicting therapeutic response to chemoimmunotherapy in advanced NSCLC at initial state.

BACKGROUND: Diffusion kurtosis imaging (DKI) and neurite orientation dispersion and density imaging (NODDI) provide more comprehensive and informative perspective on microstructural alterations of cerebral white matter (WM) than single-shell diffusion tensor imaging (DTI), especially in the detection of crossing fiber. However, studies on systemic lupus erythematosus patients without neuropsychiatric symptoms (non-NPSLE patients) using multi-shell diffusion imaging remain scarce.
METHODS: Totally 49 non-NPSLE patients and 41 age-, sex-, and education-matched healthy controls underwent multi-shell diffusion magnetic resonance imaging. Totally 10 diffusion metrics based on DKI (fractional anisotropy, mean diffusivity, axial diffusivity, radial diffusivity, mean kurtosis, axial kurtosis and radial kurtosis) and NODDI (neurite density index, orientation dispersion index and volume fraction of the isotropic diffusion compartment) were evaluated. Tract-based spatial statistics (TBSS) and atlas-based region-of-interest (ROI) analyses were performed to determine group differences in brain WM microstructure. The associations of multi-shell diffusion metrics with clinical indicators were determined for further investigation.
RESULTS: TBSS analysis revealed reduced FA, AD and RK and increased ODI in the WM of non-NPSLE patients (P < 0.05, family-wise error corrected), and ODI showed the best discriminative ability. Atlas-based ROI analysis found increased ODI values in anterior thalamic radiation (ATR), inferior frontal-occipital fasciculus (IFOF), forceps major (F_major), forceps minor (F_minor) and uncinate fasciculus (UF) in non-NPSLE patients, and the right ATR showed the best discriminative ability. ODI in the F_major was positively correlated to C3.
CONCLUSIONS: This study suggested that DKI and NODDI metrics can complementarily detect WM abnormalities in non-NPSLE patients and revealed ODI as a more sensitive and specific biomarker than DKI, guiding further understanding of the pathophysiological mechanism of normal-appearing WM injury in SLE.