The therapeutic planning varies for different grades of choroid plexus tumours (CPTs). The aim of this study was to define the similarities and distinctions among MRIs for different grades of CPTs, providing more guidance for clinical decisions. We reviewed the MRI findings in 35 patients with CPT verified by surgical pathology, including 18 choroid plexus papillomas (CPPs, grade I), 11 atypical choroid plexus papillomas (aCPPs, grade II), and 6 choroid plexus carcinomas (CPCs, grade III). Nonparametric testing based on ranks was performed to evaluate the association of pathological grade with MRI findings. Among the 35 CPTs, 29 were located in the ventricular system. The tumours were generally slightly hypo- or isointense on T1WI, slightly hyper- or isointense on T2WI, and moderately or strongly enhanced in post-contrast imaging. Twenty cases were accompanied by hydrocephalus. The median tumour longest diameters of CPPs, aCPPs, and CPCs were 28.6, 44.6, and 60.6 mm, respectively. Four cases were purely cystic, 6 were papillary, 10 were lobulated, and 2 were irregular. Three cases had necrosis. The median oedema diameters of CPPs, aCPPs, and CPCs were 0, 0, and 24.1 mm, respectively. The grades of CPTs were statistically associated with tumour longest diameter (rs = 0.68, P < 0.001), internal morphology (χ2 = 10.32, P = 0.016), necrosis (Z = 2.27, P = 0.023), and oedema diameter (rs = 0.72, P < 0.001). CPTs typically appeared as intraventricular papillary or lobulated lesions, often accompanied by hydrocephalus. Larger tumour, irregular or fuzzy internal morphology, presentation of necrosis and wide-ranging peritumoural oedema might increase the likelihood of malignancy.

It is crucial to differentiate patients with temporal lobe epilepsy (TLE) from the healthy population and determine abnormal brain regions in TLE. The cortical features and changes can reveal the unique anatomical patterns of brain regions from structural magnetic resonance (MR) images. In this study, structural MR images from 41 patients with left TLE, 34 patients with right TLE, and 58 normal controls (NC) were acquired, and four kinds of cortical measures, namely cortical thickness, cortical surface area, gray matter volume (GMV), and mean curvature, were explored for discriminative analysis. Three feature selection methods including the independent sample t-test filtering, the sparse-constrained dimensionality reduction model (SCDRM), and the support vector machine-recursive feature elimination (SVM-RFE) were investigated to extract dominant features among the compared groups for classification using the support vector machine (SVM) classifier. The results showed that the SVM-RFE achieved the highest performance (most classifications with more than 84% accuracy), followed by the SCDRM, and the t-test. Especially, the surface area and GMV exhibited prominent discriminative ability, and the performance of the SVM was improved significantly when the four cortical measures were combined. Additionally, the dominant regions with higher classification weights were mainly located in the temporal and the frontal lobe, including the entorhinal cortex, rostral middle frontal, parahippocampal cortex, superior frontal, insula, and cuneus. This study concluded that the cortical features provided effective information for the recognition of abnormal anatomical patterns and the proposed methods had the potential to improve the clinical diagnosis of TLE.

Background: Accurate identification of Parkinson’s disease (PD) and Parkinsonism-Plus syndrome (PPS), especially in the early stage of the disease, is very important. The purpose of this study was to investigate the discriminative spatial pattern of cerebral blood flow (CBF) between patients with PD and PPS. Methods: Arterial spin labeling (ASL) perfusion-weighted imaging was performed in 20 patients with PD (mean age 56.35 ± 7.56 years), 16 patients with PPS (mean age 59.62 ± 6.89 years), and 17 healthy controls (HCs, mean age 54.17 ± 6.58 years). Voxel-wise comparison of the CBF was performed among PD, PPS, and HC groups. The receiver operating characteristic (ROC) curve was used to evaluate the performance of CBF in discriminating between PD and PPS. The relationship between CBF and non-motor neuropsychological scores was assessed by correlation analysis. Results: PD group showed a significantly decreased CBF in the right cerebelum_crus2, the left middle frontal gyrus (MFG), the triangle inferior frontal gyrus (IFG_Tri), the left frontal medial orbital gyrus (FG_Med_Orb) and the left caudate nucleus (CN) compared with the HC group (P < 0.05). Besides the above regions, the left supplementary motor area (SMA), the right thalamus had decreased CBF in the PPS group compared with the HC group (P < 0.05). PPS group had lower CBF value in the left MFG, the left IFG_Tri, the left CN, the left SMA, and the right thalamus compared with the PD group (P < 0.05). CBFs in left IFG_Tri, the left CN, the left SMA, and the right thalamus had moderate to high capacity in discriminating between PD and PPS patients (AUC 0.719–0.831). The CBF was positively correlated with the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores in PD patients, while positively correlated with the MMSE, Hamilton Anxiety Scale (HAMA), Hamilton Depression Scale (HAMD) scores in PPS patients (P < 0.05). Conclusion: PD and PPS patients have certain discriminative patterns of reduced CBFs, which can be used as a surrogate marker for differential diagnosis.

Objectives: To determine the performance of pretreatment structural and arterial spin labelling (ASL) MRI in predicting p53 mutation in patients with high-grade gliomas (HGGs).Methods: Pre-treatment structural and ASL MRI were performed in 57 patients with histologically confirmed HGGs and information of p53 status. Whole-lesion histogram analysis of cerebral blood flow (CBF) images of the enhancing tumour and the peritumoral oedema in the HGGs were performed. Visually AcceSAble Rembrandt Images features were used as qualitative analysis. The differences of ASL histogram parameters and Visually AcceSAble Rembrandt Images features between HGGs with or without p53 mutation were analyzed with post hoc correction for multiple comparisons. LASSO regression was performed to select the optimal features that could predict p53 mutation, followed by receiver operating characteristic analysis to determine the predictive efficacy.Results: A total of 33 HGGs with p53 mutation and 24 without p53 mutation were included. HGGs with mutant p53 showed lower CBF_percentile5and CBF_uniformityof the enhancing tumour (p < 0.05) and higher prevalence of the qualitative MRI feature of enhancing tumour crossing midline (ETCM) (p < 0.05) as compared with HGGs with wild-type p53. LASSO regression showed that the CBF_uniformityof the enhancing tumour and ETCM were predictive features for p53 mutation. CBF_uniformityshowed an acceptable performance in predicting p53 mutation (area under the curve = 0.721), when combined with the feature of ETCM, its predictive efficacy was significantly improved (area under the curve = 0.814, p = 0.012).Conclusion: An integrated pre-treatment structural and ASL MRI can help to predict p53 mutation in HGGs.

Objectives: To explore the feasibility of predicting overall survival (OS) of patients with midline glioma using multi-parameter magnetic resonance imaging (MRI) features. Methods: Data of 84 patients with midline gliomas were retrospectively collected, including 40 patients with OS > 12 months (28 cases were adults, 14 cases were H3 K27M-mutation) and 44 patients with OS < 12 months (29 cases were adults, 31 cases were H3 K27M-mutation). Features were extracted from the largest slice of tumors, which were manually segmented on T2-weighted (T2w), T2 fluid-attenuated inversion recovery (T2 FLAIR), and contrast-enhanced T1-weighted (T1c) images. Data were randomly divided into training (70%) and test cohorts (30%) and normalized and standardized using Z-scores. Feature dimensionality reduction was performed using the variance method and maximum relevance and minimum redundancy (mRMR) algorithm. We used the logistic regression algorithm to construct three models for T2w, T2 FLAIR, and T1c images as well as one combined model. The test cohort was used to evaluate the models, and receiver operating characteristic (ROC) curves, areas under the curve (AUCs), sensitivity, specificity, and accuracy were calculated. The nomogram of the combined model was built and evaluated using a calibration curve. Decision curve analysis (DCA) was used to evaluate the clinical application value of the four models. Results: A total of 1,316 features were extracted from T2w, T2 FLAIR, and T1c images, respectively. And then the best non-redundant features were selected from the extracted features using the variance method and mRMR. Finally, five features were extracted each from T2w, T2 FLAIR, and T1c images, and 12 features were extracted for the combined model. Four models were established using the optimal features. In the test cohort, the combined model performed the best out of all models. The AUCs of the T2w, T2 FLAIR, T1c, and combined models were 0.73, 0.78, 0.74, and 0.87, respectively, and accuracies were 0.72, 0.76, 0.72, and 0.84, respectively. The ROC curves and DCA showed that the combined model had the highest efficiency and most favorable clinical benefits. Conclusion: The combined radiomics model based on multi-parameter MRI features provided a reliable non-invasive method for the prognostic prediction of midline gliomas.

This study aims to explore the relationship between neuropathologic and the post-surgical prognosis of focal cortical dysplasia (FCD) typed-Ⅲ-related medically refractory epilepsy. A total of 266 patients with FCD typed-Ⅲ-related medically refractory epilepsy were retrospectively studied. Presurgical clinical data, type of surgery, and postsurgical seizure outcome were analyzed. The minimum post-surgical follow-up was 1 year. A total of 266 patients of FCD type Ⅲ were included in this study and the median follow-up time was 30 months (range, 12~48 months). Age at onset ranged from 1.0 years to 58.0 years, with a median age of 12.5 years. The number of patients under 12 years old was 133 (50%) in patients with FCD type Ⅲ. A history of febrile seizures was present in 42 (15.8%) cases. In the entire postoperative period, 179 (67.3%) patients were seizure-free. Factors with p < 0.15 in univariate analysis, such as age of onset of epilepsy (p = 0.145), duration of epilepsy (p = 0.004), febrile seizures (p = 0.150), being MRI-negative (p = 0.056), seizure type (p = 0.145) and incomplete resection, were included in multivariate analysis. Multivariate analyses revealed that MRI-negative findings of FCD (OR 0.34, 95% CI 0.45–0.81, p = 0.015) and incomplete resection (OR 0.12, 95% CI 0.05–0.29, p < 0.001) are independent predictors of unfavorable seizure outcomes. MRI-negative finding of FCD lesions and incomplete resection were the most important predictive factors for poor seizure outcome in patients with FCD type Ⅲ.