PURPOSE:To gauge the utility of multiparametric MRI in characterizing pathologic changes after iodinated contrast-induced acute kidney injury (CI-AKI) in rats. METHODS:We randomly grouped 24 rats injected with 8 g iodine/kg of body weight (n = 6 each) and 6 rats injected with saline as controls. All rats underwent T1, T2 mapping and diffusion kurtosis imaging (DKI) after contrast injection at 0 (control), 1, 3, 7, 13 days. T1, T2, and mean kurtosis (MK) values were performed in renal outer/inner stripes of outer medulla (OSOM and ISOM) and cortex (CO), and their diagnosis performance for CI-AKI also been evaluated. Serum creatinine (SCr), insulin-like growth factor-binding protein 7 (IGFBP7), tissue inhibitor metalloproteinase 2 (TIMP-2), aquaporin-1 (AQP1), α-smooth muscle actin (α-SMA), and histologic indices were examined. RESULTS:Compared with controls, urinary concentrations of both TIMP-2 and IGFBP7 were obviously elevated from Day 1 to Day 13 (all p < 0.05). T2 values were significantly higher than control group for Days 1 and 3, and T1 and MK increased were more remarkable at all time points (Days 1-13) in CI-AKI (all p < 0.05) than control group. Changes in T1 and MK strongly correlated with renal injury scores of all anatomical compartments and with expression levels of AQP1 and moderately correlated with α-SMA. Changes in T2 values correlating moderately with renal scores of CO, ISOM and OSOM and AQP1. The MK obtained the highest area under the receiver operating characteristic (ROC) curve of 0.846 with a sensitivity of 70.8 % and specificity of 88.9 %. CONCLUSIONS:Combined use of multiparametric MRI could be a valid noninvasive method for comprehensive monitoring of CI-AKI. Among these parameters, MK may achieve the best diagnostic performance for CI-AKI.

Background: The acute changes that occur in the small-world topology of the brain in concussion patients remain unclear. Here, we investigated acute changes in the small-world organization of brain networks in concussion patients and their influence on persistent post-concussion symptoms. Methods: Eighteen concussion patients and eighteen age-matched controls were enrolled in this study. All participants underwent computed tomography, magnetic resonance imaging (MRI), susceptibility weighted imaging, and blood oxygen level-dependent functional MRI. A complex network analysis method based on graph theory was used to calculate the parameters of small-world networks under different degrees of network sparsity. All subjects were evaluated using the Glasgow Coma Scale and Rivermead Postconcussion Symptom Questionnaire. Results: Compared with the controls, the normalized cluster coefficient (γ) of whole brain networks in patients and the “small-world” index (σ) was slightly enhanced, whereas the standardized minimum path (λ) was slightly shorter. Whole brain effect (Eglobal) and local effect (Elocal) changes were not pronounced. Under the condition of minimum network sparsity (Dmin = 0.13), the numbers of nodes in the “right intraorbital superior frontal gyrus” (Anatomical Automatic Labeling, AAL26), right globus pallidus (AAL76), and bilateral temporal transverse gyrus (AAL79,80) in brain concussion patients were significantly lower. The numbers of nodes in the left subcapital lobe (AAL61) and left occipital gyrus (AAL51) were significantly higher, and the normalized cluster coefficients of the right intraorbital supraphalus (AAL26) and left posterior cingulate gyrus (AAL35) were significantly increased. The normalized clustering coefficients of the right triangular subfrontal gyrus (AAL55) (based on the normalized clustering coefficients of nodes in AAL14) and left sub-parietal lobes (AAL61) were significantly reduced. The mean local effects of nodes in the right intraorbital upper frontal gyrus (AAL26), left posterior cingulate gyrus (AAL35), and bilateral auxiliary motor cortex (AAL19, 20) were enhanced, whereas the mean local effects of the bilateral triangular inferior frontal gyrus (AAL13,14) and left insular cap (AAL11) were reduced (p < 0.05). Conclusions: The overall trend of network topology abnormalities in patients was random, and generalized and local functional abnormalities were seen. Changes in the function and affective circuitry of the resting default network were particularly pronounced in these patients, which we speculate may be one of the main drivers of the cognitive dysfunction and mood changes seen in concussion patients.

We assessed possible brain abnormalities in adult patients with moderate and severe obstructive sleep apnea using the mean kurtosis (MK) from diffusion kurtosis imaging and analyzed the correlation between MK and cognitive function. A total of 30 patients with moderate or severe obstructive sleep apnea and 30 healthy controls evaluated by the Montreal Cognitive Assessment scale were enrolled. All participants underwent diffusion kurtosis imaging and 3-dimensional T1-weighted imaging on a 3.0T magnetic resonance scanner. The MK values of gray and white matter brain regions were compared. Partial correlation analysis was used to analyze the correlation between respiratory sleep parameters/cognitive score and MK values in different brain regions. Compared with the healthy controls, the MK of 20 brain regions (13 after false discovery rate correction) and cognitive scores in the obstructive sleep apnea group were significantly lower. In the obstructive sleep apnea group, apnea-hypopnea index was negatively correlated with the MK in the white matter of the right occipital lobe; lowest oxygen saturation was positively correlated with the MK in the bilateral parietal, precentral, and right postcentral cortex; total score on the Montreal Cognitive Assessment scale was positively correlated with MK in the left hippocampus; language function was positively correlated with MK in the white matter of the left parietal lobe; and delayed recall was positively correlated with the MK in right insula cortex and bilateral cingulate. After false discovery rate correction, only the correlations of lowest oxygen saturation with right precentral gyrus cortex and bilateral parietal cortex were significant. MK values of diffusion kurtosis imaging may provide valuable information in assessing the neurological impacts of obstructive sleep apnea. Zhang N, Peng K, Guo J-X, Liu Q, Xiao A-L, Jing H. Microstructural brain abnormalities and associated neurocognitive dysfunction in obstructive sleep apnea: a pilot study with diffusion kurtosis imaging. J Clin Sleep Med. 2024;20(10):1571–1578.

Abstract Background Differentiation of glioma and solitary brain metastasis (SBM), which requires biopsy or multi-disciplinary diagnosis, remains sophisticated clinically. Histogram analysis of MR diffusion or molecular imaging hasn’t been fully investigated for the differentiation and may have the potential to improve it. Methods A total of 65 patients with newly diagnosed glioma or metastases were enrolled. All patients underwent DWI, IVIM, and APTW, as well as the T1W, T2W, T2FLAIR, and contrast-enhanced T1W imaging. The histogram features of apparent diffusion coefficient (ADC) from DWI, slow diffusion coefficient (Dslow), perfusion fraction (frac), fast diffusion coefficient (Dfast) from IVIM, and MTRasym@3.5ppm from APTWI were extracted from the tumor parenchyma and compared between glioma and SBM. Parameters with significant differences were analyzed with the logistics regression and receiver operator curves to explore the optimal model and compare the differentiation performance. Results Higher ADCkurtosis (P = 0.022), frackurtosis (P<0.001),and fracskewness (P<0.001) were found for glioma, while higher (MTRasym@3.5ppm)10 (P = 0.045), frac10 (P<0.001),frac90 (P = 0.001), fracmean (P<0.001), and fracentropy (P<0.001) were observed for SBM. frackurtosis (OR = 0.431, 95%CI 0.256–0.723, P = 0.002) was independent factor for SBM differentiation. The model combining (MTRasym@3.5ppm)10, frac10, and frackurtosis showed an AUC of 0.857 (sensitivity: 0.857, specificity: 0.750), while the model combined with frac10 and frackurtosis had an AUC of 0.824 (sensitivity: 0.952, specificity: 0.591). There was no statistically significant difference between AUCs from the two models. (Z = -1.14, P = 0.25). Conclusions The frac10 and frackurtosis in enhanced tumor region could be used to differentiate glioma and SBM and (MTRasym@3.5ppm)10 helps improving the differentiation specificity.

Objectives To investigate the application of the three-compartment restriction spectrum imaging (RSI) model, diffusion kurtosis imaging (DKI), and diffusion-weighted imaging (DWI) in predicting Ki-67 status in rectal carcinoma. Methods A total of 80 rectal carcinoma patients, including 47 high-proliferation (Ki-67 > 50%) cases and 33 low-proliferation (Ki-67 ≤ 50%) cases, underwent pelvic MRI were enrolled. Parameters derived from RSI (f1, f2, and f3), DKI (MD and MK), and DWI (ADC) were calculated and compared between the two groups. Logistic regression (LR) analysis was conducted to identify independent predictors and assess combined diagnosis. Area under the receiver operating characteristic curve (AUC), DeLong analysis, and calibration curve analyses were performed to evaluate diagnostic performance. Results The patients with high-proliferation rectal carcinoma exhibited significantly higher f1 and MK values and significantly lower ADC, MD, f2, and f3 values than those with low-proliferation rectal carcinoma (P < 0.05). LR analysis showed that MD, MK, and f2 were independent predictors for Ki-67 status in rectal carcinoma. Moreover, the combination of these three parameters achieved an optimal diagnostic efficacy (AUC = 0.877, sensitivity = 80.85%, specificity = 84.85%) that was significantly better than that obtained using ADC (AUC = 0.783, Z = 2.347, P = 0.019), f2 (AUC = 0.732, Z = 2.762, P = 0.006), and f3 (AUC = 0.700, Z = 3.071, P = 0.002). The combined diagnosis also showed good performance (AUC = 0.859) in the internal validation analysis based on 1000 bootstrap samples, while the calibration curve demonstrated that the combined diagnosis provided good stability. Conclusion RSI, DKI, and DWI can effectively differentiate between patients with high- and low-proliferation rectal carcinoma. Furthermore, the MD, MK, and f2 imaging parameters may be a novel and promising combination biomarker for examining Ki-67 status in rectal carcinoma.

Rationale and Objectives: To evaluate the potential of Synthetic Magnetic Resonance Imaging (SynMRI) in identifying muscular invasion in bladder cancer (BCa), and explore whether there is additional value in combination with the Vesical Imaging-Reporting and Data System (VI-RADS). Methods: In this prospective single-center study, pathologically-confirmed BCa were enrolled between May 2023 and November 2023. All participants underwent preoperative multiparametric MRI, including T1/T2 weighted, SynMRI and diffusion-weighted imaging. T1/T2/PD values and apparent diffusion coefficient (ADC) values were compared between muscle invasive (MIBC) and non-invasive (NMIBC) groups. Receiver operating characteristic (ROC) analysis with the variables and their combination was performed to explore the performance of distinguishing the MIBC from NMIBC, and the ROC curves were compared using DeLong's test. Results: A total of 54 BCa patients were enrolled (38 males; NMIBC/MIBC = 37/19) and all assessed with VI-RADS without dynamic enhanced imaging (DCE). Compared to NMIBC group, MIBC group had significantly larger diameter, higher VI-RADS score, lower T2 and ADC values (P < 0.05). VI-RADS score and T2 showed independent predictive value in differentiating NMIBC and MIBC. The combined model (T2 + VI-RADS + Diameter) resulted in significantly improved specificity (0.842), sensitivity (0.914), and AUC (0.943), in comparison to VI-RADS or ADC alone (P < 0.05). Conclusion: T2 relaxation time can be easily obtained from SynMRI in routine clinical protocol and assist VI-RADS score system without DCE to improve differentiation performance in identifying NMIBC and MIBC.

This study investigated the use of bi-exponential diffusion-weighted imaging (DWI) combined with structural features to differentiate high-grade glioma (HGG) from solitary brain metastasis (SBM). A total of 57 patients (31 HGG, 26 SBM) who underwent pre-surgical multi-b DWI and structural MRI (T1W, T2W, T1W + C) were included. Volumes of interest (VOI) in the peritumoral edema area (PTEA) and enhanced tumor area (ETA) were selected for analysis. Histogram features of slow diffusion coefficient (Dslow), fast diffusion coefficient (Dfast), and perfusion fraction (frac) were extracted. Results showed that HGG patients had higher skewness of Dfast (P = 0.022) and frac (P = 0.077), higher kurtosis of Dslow (P = 0.019) and frac (P = 0.025), and lower entropy of Dslow (P = 0.005) and frac (P = 0.001) within the ETA. Additionally, HGG exhibited lower mean frac in both ETA (P = 0.007) and PTEA (P = 0.017). Combining skewness of frac in ETA with clear tumor margin enhanced diagnostic performance, achieving an optimal AUC of 0.79. These findings suggest that histogram analysis of diffusion and perfusion characteristics in ETA and structural features can effectively differentiate HGG from SBM.

Introduction: Continuous positive airway pressure (CPAP) therapy improves clinical symptoms in patients with obstructive sleep apnea (OSA); however, the mechanism of this clinical improvement and how it may be associated with the restoration of white matter (WM) structures in the brain is unclear. Therefore, this study investigated the relationship between the structural recovery of brain WM and improvements in cognitive function and emotion after long-term (12 months) CPAP treatment in patients with OSA. Methods: We collected data from 17 patients with OSA before and 12 months after CPAP treatment, including sleep monitoring, clinical assessment, and diffusion tensor imaging (DTI) magnetic resonance imaging. Results: We observed a partial reversible recovery of brain WM (mean and radial diffusion coefficients) after treatment. This recovery involved the commissural fibers (cingulum, body of corpus callosum), projection fibers (retrolenticular part of the internal capsule, posterior thalamic radiation, posterior limb of the internal capsule, superior corona radiata, posterior corona radiata), association fibers (external capsule, superior longitudinal fasciculus, inferior longitudinal fasciculus), and other regions. In addition, the improvements in WM fibers in one part of the brain significantly were correlated with the Hamilton Anxiety Scale and Hamilton Depression Scale scores. Discussion: Our results suggest that reversible recovery of reduced brain WM integrity due to OSA may require longer CPAP treatment. Moreover, changes in the integrity of the commissural fibers were associated with emotion regulation. These restored WM areas may explain the cognitive and mood improvements observed after OSA treatment.

BackgroundThe detection rate of lung nodules has increased considerably with CT as the primary method of examination, and the repeated CT examinations at 3 months, 6 months or annually, based on nodule characteristics, have increased the radiation exposure of patients. So, it is urgent to explore a radiation‐free MRI examination method that can effectively address the challenges posed by low proton density and magnetic field inhomogeneities.PurposeTo evaluate the potential of zero echo time (ZTE) MRI in lung nodule detection and lung CT screening reporting and data system (lung‐RADS) classification, and to explore the value of ZTE‐MRI in the assessment of lung nodules.Study TypeProspective.Population54 patients, including 21 men and 33 women.Field Strength/SequenceChest CT using a 16‐slice scanner and ZTE‐MRI at 3.0T based on fast gradient echo.AssessmentNodule type (ground‐glass nodules, part‐solid nodules, and solid nodules), lung‐RADS classification, and nodule diameter (manual measurement) on CT and ZTE‐MRI images were recorded.Statistical TestsThe percent of concordant cases, Kappa value, intraclass correlation coefficient (ICC), Wilcoxon signed‐rank test, Spearman's correlation, and Bland–Altman. The p‐value <0.05 is considered significant.ResultsA total of 54 patients (age, 54.8 ± 11.9 years; 21 men) with 63 nodules were enrolled. Compared with CT, the total nodule detection rate of ZTE‐MRI was 85.7%. The intermodality agreement of ZTE‐MRI and CT lung nodules type evaluation was substantial (Kappa = 0.761), and the intermodality agreement of ZTE‐MRI and CT lung‐RADS classification was moderate (Kappa = 0.592). The diameter measurements between ZTE‐MRI and CT showed no significant difference and demonstrated a high degree of interobserver (ICC = 0.997–0.999) and intermodality (ICC = 0.956–0.985) agreements.Data ConclusionThe measurement of nodule diameter by pulmonary ZTE‐MRI is similar to that by CT, but the ability of lung‐RADS to classify nodes from MRI images still requires further research.Level of Evidence2Technical EfficacyStage 2

Background: It is important to assess the proliferation of endometrial carcinoma (EC) noninvasively using imaging methods. This prospective diagnostic study investigated the value of biexponential and stretched exponential models of intravoxel incoherent motion (IVIM) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting the Ki-67 status of EC. Methods: In all, 70 patients with EC underwent pelvic MRI. The diffusion coefficient (D), pseudo diffusion coefficient (D*), perfusion fraction (f), distributed diffusion coefficient (DDC), water molecular diffusion heterogeneity index (alpha), volume transfer constant (Ktrans), rate transfer constant (Kep), and volume of extravascular extracellular space per unit volume of tissue (Ve) were compared. The area under the receiver operating characteristic (ROC) curve (AUC) was used to quantify diagnostic efficacy. Multivariate logistic regression and bootstrap (1,000 samples) analyses were used to establish and evaluate, respectively, the optimal model to predict Ki-67 status. Results: D, Ktrans, and Kepwere lower while alpha was higher in the high-proliferation group as compared with low-proliferation group (all P values<0.05). D and Kep were independent predictors of Ki-67 status in EC, and the combination of these parameters had optimal diagnostic efficacy (AUC 0.920; sensitivity 85.71%; specificity 89.29%), which was significantly better than that of D (AUC 0.753; Z=2.874; P=0.004), alpha (AUC 0.715; Z=3.505; P=0.001), Ktrans (AUC 0.808; Z=2.741; P=0.006), and Kep (AUC 0.832; Z=2.147; P=0.032) alone. The validation model showed good accuracy (AUC 0.882; 95% confidence interval 0.861-0.897) and consistency (C-statistic =0.902). D, Kep, Ktrans, and alpha showed a slightly negative (r=-0.271), moderately negative (r=-0.534), slightly negative (r=-0.409), and slightly positive (r=0.488) correlation with the Ki-67 index, respectively (all P values <0.05). Conclusions: IVIM-and DCE-MRI-derived parameters, including D, alpha, Ktrans, and Kep, were associated with Ki-67 status in EC, and the combination of D and Kep may serve as a superior imaging marker for the identification of low-and high-proliferation EC.

Traumatic axonal injury (TAI) may result in the disruption of brain functional networks and is strongly associated with cognitive impairment. However, the neural mechanisms affecting the neurocognitive function after TAI remain to be elucidated. We collected the resting-state functional magnetic resonance imaging data from 28 patients with TAI and 28 matched healthy controls. An automated anatomical labeling atlas was used to construct a functional brain connectome. We utilized a graph theoretical approach to investigate the alterations in global and regional network topologies, and network-based statistics analysis was utilized to localize the connected networks more precisely. The current study revealed that patients with TAI and healthy controls both showed a typical small-world topology of the functional brain networks. However, patients with TAI exhibited a significantly lower local efficiency compared to healthy controls, whereas no significant difference emerged in other small-world properties (Cp, Lp, γ, λ, and σ) and global efficiency. Moreover, patients with TAI exhibited aberrant nodal centralities in some regions, including the frontal lobes, parietal lobes, caudate nucleus, and cerebellum bilaterally, and right olfactory cortex. The network-based statistics results showed alterations in the long-distance functional connections in the subnetwork in patients with TAI, involving these brain regions with significantly altered nodal centralities. These alterations suggest that brain networks of individuals with TAI present aberrant topological attributes that are associated with cognitive impairment, which could be potential biomarkers for predicting cognitive dysfunction and help understanding the neuropathological mechanisms in patients with TAI.

Abstract Background: To investigate the value of diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and amide proton transfer-weighted (APTW) imaging in differentiating the primary glioma from solitary brain metastases (SBM) with the histogram and texture features. Methods: A total of 65 patients with newly diagnosed glioma or metastases were enrolled. All patients underwent DWI, IVIM, and APTW imaging, as well as the T1W, T2W, T2FLAIR, and enhanced T1W imaging. The first-order texture and histogram features of apparent diffusion coefficient (ADC) from DWI, diffusion coefficient (D), perfusion fraction (f), pseudo-diffusion coefficient (D*) from IVIM, and MTRasym (3.5 ppm) from APTW were extracted from the tumor parenchyma and compared between glioma and SBM. Parameters with significant differences were analyzed with the logistics regression and receiver operator curves to explore the optimal model and compare the differentiation performance. Results: Higher ADCkurtosis (p=0.02), f90 (p＜0.01), and fkurtosis (p=0.01) were found for glioma, while higher MTRasym (3.5ppm)10 (p= 0.05), fentropy (p＜0.01), and fmean (p＜0.01) were observed for SBM. fkurtosis (OR =0.66, 95%CI 0.48-0.92, p=0.02), and fmean (OR=1.44, 95%CI 1.16-1.18, p<0.01) were independent factors for SBM differentiation. The model combining age, fkurtosis, and fmean showed an AUC of 0.83, while the model combined with fkurtosis and fmean with improved sensitivity had an AUC of 0.81. Conclusion: The fmean and fkurtosis in enhanced tumor region could be used to differentiate glioma and SBM.

Background The aim of the study was to investigate the value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and intravoxel incoherent motion (IVIM) in differentiating TP53-mutant from wild type, low-risk from non-low-risk early-stage endometrial carcinoma (EC). Patients and methods A total of 74 EC patients underwent pelvic MRI. Parameters volume transfer constant (K trans ), rate transfer constant (K ep ), the volume of extravascular extracellular space per unit volume of tissue (V e ), true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and microvascular volume fraction (f) were compared. The combination of parameters was investigated by logistic regression and evaluated by bootstrap (1000 samples), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results In the TP53-mutant group, K trans and K ep were higher and D was lower than in the TP53-wild group; K trans , V e , f, and D were lower in the non-low-risk group than in the low-risk group (all P < 0.05). In the identification of TP53-mutant and TP53-wild early-stage EC, K trans and D were independent predictors, and the combination of them had an optimal diagnostic efficacy (AUC, 0.867; sensitivity, 92.00%; specificity, 80.95%), which was significantly better than D (Z = 2.169, P = 0.030) and K trans (Z = 2.572, P = 0.010). In the identification of low-risk and non-low-risk early-stage EC, K trans , V e , and f were independent predictors, and the combination of them had an optimal diagnostic efficacy (AUC, 0.947; sensitivity, 83.33%; specificity, 93.18%), which was significantly better than D (Z = 3.113, P = 0.002), f (Z = 4.317, P < 0.001), K trans (Z = 2.713, P = 0.007), and V e (Z = 3.175, P = 0.002). The calibration curves showed that the above two combinations of independent predictors, both have good consistency, and DCA showed that these combinations were reliable clinical prediction tools. Conclusions Both DCE-MRI and IVIM facilitate the prediction of TP53 status and risk stratification in early-stage EC. Compare with each single parameter, the combination of independent predictors provided better predictive power and may serve as a superior imaging marker.

Background: Preterm infants with necrotizing enterocolitis (NEC) are at high risk of adverse neurodevelopmental outcomes. The aim of this study was to explore the value of diffusion tensor imaging (DTI) combined with serum C-reactive protein (CRP) and procalcitonin (PCT) in evaluating alterations of white matter (WM) microstructure in preterm infants with NEC.Methods: A retrospective cross-sectional study was conducted in which all participants were consecutively enrolled at The Third Affiliated Hospital of Zhengzhou University from June 2017 and October 2021. Data from 30 preterm infants with NEC [mean gestational age at birth 31.41 +/- 1.15 weeks; mean age at magnetic resonance imaging (MRI) 37.53 +/- 3.08 weeks] and 40 healthy preterm infants with no NEC were recorded (mean gestational age at birth 32.27 +/- 2.09 weeks; mean age at MRI 37.15 +/- 3.23 weeks). WM was used to obtain the fractional anisotropy (FA) and mean diffusivity (MD) values of the regions of interest (ROIs). Additionally, serum levels of CRP and PCT were determined. Spearman correlation analysis was performed between the WM-derived parameters, CRP level, and the PCT serum index.Results: Preterm infants with NEC had reduced FA values and elevated MD values in WM regions [posterior limbs of the internal capsule (PLIC), lentiform nucleus (LN), frontal white matter (FWM)] compared to the control group (P<0.05). Additionally, the FA of the PLIC was negatively correlated with serum CRP (r=-0.846; P<0.05) and PCT (r=-0.843; P<0.05). Meanwhile, the MD of PLIC was positively correlated with serum CRP (r=0.743; P<0.05) and PCT (r=0.743; P<0.05, respectively). The area under the curve (AUC) of FA and MD combined with CRP and PCT in the diagnosis of WM microstructure alterations with NEC was 0.968, representing a considerable improvement in predicted efficacy over single indicators, including FA [AUC: 0.938; 95% confidence interval (CI): 0.840-0.950], MD (AUC: 0.807; 95% CI: 0.722-0.838), CRP (AUC: 0.867; 95% CI: 0.822-0.889), and PCT (AUC: 0.706; 95% CI: 0.701-0.758).Conclusions: WM can noninvasively and quantitatively assess the WM microstructure alterations in preterm infants with NEC. WM combined with serum CRP and PCT demonstrated superior performance in detecting and evaluating WM microstructure alterations in preterm infants with NEC.

Rationale and Objectives: To develop the nomogram utilizing the American College of Radiology BI-RADS descriptors, clinical features, and apparent diffusion coefficient (ADC) to differentiate benign from malignant breast lesions. Materials and Methods: A total of 341 lesions (161 malignant and 180 benign) were included. Clinical data and imaging features were reviewed. Univariable and multivariable logistic regression analyses were performed to determine the independent variables. ADC as a continuous or classified into binary form with a cutoff value of 1.30 x 10(-3) mm(2)/s, incorporated other independent predictors to construct two nomograms, respectively. Receiver operating curve and calibration plot was employed to test the models' discriminative ability. The diagnostic performance between the developed model and the Kaiser score (KS) was also compared. Results: In both models, high patient age, the presence of root sign, time-intensity curves (TICs) types (plateau and washout), heterogenous internal enhancement, the presence of peritumoral edema, and ADC were independently associated with malignancy. The AUCs of two multivariable models (AUC, 0.957; 95% CI: 0.929-0.976 and AUC, 0.958; 95% CI: 0.931-0.976) were significantly higher than that of the KS (AUC, 0.919, 95% CI: 0.885-0.946; both P < 0.001). At the same sensitivity of 95.7%, our models showed an increase in specificity by 5.56% (P = 0.076) and 6.11% (P = 0.035), respectively, as compared to the KS. Conclusion: The models incorporating MRI features (root sign, TIC, margins, internal enhancement, and presence of edema), quantitative ADC value, and patient age showed improved diagnostic performance and might have avoided more unnecessary biopsies in comparison with the KS, although further external validation is required.

ObjectivesTo investigate whether combining radiomics extracted from ultrafast dynamic contrast-enhanced MRI (DCE-MRI) with an artificial neural network enables differentiation of MR BI-RADS 4 breast lesions and thereby avoids false-positive biopsies.MethodsThis retrospective study consecutively included patients with MR BI-RADS 4 lesions. The ultrafast imaging was performed using Differential sub-sampling with cartesian ordering (DISCO) technique and the tenth and fifteenth postcontrast DISCO images (DISCO-10 and DISCO-15) were selected for further analysis. An experienced radiologist used freely available software (FAE) to perform radiomics extraction. After principal component analysis (PCA), a multilayer perceptron artificial neural network (ANN) to distinguish between malignant and benign lesions was developed and tested using a random allocation approach. ROC analysis was performed to evaluate the diagnostic performance.Results173 patients (mean age 43.1 years, range 18–69 years) with 182 lesions (95 benign, 87 malignant) were included. Three types of independent principal components were obtained from the radiomics based on DISCO-10, DISCO-15, and their combination, respectively. In the testing dataset, ANN models showed excellent diagnostic performance with AUC values of 0.915-0.956. Applying the high-sensitivity cutoffs identified in the training dataset demonstrated the potential to reduce the number of unnecessary biopsies by 63.33%-83.33% at the price of one false-negative diagnosis within the testing dataset.ConclusionsThe ultrafast DCE-MRI radiomics-based machine learning model could classify MR BI-RADS category 4 lesions into benign or malignant, highlighting its potential for future application as a new tool for clinical diagnosis.

The pre-operative non-invasive differential diagnosis of hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) mainly depends on imaging. However, the accuracy of conventional imaging and radiomics methods in differentiating between the two carcinomas is unsatisfactory. In this study, we aimed to establish a novel deep learning model based on computed tomography (CT) images to provide an effective and non-invasive pre-operative differential diagnosis method for HCC and ICC. We retrospectively investigated the CT images of 395 HCC patients and 99 ICC patients who were diagnosed based on pathological analysis. To differentiate between HCC and ICC we developed a deep learning model called CSAM-Net based on channel and spatial attention mechanisms. We compared the proposed CSAM-Net with conventional radiomic models such as conventional logistic regression, least absolute shrinkage and selection operator regression, support vector machine, and random forest models. With respect to differentiating between HCC and ICC, the CSAM-Net model showed area under the receiver operating characteristic curve (AUC) values of 0.987 (accuracy = 0.939), 0.969 (accuracy = 0.914), and 0.959 (accuracy = 0.912) for the training, validation, and test sets, respectively, which were significantly higher than those of the conventional radiomics models (0.736–0.913 [accuracy = 0.735–0.912], 0.602–0.828 [accuracy = 0.647–0.818], and 0.638–0.845 [accuracy = 0.618–0.849], respectively. The decision curve analysis showed a high net benefit of the CSAM-Net model, which suggests potential efficacy in differentiating between HCC and ICC in the diagnosis of liver cancers. The proposed CSAM-Net model based on channel and spatial attention mechanisms provides an effective and non-invasive tool for the differential diagnosis of HCC and ICC on CT images, and has potential applications in diagnosis of liver cancers.

MiR-1283 has been identified as a tumor suppressor in some malignancies. Whereas, the role of miR-1283 in HER2-positive (HER2+) breast cancer, particularly its role in regulating cell proliferation, one of the most significant features of tumor progression, is unclear. The related microRNA screened by the breast cancer sample GSE131599 dataset were detected in HER2+ breast cancer tissues and cell lines. Then, the obtained miR-1283 was overexpressed in SKBR3 and BT-474 cells followed by relevant functional assays concerning cell proliferation and apoptosis. The xenograft mouse model was induced and the effect of miR-1283 on tumor growth and cell proliferation was examined. The target of miR-1283 and the transcription factor regulating miR-1283 were predicted and identified. Finally, the influence of transcription factor KLF14 on cell proliferation and apoptosis was investigated. An integrated analysis confirmed that miR-1283 expression was significantly decreased in HER2+ breast cancer tissues. Also, by q-RT-PCR detection, miR-1283 expression was markedly reduced in HER2+ breast cancer tissues and cell lines. The miR-1283 overexpression prevented the proliferation and enhanced apoptosis of HER2+ breast cancer cells, as well as inhibited tumor growth. Mechanistically, miR-1283 inhibited TFAP2C expression by targeting the 3'-untranslated regions of TFAP2C messenger RNA, and the KLF14 enhanced miR-1283 level via binding to its promoter. The result subsequently confirmed the KLF14/miR-1283 signaling suppressed cell proliferation in HER2+ breast cancer. Our results suggested that the KLF14/miR-1283/TFAP2C axis inhibited HER2+ breast cancer progression, which might provide novel insight into mechanical exploration for this disease.

Background Acute pericoronitis (AP) is a common cause of odontogenic toothache. Pain significantly affects the structure and function of the brain, but alterations in spontaneous brain activity in patients with AP are unclear. Purpose To apply the amplitude of low-frequency fluctuations (ALFF) method in resting-state functional magnetic resonance imaging to investigate altered spontaneous brain activity characteristics in patients with AP in different frequency bands (typical, slow-4, and slow-5 bands) and assess their correlation with clinical scores. Patients and Methods Thirty-four right-handed patients with AP and 31 healthy controls (HC), matched for age, sex, education, and right-handedness, were enrolled. All subjects underwent resting-state functional magnetic resonance imaging. DPABI software was used for data preprocessing and extracting the ALFF values in different frequency bands. Subsequently, differences in ALFF values in the three bands were compared between the two groups. Correlation between ALFF values in the differential brain regions and clinical scores was assessed. Results In the typical band, ALFF values were higher in the left insula, left superior occipital gyrus, left inferior parietal lobule, left posterior cerebellar lobule, and right postcentral gyrus in the AP than in the HC group. In the slow-4 band, ALFF values in the left superior occipital gyrus, right superior occipital gyrus, and right middle occipital gyrus were higher, and those in the right cingulate gyrus and right superior temporal gyrus were lower in the AP than in the HC group. In the slow-5 band, the ALFF values in the left insula and left superior occipital gyrus were higher in the AP than in the HC group. The ALFF values of the typical bands in the left insula, left inferior parietal lobule, and right postcentral gyrus correlated negatively, those of the slow-4 band in the right middle occipital gyrus correlated positively, and those of the slow-5 band in the left insula correlated negatively with the visual analogue scale score in the AP group. Conclusion Our results suggested that the intrinsic brain activity of AP patients was abnormal and frequency-dependent. This provides new insights to explore the neurophysiological mechanisms of AP.

OBJECTIVE:This study aimed to perform an assessment of brain microstructure in children with autism aged 2 to 5 years using relaxation times acquired by synthetic magnetic resonance imaging.MATERIALS AND METHODS:Thirty-four children with autism spectrum disorder (ASD) (ASD group) and 17 children with global developmental delay (GDD) (GDD group) were enrolled, and synthetic magnetic resonance imaging was performed to obtain T1 and T2 relaxation times. The differences in brain relaxation times between the 2 groups of children were compared, and the correlation between significantly changed T1/T2 and clinical neuropsychological scores in the ASD group was analyzed.RESULTS:Compared with the GDD group, shortened T1 relaxation times in the ASD group were distributed in the genu of corpus callosum (GCC) ( P = 0.003), splenium of corpus callosum ( P = 0.002), and right thalamus (TH) ( P = 0.014), whereas shortened T2 relaxation times in the ASD group were distributed in GCC ( P = 0.011), left parietal white matter ( P = 0.035), and bilateral TH (right, P = 0.014; left, P = 0.016). In the ASD group, the T2 of the left parietal white matter is positively correlated with gross motor (developmental quotient [DQ] 2) and personal-social behavior (DQ5), respectively ( r = 0.377, P = 0.028; r = 0.392, P = 0.022); the T2 of the GCC was positively correlated with DQ5 ( r = 0.404, P = 0.018); and the T2 of the left TH is positively correlated with DQ2 and DQ5, respectively ( r = 0.433, P = 0.009; r = 0.377, P = 0.028). All significantly changed relaxation values were not significantly correlated with Childhood Autism Rating Scale scores.CONCLUSIONS:The shortened relaxometry times in the brain of children with ASD may be associated with the increased myelin content and decreased water content in the brain of children with ASD in comparison with GDD, contributing the understanding of the pathophysiology of ASD. Therefore, the T1 and T2 relaxometry may be used as promising imaging markers for ASD diagnosis.