Predicting glioblastoma recurrence using multiparametric MR imaging of non-enhancing peritumoral regions at baseline.

Background: To assess the feasibility of multiparametric magnetic resonance imaging in predicting tumor recurrence in nonenhancing peritumoral regions in patients with glioblastoma at baseline. Methods: Fifty-eight patients with recurrent glioblastoma underwent multiparametric magnetic resonance imaging, including T2-weighted fluid-attenuated inversion recovery, diffusion-weighted imaging, and dynamic susceptibility contrast perfusion-weighted imaging. Nonenhancing peritumoral regions with glioblastoma recurrence were identified by coregistering preoperative and post-recurrent magnetic resonance images. Regions of interest were placed in nonenhancing peritumoral regions with and without tumor recurrence to calculate the apparent diffusion coefficient value, and relative ratios of T2-weighted fluid-attenuated inversion recovery signal intensity, apparent diffusion coefficient, and cerebral blood volume values. Results: Significant lower relative T2-weighted fluid-attenuated inversion recovery signal intensity, apparent diffusion coefficient, and relative apparent diffusion coefficient but higher relative cerebral blood volume values were found in the nonenhancing peritumoral regions with tumor recurrence than without recurrence (all P < 0.05). The threshold values ≥ 0.89 for relative cerebral blood volume provide the optimal performance for predicting the nonenhancing peritumoral regions with future tumor recurrence, with the sensitivity, specificity, and accuracy of 84.7%, 83.6%, and 85.8%, respectively. The combination of relative T2-weighted fluid-attenuated inversion recovery signal intensity, apparent diffusion coefficient, and relative cerebral blood volume can provide better predictive performance than relative cerebral blood volume (P = 0.015). Conclusion: The combined use of T2-weighted fluid-attenuated inversion recovery, diffusion-weighted imaging, and dynamic susceptibility contrast perfusion-weighted imaging can effectively estimate the risk of future tumor recurrence at baseline.

Is 3T MR nerve-bone fusion imaging a viable alternative to MRI-CBCT to identify the relationship between the inferior alveolar nerve and mandibular third molar.

OBJECTIVES: To investigate the feasibility of MRI nerve-bone fusion imaging in assessing the relationship between inferior alveolar nerve (IAN) / mandibular canal (MC) and mandibular third molar (MTM) compared with MRI-CBCT fusion. MATERIALS AND METHODS: The MRI nerve-bone fusion and MRI-CBCT fusion imaging were performed in 20 subjects with 37 MTMs. The Hausdorff distance (HD) value and dice similarity coefficient (DSC) was calculated. The relationship between IAN/MC and MTM roots, inflammatory, and fusion patterns were compared between these two fused images. The reliability was assessed using a weighted κ statistic. RESULTS: The mean HD and DSC ranged from 0.62 ~ 1.35 and 0.83 ~ 0.88 for MRI nerve-bone fusion, 0.98 ~ 1.50 and 0.76 ~ 0.83 for MRI-CBCT fusion. MR nerve-bone fusion had considerable reproducibility compared to MRI-CBCT fusion in relation classification (MR nerve-bone fusion κ = 0.694, MRI-CBCT fusion κ = 0.644), direct contact (MR nerve-bone fusion κ = 0.729, MRI-CBCT fusion κ = 0.720), and moderate to good agreement for inflammation detection (MR nerve-bone fusion κ = 0.603, MRI-CBCT fusion κ = 0.532, average). The MR nerve-bone fusion imaging showed a lower ratio of larger pattern compared to MR-CBCT fusion (16.2% VS 27.3% in the molar region, and 2.7% VS 5.4% in the retromolar region). And the average time spent on MR nerve-bone fusion and MRI-CBCT fusion was 1 min and 3 min, respectively. CONCLUSIONS: Both MR nerve-bone fusion and MRI-CBCT fusion exhibited good consistency in evaluating the spatial relationship between IAN/MC and MTM, fusion effect, and inflammation detection. CLINICAL RELEVANCE: MR nerve-bone fusion imaging can be a preoperative one-stop radiation-free examination for patients at high risk for MTM surgery.

FlexDTI: flexible diffusion gradient encoding scheme-based highly efficient diffusion tensor imaging using deep learning.

OBJECTIVE: Most deep neural network-based diffusion tensor imaging methods require the diffusion gradients' number and directions in the data to be reconstructed to match those in the training data. This work aims to develop and evaluate a novel dynamic-convolution-based method called FlexDTI for highly efficient diffusion tensor reconstruction with flexible diffusion encoding gradient scheme. Approach: FlexDTI was developed to achieve high-quality DTI parametric mapping with flexible number and directions of diffusion encoding gradients. The method used dynamic convolution kernels to embed diffusion gradient direction information into feature maps of the corresponding diffusion signal. Furthermore, it realized the generalization of a flexible number of diffusion gradient directions by setting the maximum number of input channels of the network. The network was trained and tested using datasets from the Human Connectome Project and local hospitals. Results from FlexDTI and other advanced tensor parameter estimation methods were compared. Main results: Compared to other methods, FlexDTI successfully achieves high-quality diffusion tensor-derived parameters even if the number and directions of diffusion encoding gradients change. It reduces normalized root mean squared error (NRMSE) by about 50% on fractional anisotropy (FA) and 15% on mean diffusivity (MD), compared with the state-of-the-art deep learning method with flexible diffusion encoding gradient scheme. Significance: FlexDTI can well learn diffusion gradient direction information to achieve generalized DTI reconstruction with flexible diffusion gradient scheme. Both flexibility and reconstruction quality can be taken into account in this network. .

Clinical-Radiologic Morphology-Radiomics Model on Gadobenate Dimeglumine-Enhanced MRI for Identification of Highly Aggressive Hepatocellular Carcinoma: Temporal Validation and Multiscanner Validation.

BACKGROUND: Highly aggressive hepatocellular carcinoma (HCC) is characterized by high tumor recurrence and poor outcomes, but its definition and imaging characteristics have not been clearly described. PURPOSE: To develop and validate a fusion model on gadobenate dimeglumine-enhanced MRI for identifying highly aggressive HCC. STUDY TYPE: Retrospective. POPULATION: 341 patients (M/F = 294/47) with surgically resected HCC, divided into a training cohort (n = 177), temporal validation cohort (n = 77), and multiscanner validation cohort (n = 87). FIELD STRENGTH/SEQUENCE: 3T, dynamic contrast-enhanced MRI with T1-weighted volumetric interpolated breath-hold examination gradient-echo sequences, especially arterial phase (AP) and hepatobiliary phase (HBP, 80-100 min). ASSESSMENT: Clinical factors and diagnosis assessment based on radiologic morphology characteristics associated with highly aggressive HCCs were evaluated. The radiomics signatures were extracted from AP and HBP. Multivariable logistic regression was performed to construct clinical-radiologic morphology (CR) model and clinical-radiologic morphology-radiomics (CRR) model. A nomogram based on the optimal model was established. Early recurrence-free survival (RFS) was evaluated in actual groups and risk groups calculated by the nomogram. STATISTICAL TESTS: The performance was evaluated by receiver operating characteristic curve (ROC) analysis, calibration curves analysis, and decision curves. Early RFS was evaluated by using Kaplan-Meier analysis. A P value <0.05 was considered statistically significant. RESULTS: The CRR model incorporating corona enhancement, cloud-like hyperintensity on HBP, and radiomics signatures showed the highest diagnostic performance. The area under the curves (AUCs) of CRR were significantly higher than those of the CR model (AUC = 0.883 vs. 0.815, respectively, for the training cohort), 0.874 vs. 0.769 for temporal validation, and 0.892 vs. 0.792 for multiscanner validation. In both actual and risk groups, highly and low aggressive HCCs showed statistically significant differences in early recurrence. DATA CONCLUSION: The clinical-radiologic morphology-radiomics model on gadobenate dimeglumine-enhanced MRI has potential to identify highly aggressive HCCs and non-invasively obtain prognostic information. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 2.

Clinical and multiparametric MRI features for differentiating uterine carcinosarcoma from endometrioid adenocarcinoma.

INTRODUCTION: The purpose of our study was to differentiate uterine carcinosarcoma (UCS) from endometrioid adenocarcinoma (EAC) by the multiparametric magnetic resonance imaging (MRI) features. METHODS: We retrospectively evaluated clinical and MRI findings in 17 patients with UCS and 34 patients with EAC proven by histologically. The following clinical and pathological features were evaluated: post- or pre-menopausal, clinical presentation, invasion depth, FIGO stage, lymphaticmetastasis. The following MRI features were evaluated: tumor dimension, cystic degeneration or necrosis, hemorrhage, signal intensity (SI) on T2-weighted images (T2WI), relative SI of lesion to myometrium on T2WI, T1WI, DWI, ADCmax, ADCmin, ADCmean (RSI-T2, RSI-T1, RSI-DWI, RSI-ADCmax, RSI-ADCmin, RSI-ADCmean), ADCmax, ADCmin, ADCmean, the maximum, minimum and mean relative enhancement (RE) of lesion to myometrium on the arterial and venous phases (REAmax, REAmin, REAmean, REVmax, REVmin, REVmean). Receiver operating characteristic (ROC) analysis and the area under the curve (AUC) were used to evaluate prediction ability. RESULTS: The mean age of UCS was higher than EAC. UCS occurred more often in the postmenopausal patients. UCS and EAC did not significantly differ in depth of myometrial invasion, FIGO stage and lymphatic metastasis. The anterior-posterior and transverse dimensions were significantly larger in UCS than EAC. Cystic degeneration or necrosis and hemorrhage were more likely occurred in UCS. The SI of tumor on T2WI was more heterogeneous in UCS. The RSI-T2, ADCmax, ADCmean, RSI-ADCmax and RSI-ADCmean of UCS were significantly higher than EAC. The REAmax, REAmin, REAmean, REVmax, REVmin and REVmean of UCS were all higher than EAC. The AUCs were 0.72, 0.71, 0.86, 0.96, 0.89, 0.84, 0.73, 0.97, 0.88, 0.94, 0.91, 0.69 and 0.80 for the anterior-posterior dimension, transverse dimension, RSI-T2, ADCmax, ADCmean, RSI-ADCmax, RSI-ADCmean, REAmax, REAmin, REAmean, REVmax, REVmin and REVmean, respectively. The AUC was 0.997 of the combined of ADCmax, REAmax and REVmax. Our study showed that ADCmax threshold value of 789.05 (10-3mm2/s) can differentiate UCS from EAC with 100% sensitivity, 76.5% specificity, and 0.76 AUC, REAmax threshold value of 0.45 can differentiate UCS from EAC with 88.2% sensitivity, 100% specificity, and 0.88 AUC. CONCLUSION: Multiparametric MRI features may be utilized as a biomarker to distinguish UCS from EAC.

Multiparametric MRI radiomics improves preoperative diagnostic performance for local staging in patients with endometrial cancer.

PURPOSE: To determine whether multiparametric magnetic resonance imaging (MRI) radiomics-based machine learning methods can improve preoperative local staging in patients with endometrial cancer (EC). METHODS: Data of patients with histologically confirmed EC who underwent preoperative MRI were retrospectively analyzed and divided into a training or test set. Radiomic features extracted from multiparametric MR images were used to train and test the prediction of deep myometrial invasion (DMI) and cervical stromal invasion (CSI). Two radiologists assessed the presence of DMI and CSI on conventional MR images. A combined model incorporating a radiomic signature and conventional MR images was constructed and presented as a nomogram. Performance of the predictive models was assessed using the area under curve (AUC) in the receiver operating curve analysis and pairwise comparison using DeLong's test with Bonferroni correction. RESULTS: This study included 198 women (training set = 138, test set = 60). Conventional MRI achieved AUCs of 0.837 and 0.799 for detecting DMI and 0.825 and 0.858 for detecting CSI in the training and test sets, respectively. The nomogram achieved AUCs of 0.928 and 0.869 for detecting DMI and 0.913 and 0.937 for detecting CSI in the training and test sets, respectively. The ability of the nomogram to detect DMI and CSI in the two sets was superior to that of conventional MRI (adjusted p < 0.05), except for the ability to detect CSI in the test set (adjusted p > 0.05). CONCLUSION: A nomogram incorporating radiomics signature into conventional MRI improved the efficacy of preoperative local staging of EC.

MRI characteristics predict BRAF V600E status in gangliogliomas and pleomorphic xanthoastrocytomas and provide survival prognostication.

BACKGROUND: BRAF V600E mutation is a common genomic alteration in gangliogliomas (GGs) and pleomorphic xanthoastrocytomas (PXAs) with prognostic and therapeutic implications. PURPOSE: To investigate the ability of magnetic resonance imaging (MRI) features to predict BRAF V600E status in GGs and PXAs and their prognostic values. MATERIAL AND METHODS: A cohort of 44 patients with histologically confirmed GGs and PXAs was reviewed retrospectively. BRAF V600E status was determined by immunohistochemistry (IHC) staining and fluorescence quantitative polymerase chain reaction (PCR). Demographics and MRI characteristics of the two groups were evaluated and compared. Univariate and multivariate Cox regression analyses were performed to identify MRI features that were prognostic for progression-free survival (PFS). RESULTS: T1/FLAIR ratio, enhancing margin, and mean relative apparent diffusion coefficient (rADCmea) value showed significant differences between the BRAF V600E-mutant and BRAF V600E-wild groups (all P < 0.05). Binary logistic regression analysis revealed only rADCmea value was the independent predictive factor for BRAF V600E status (P = 0.027). Univariate Cox regression analysis showed age at diagnosis (P = 0.032), WHO grade (P = 0.020), enhancing margin (P = 0.029), and rADCmea value (P = 0.005) were significant prognostic factors for PFS. In multivariate Cox regression analysis, increasing age (P = 0.040, hazard ratio [HR] = 1.04, 95% confidence interval [CI] = 1.002-1.079) and lower rADCmea values (P = 0.021, HR = 0.036, 95% CI = 0.002-0.602) were associated with poor PFS in GGs and PXAs. CONCLUSION: Imaging features are potentially predictive of BRAF V600E status in GGs and PXAs. Furthermore, rADCmea value is a valuable prognostic factor for patients with GGs or PXAs.

Initial and follow-up high-resolution vessel wall MRI study of spontaneous cervicocranial artery dissection.

OBJECTIVES: To explore the factors associated with ischemic stroke secondary to spontaneous cervicocranial artery dissection (sCCAD) and evaluate the initial imaging markers related to outcomes. METHODS: Initial and follow-up high-resolution vessel wall MRI (VW-MRI) in consecutive patients with sCCAD were retrospectively analyzed. The associations of clinical and imaging factors and variants of the circle of Willis (COW) with ischemic stroke were evaluated using binary logistic regression analyses. The anatomical outcomes were categorized as complete, partial, and no remodeling based on changes of the vessel wall and lumen. Ordinal logistic regression analysis was used to assess associations between initial features and outcomes. RESULTS: A total of 115 dissected arteries (79 strokes, 36 non-strokes) were detected in 103 patients. Follow-up VW-MRI was available in 46 patients (44.7%, with 51 vessels), with a median interval of 8.5 months. Pseudoaneurysm (odd ratio [OR], 0.178; 95% confidence interval [CI], 0.039-0.810; p = 0.026) tended to rarely cause ischemic stroke, while intraluminal thrombus (OR, 5.558; 95% CI, 1.739-17.765; p = 0.004), incomplete COW (OR, 9.309; 95% CI, 2.122-40.840; p = 0.003), and partial complete COW (OR, 4.463; 95% CI, 1.211-16.453; p = 0.025) were independently associated with stroke occurrence. Furthermore, the presence of double lumen (OR, 5.749; 95% CI, 1.358-24.361; p = 0.018) and occlusion (OR, 12.975; 95% CI, 3.022-55.645; p = 0.001) were associated with no remodeling of sCCAD. CONCLUSIONS: Multiple initial factors were found to be related to stroke occurrence and anatomical outcomes of sCCAD. High-resolution VW-MRI may provide valuable insights into the pathophysiology and evolution of sCCAD. CLINICAL RELEVANCE STATEMENT: Initial and follow-up high-resolution vessel wall MRI may help elucidate the pathophysiology of spontaneous cervicocranial artery dissection and provide important insights into the evolution and further facilitate the optimal management of patients with spontaneous cervicocranial artery dissection. KEY POINTS: • Clinical and imaging factors, as well as the status of primary collateral circulation, are associated with ischemic stroke secondary to spontaneous cervicocranial artery dissection. • The follow-up high-resolution vessel wall MRI provides valuable insights into the long-term evolution and anatomical outcomes of spontaneous cervicocranial artery dissection. • The high-resolution vessel wall MRI features related to ischemic stroke and anatomical outcomes may further facilitate the optimal management of patients with spontaneous cervicocranial artery dissection.

Characterization of parotid gland tumors: Whole-tumor histogram analysis of diffusion weighted imaging, diffusion kurtosis imaging, and intravoxel incoherent motion - A pilot study.

PURPOSE: To investigate the diagnostic performance of histogram features of diffusion parameters in characterizating parotid gland tumors. METHOD: From December 2018 to January 2023, patients who underwent diffusion weighted imaging (DWI), diffusion kurtosis imaging (DKI), and intravoxel incoherent motion (IVIM) were consecutively enrolled in this retrospective study. The histogram features of diffusion parameters, including apparent diffusion coefficient (ADC), diffusion coefficient (Dk), diffusion kurtosis (K), pure diffusion coefficient (D), pseudo-diffusion coefficient (DP), and perfusion fraction (FP) were analyzed. The Mann-Whitney U test was used for comparison between benign parotid gland tumors (BPGTs) and malignant parotid gland tumors (MPGTs). Receiver operating characteristic curve and logistic regression analysis were used to identify the differential diagnostic performance. The Spearman's correlation coefficient was used to analyze the correlation between diffusion parameters and Ki-67 labeling index. RESULTS: For diffusion MRI, twenty-three histogram features of diffusion parameters showed significant differences between BPGTs and MPGTs (all P < 0.05). Compared with the DWI model, the IVIM model and combined model had better diagnostic specificity (58 %, 94 %, and 88 %, respectively; both corrected P < 0.001) and accuracy (64 %, 89 %, and 86 %, respectively; both corrected P = 0.006). The combined model was superior to the single DWI model with improved IDI (IDI improvement 0.25). Significant correlations were found between Ki-67 and ADCmean, Dkmean, Kmean, and Dmean (r = -0.57 to 0.53; all P < 0.05). CONCLUSIONS: Whole-tumor histogram analysis of IVIM and combined diffusion model could further improve the diagnostic performance for differentiating BPGTs from MPGTs.

Early acquired resistance to EGFR-TKIs in lung adenocarcinomas before radiographic advanced identified by CT radiomic delta model based on two central studies.

Early acquired resistance (EAR) to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) in lung adenocarcinomas before radiographic advance cannot be perceived by the naked eye. This study aimed to discover and validate a CT radiomic model to precisely identify the EAR. Training cohort (n = 67) and internal test cohort (n = 29) were from the First Affiliated Hospital of Fujian Medical University, and external test cohort (n = 29) was from the Second Affiliated Hospital of Xiamen Medical College. Follow-up CT images at three different times of each patient were collected: (1) baseline images before EGFR-TKIs therapy; (2) first follow-up images after EGFR-TKIs therapy (FFT); (3) EAR images, which were the last follow-up images before radiographic advance. The features extracted from FFT and EAR were used to construct the classic radiomic model. The delta features which were calculated by subtracting the baseline from either FFT or EAR were used to construct the delta radiomic model. The classic radiomic model achieved AUC 0.682 and 0.641 in training and internal test cohorts, respectively. The delta radiomic model achieved AUC 0.730 and 0.704 in training and internal test cohorts, respectively. Over the external test cohort, the delta radiomic model achieved AUC 0.661. The decision curve analysis showed that when threshold of the probability of the EAR to the EGFR-TKIs was between 0.3 and 0.82, the proposed model was more benefit than treating all patients. Based on two central studies, the delta radiomic model derived from the follow-up non-enhanced CT images can help clinicians to identify the EAR to EGFR-TKIs in lung adenocarcinomas before radiographic advance and optimize clinical outcomes.

Application value of CT radiomic nomogram in predicting T790M mutation of lung adenocarcinoma.

BACKGROUND: The purpose of this study was to develop a radiomic nomogram to predict T790M mutation of lung adenocarcinoma base on non-enhanced CT lung images. METHODS: This retrospective study reviewed demographic data and lung CT images of 215 lung adenocarcinoma patients with T790M gene test results. 215 patients (including 52 positive) were divided into a training set (n = 150, 36 positive) and an independent test set (n = 65, 16 positive). Multivariate logistic regression was used to select demographic data and CT semantic features to build clinical model. We extracted quantitative features from the volume of interest (VOI) of the lesion, and developed the radiomic model with different feature selection algorithms and classifiers. The models were trained by a 5-fold cross validation strategy on the training set and assessed on the test set. ROC was used to estimate the performance of the clinical model, radiomic model, and merged nomogram. RESULTS: Three demographic features (gender, smoking, emphysema) and ten radiomic features (Kruskal-Wallis as selection algorithm, LASSO Logistic Regression as classifier) were determined to build the models. The AUC of the clinical model, radiomic model, and nomogram in the test set were 0.742(95%CI, 0.619-0.843), 0.810(95%CI, 0.696-0.907), 0.841(95%CI, 0.743-0.938), respectively. The predictive efficacy of the nomogram was better than the clinical model (p = 0.042). The nomogram predicted T790M mutation with cutoff value was 0.69 and the score was above 130. CONCLUSION: The nomogram developed in this study is a non-invasive, convenient, and economical method for predicting T790M mutation of lung adenocarcinoma, which has a good prospect for clinical application.

Improved performance of non-preloaded and high flip-angle dynamic susceptibility contrast perfusion-weighted imaging sequences in the presurgical differentiation of brain lymphoma and glioblastoma.

OBJECTIVE: This study aimed to compare the accuracy of relative cerebral blood volume (rCBV) and percentage signal recovery (PSR) obtained from high flip-angle dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) sequences with and without contrast agent (CA) preload for presurgical discrimination of brain glioblastoma and lymphoma. METHODS: Consecutive 336 patients (glioblastoma, 236; PCNSL, 100) were included. All the patients underwent DSC-PWI on 3.0-T magnetic resonance units before surgery. The rCBV and PSR with preloaded and non-preloaded CA were measured. The means of the continuous variables were compared using Welch's t-test. The diagnostic accuracies of the individual parameters were compared using the receiver operating characteristic curve analysis. RESULTS: The rCBV was higher with preloaded CA than with non-preloaded CA (glioblastoma, 10.20 vs. 8.90, p = 0.020; PCNSL, 3.88 vs. 3.27, p = 0.020). The PSR was lower with preloaded CA than with non-preloaded CA (glioblastoma, 0.59 vs. 0.90; PCNSL, 0.70 vs. 1.63; all p < 0.001). Regarding the differentiation of glioblastoma and PCNSL, the AUC of rCBV with preloaded CA was indistinguishable from that of non-preloaded CA (0.940 vs. 0.949, p = 0.703), whereas the area under the curve of PSR with preloaded CA was lower than non-preloaded CA (0.529 vs. 0.884, p < 0.001). CONCLUSION: With preloaded CA, diagnostic performance in differentiating glioblastoma and PCNSL did not improve for rCBV and it was decreased for PSR. Therefore, high flip-angle non-preload DSC-PWI sequences offer excellent accuracy and may be of choice sequence for presurgical discrimination of brain lymphoma and glioblastoma. CLINICAL RELEVANCE STATEMENT: High flip-angle DSC-PWI using non-preloaded CA may be an excellent diagnostic method for distinguishing glioblastoma from PCNSL. KEY POINTS: • Differentiating primary central nervous system lymphoma and glioblastoma accurately is critical for their management. • DSC-PWI sequences optimised for the most accurate CBV calculations may not be the optimal sequences for presurgical brain tumour diagnosis as they could be masquerading leakage phenomena that may provide interesting information in terms of differential diagnosis. • High flip-angle non-preloaded DSC-PWI sequences render the best accuracy in the presurgical differentiation of brain lymphoma and glioblastoma.

Altered dynamic functional network connectivity states in patients with acute basal ganglia ischemic stroke.

BACKGROUND: Dynamic functional network connectivity (dFNC) patterns are successfully able to capture the time-varying features of intrinsic fluctuations throughout a scan. We explored dFNC alterations across the entire brain in patients with acute ischemic stroke (AIS) of the basal ganglia (BG). METHOD: Resting-state functional magnetic resonance imaging data were acquired from 26 patients with first-ever AIS in the BG and 26 healthy controls (HCs). Independent component analysis, the sliding window method, and the K-means clustering method were used to obtain reoccurring dynamic network connectivity patterns. Moreover, temporal features across diverse dFNC states were compared between the two groups, and the local and global efficiencies across states were analyzed to explore the characteristics of the topological networks among states. RESULTS: Four dFNC states were characterized for comparison of dynamic brain network connectivity patterns. In contrast to the HC group, the AIS group spent a significantly higher fraction of time in State 1, which is characterized by a relatively weaker brain network connectome. Conversely, compared with HC, patients with AIS showed a lower mean dwell time in State 2, which was characterized by a relatively stronger brain network connectome. Additionally, functional networks exhibited variable efficiency of information transfer across 4 states. CONCLUSIONS: AIS not only altered the interaction between the different dynamic networks but also promoted characteristic alterations in the temporal and topological features of large-scale dynamic network connectivity.

Prediction of Ki-67 labeling index, ATRX mutation, and MGMT promoter methylation status in IDH-mutant astrocytoma by morphological MRI, SWI, DWI, and DSC-PWI.

OBJECTIVE: Noninvasive detection of molecular status of astrocytoma is of great clinical significance for predicting therapeutic response and prognosis. We aimed to evaluate whether morphological MRI (mMRI), SWI, DWI, and DSC-PWI could predict Ki-67 labeling index (LI), ATRX mutation, and MGMT promoter methylation status in IDH mutant (IDH-mut) astrocytoma. METHODS: We retrospectively analyzed mMRI, SWI, DWI, and DSC-PWI in 136 patients with IDH-mut astrocytoma.The features of mMRI and intratumoral susceptibility signals (ITSS) were compared using Fisher exact test or chi-square tests. Wilcoxon rank sum test was used to compare the minimum ADC (ADCmin), and minimum relative ADC (rADCmin) of IDH-mut astrocytoma in different molecular markers status. Mann-Whitney U test was used to compare the rCBVmax of IDH-mut astrocytoma with different molecular markers status. Receiver operating characteristic curves was performed to evaluate their diagnostic performances. RESULTS: ITSS, ADCmin, rADCmin, and rCBVmax were significantly different between high and low Ki-67 LI groups. ITSS, ADCmin, and rADCmin were significantly different between ATRX mutant and wild-type groups. Necrosis, edema, enhancement, and margin pattern were significantly different between low and high Ki-67 LI groups. Peritumoral edema was significantly different between ATRX mutant and wild-type groups. Grade 3 IDH-mut astrocytoma with unmethylated MGMT promoter was more likely to show enhancement compared to the methylated group. CONCLUSIONS: mMRI, SWI, DWI, and DSC-PWI were shown to have the potential to predict Ki-67 LI and ATRX mutation status in IDH-mut astrocytoma. A combination of mMRI and SWI may improve diagnostic performance for predicting Ki-67 LI and ATRX mutation status. CLINICAL RELEVANCE STATEMENT: Conventional MRI and functional MRI (SWI, DWI, and DSC-PWI) can predict Ki-67 expression and ATRX mutation status of IDH mutant astrocytoma, which may help clinicians determine personalized treatment plans and predict patient outcomes. KEY POINTS: • A combination of multimodal MRI may improve the diagnostic performance to predict Ki-67 LI and ATRX mutation status. • Compared with IDH-mutant astrocytoma with low Ki-67 LI, IDH-mutant astrocytoma with high Ki-67 LI was more likely to show necrosis, edema, enhancement, poorly defined margin, higher ITSS levels, lower ADC, and higher rCBV. • ATRX wild-type IDH-mutant astrocytoma was more likely to show edema, higher ITSS levels, and lower ADC compared to ATRX mutant IDH-mutant astrocytoma.

Whole-tumor histogram analysis of diffusion and perfusion metrics for noninvasive pediatric glioma grading.

PURPOSE: An accurate assessment of the World Health Organization grade is vital for patients with pediatric gliomas to direct treatment planning. We aim to evaluate the diagnostic performance of whole-tumor histogram analysis of diffusion-weighted imaging (DWI) and dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC-PWI) for differentiating pediatric high-grade gliomas from pediatric low-grade gliomas. METHODS: Sixty-eight pediatric patients (mean age, 10.47 ± 4.37 years; 42 boys) with histologically confirmed gliomas underwent preoperative MR examination. The conventional MRI features and whole-tumor histogram features extracted from apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) maps were analyzed, respectively. Receiver operating characteristic curves and the binary logistic regression analysis were performed to determine the diagnostic performance of parameters. RESULTS: For conventional MRI features, location, hemorrhage and tumor margin showed significant difference between pediatric high- and low-grade gliomas (all, P < .05). For advanced MRI parameters, ten histogram features of ADC and CBV showed significant differences between pediatric high- and low-grade gliomas (all, P < .05). The diagnostic performance of the combination of DSC-PWI and DWI (AUC = 0.976, sensitivity = 100%, NPV = 100%) is superior to conventional MRI or DWI model, respectively (AUCcMRI = 0.700, AUCDWI = 0.830; both, P < .05). CONCLUSION: The whole-tumor histogram analysis of DWI and DSC-PWI is a promising method for grading pediatric gliomas.

Grading meningiomas with diffusion metrics: a comparison between diffusion kurtosis, mean apparent propagator, neurite orientation dispersion and density, and diffusion tensor imaging.

OBJECTIVES: To compare the histogram features of multiple diffusion metrics in predicting the grade and cellular proliferation of meningiomas. METHODS: Diffusion spectrum imaging was performed in 122 meningiomas (30 males, 13-84 years), which were divided into 31 high-grade meningiomas (HGMs, grades 2 and 3) and 91 low-grade meningiomas (LGMs, grade 1). The histogram features of multiple diffusion metrics obtained from diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), mean apparent propagator (MAP), and neurite orientation dispersion and density imaging (NODDI) in the solid tumours were analysed. All values between the two groups were compared with the Man-Whitney U test. Logistic regression analysis was applied to predict meningioma grade. The correlation between diffusion metrics and Ki-67 index was analysed. RESULTS: The DKI_AK (axial kurtosis) maximum, DKI_AK range, MAP_RTPP (return-to-plane probability) maximum, MAP_RTPP range, NODDI_ICVF (intracellular volume fraction) range, and NODDI_ICVF maximum values were lower (p < 0.0001), whilst the DTI_MD (mean diffusivity) minimum values were higher in LGMs than those in HGMs (p < 0.001). Amongst the DTI, DKI, MAP, NODDI, and combined diffusion models, no significant differences were found in areas under the receiver operating characteristic curves (AUCs) for grading meningiomas (AUCs, 0.75, 0.75, 0.80, 0.79, and 0.86, respectively; all corrected p > 0.05, Bonferroni correction). Significant but weak positive correlations were found between the Ki-67 index and DKI, MAP, and NODDI metrics (r = 0.26-0.34, all p < 0.05). CONCLUSIONS: Whole tumour histogram analyses of the multiple diffusion metrics from four diffusion models are promising methods in grading meningiomas. The DTI model has similar diagnostic performance compared with advanced diffusion models. KEY POINTS: • Whole tumour histogram analyses of multiple diffusion models are feasible for grading meningiomas. • The DKI, MAP, and NODDI metrics are weakly associated with the Ki-67 proliferation status. • DTI has similar diagnostic performance compared with DKI, MAP, and NODDI in grading meningiomas.

Apparent Diffusion Coefficient in the Differentiation of Common Pediatric Brain Tumors in the Posterior Fossa: Different Region-of-Interest Selection Methods for Time Efficiency, Measurement Reproducibility, and Diagnostic Utility.

OBJECTIVES: This study aimed to explore the diagnostic ability of apparent diffusion coefficient (ADC) values obtained from different region of interest (ROI) measurements in tumor parenchyma for differentiating posterior fossa tumors (PFTs) and the correlations between ADC values and Ki-67. METHODS: Seventy-three pediatric patients with PFTs who underwent conventional diffusion-weighted imaging were recruited in this study. Five different ROIs were manually drawn by 2 radiologists (ROI-polygon, ROI-3 sections, ROI-3-5 ovals, ROI-more ovals, and ROI-whole). The interreader/intrareader repeatability, time required, diagnostic ability, and Ki-67 correlation analysis of the ADC values based on these ROI strategies were calculated. RESULTS: Both interreader and intrareader reliabilities were excellent for ADC values among the different ROI strategies (intraclass correlation coefficient, 0.899-0.992). There were statistically significant differences in time consumption among the 5 ROI selection methods ( P < 0.001). The time required for the ROI-3-5 ovals was the shortest (32.23 ± 5.14 seconds), whereas the time required for the ROI-whole was the longest (204.52 ± 92.34 seconds). The diagnostic efficiency of the ADC values showed no significant differences among the different ROI measurements ( P > 0.05). The ADC value was negatively correlated with Ki-67 ( r = -0.745 to -0.798, all P < 0.0001). CONCLUSIONS: The ROI-3-5 ovals method has the best interobserver repeatability, the shortest amount of time spent, and the best diagnostic ability. Thus, it is considered an effective measurement to produce ADC values in the evaluation of pediatric PFTs.

Anti-Inflammatory Activity of Phenylethanoids from Acanthus ilicifolius var. xiamenensis.

Acanthus ilicifolius var. xiamenensis is a traditional herbal medicine in China. In this study, the anti-inflammatory activities of active ingredients of A. ilicifolius var. xiamenensis were investigated in RAW 264.7 cells and Freund's complete adjuvant-induced arthritic rats. Results showed that n-butanol extract exerted antiarthritic potential by reducing paw edema, arthritis score, and altered hematological and biochemical parameters in experimental rats. Phytochemical studies on n-butanol extract resulted in the isolation of five alkaloids (1-5) and five phenylethanoids (6-10). The anti-inflammatory assay of compounds 1-10 on lipopolysaccharide (LPS)-treated RAW 264.7 cells indicated that phenylethanoids 9 and 10 exhibited notable inhibitory activities. The result indicated that compounds 9 and 10 attenuated inflammation by decreasing the production of nuclear factor kappa-B (NF-κB) p65, inhibitory subunit of NF kappa B alpha, Janus kinase 2 (JAK2), signal transducer and activator of transcription 3 (STAT3), and inducible nitric oxide synthase in LPS-mediated RAW 264.7 macrophages. Phenylethanoids 9 and 10 increased the expression of interleukin-10 and endothelial nitric oxide synthase. Therefore, compounds 9 and 10 showed anti-inflammatory activity by regulation of NF-κB and JAK/STAT signaling pathways.

Clinic-radiological features and radiomics signatures based on Gd-BOPTA-enhanced MRI for predicting advanced liver fibrosis.

OBJECTIVES: To develop and validate a combined model based on Gd-BOPTA-enhanced MRI to identify advanced liver fibrosis. METHODS: A total of 102 patients with chronic HBV infection were divided into a training cohort (n = 80) and a time-independent testing cohort 1 (n = 22). In the training cohort, radiomics signatures were extracted from the hepatobiliary phase. Model 1 was constructed with clinic-radiological factors using multivariable logistic regression to predict advanced liver fibrosis, and model 2 incorporated radiomics signatures based on model 1. The diagnostic performances were compared with serum fibrosis tests and FibroScan tests using area under curve (AUC) in testing cohort 1. Another 45 patients with other causes were collected in testing cohort 2 for further validation. RESULTS: Model 1 showed age (OR = 1.079) and periportal space widening (OR = 7.838) were the independent factors for predicting advanced fibrosis. After integrating radiomics signatures, model 2 enabled more accurately than model 1 in training cohort (0.940 vs. 0.802, p = 0.003). In testing cohort 1, model 2 demonstrated a superior AUC compared with model 1 (0.900 vs. 0.813，p = 0.131), FibroScan test (0.900 vs. 0.733, p = 0.193), and serum fibrosis tests (APRI and Fib-4 was 0.667 and 0.791). In testing cohort 2, model 2 incorporating radiomics signatures showed satisfactory performance (0.874 vs. 0.757，p = 0.010) compared with model 1. CONCLUSIONS: Radiomics signatures derived from Gd-BOPTA-enhanced HBP images may offer complementary information to the clinic-radiological model for predicting advanced liver fibrosis. KEY POINTS: • Linear or reticular hyperintensity on T2WI, periportal space widening, and diffuse periportal enhancement on HBP can be useful for predicting advanced liver fibrosis. • Clinic-radiological features such as patient age and periportal space widening are the two independent factors predicting advanced fibrosis. • Radiomics signatures derived from Gd-BOPTA-enhanced HBP images offer complementary information to the clinic-radiological model for predicting advanced liver fibrosis.

Gender differences in lateral pterygoid muscle in patients with anterior disk displacement.

OBJECTIVE: To use quantitative MRI to assess gender differences in lateral pterygoid muscle (LPM) characteristics in patients with anterior disk displacement (ADD). METHODS: Lateral pterygoid muscle of 51 patients diagnosed with temporomandibular joint disorders (TMD) who underwent T1-weighted Dixon and T1-mapping sequences were retrospectively analyzed. There were 34 female patients (10 with bilateral normal position disk [NP]; 24 with bilateral ADD) and 17 male patients (eight with bilateral NP; nine with bilateral ADD) among them. After controlling for age, differences in fat fraction, T1 value, volume and histogram features related to gender and disk status were tested with 2-way ANCOVA or Quade ANCOVA with Bonferroni correction. RESULTS: Volume of LPM in NP was significantly smaller than that of ADD (p < 0.001). Fat fraction of LPM in females with NP was significantly higher than males with NP (p < 0.05). Females with ADD showed a significantly higher T1 value (p < 0.05), and higher intramuscular heterogeneity than males with ADD. CONCLUSIONS: Lateral pterygoid muscle in female TMD patients presented more fatty infiltration in the NP stage and might present more fibrosis in the ADD stage compared with males. Together, this leads to more serious intramuscular heterogeneity during the pathogenesis of ADD in females.