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Background: A sizable number of patients with focal cortical dysplasia (FCD) type III-related refractory epilepsy continue to experience seizures postsurgically. Deep learning models can automatically assess complex medical image characteristics and predict prognosis with higher efficiency. This study sought to determine whether T2-weighted fluid attenuated inversion recovery (T2W FLAIR) images could predict prognosis of FCD type III-related refractory epilepsy using a deep learning approach. Methods: Magnetic resonance imaging (MRI) images of 266 patients with FCD type III diagnosed between 2015 and 2019 were included in this retrospective analysis. A deep learning algorithm utilizing a convolutional neural network (CNN) was trained to classify T2W FLAIR images according to Engel's classification. The preprocessed original image and the region of interest (ROI) outlined by clinicians were input into our neural network separately and then together. Precision, sensitivity, specificity, receiver operating characteristic (ROC) curves, and areas under the ROC curves (AUCs) were computed as part of the statistical analyses of the network performance with varied inputs of the network model assessed. Results: The overall performance met the following metrics when the original image only was input: AUC of 96.22%, sensitivity of 84.47%, and specificity of 97.21%. The metrics were as follows when the ROI only was input: area under the ROC curve of 94.76%, sensitivity of 84.92%, and specificity of 96.24%. For the combined inputs, the metrics were as follows: AUC of 97.17%, sensitivity of 90.86%, and specificity of 96.63%. Conclusions: Deep learning used with conventional MRI can effectively predict the recurrence conditions of epilepsy. Artificial intelligence may help the design of clinical management and enable more precise and individualized prediction for postsurgical prognosis of FCD type III-related refractory epilepsy.
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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.
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This study aims to explore the relationship between neuropathologic and the post-surgical prognosis of focal cortical dysplasia (FCD) typed-â ¢-related medically refractory epilepsy. A total of 266 patients with FCD typed-â ¢-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 â ¢ 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 â ¢. 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 â ¢.
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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 CBFpercentile5 and CBFuniformity of 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 CBFuniformity of the enhancing tumour and ETCM were predictive features for p53 mutation. CBFuniformity showed 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.
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Neoplasias Encefálicas/genética , Imagen Eco-Planar/métodos , Genes p53/genética , Glioma/genética , Mutación , Adulto , Área Bajo la Curva , Edema Encefálico/diagnóstico por imagen , Neoplasias Encefálicas/irrigación sanguínea , Neoplasias Encefálicas/diagnóstico por imagen , Neoplasias Encefálicas/patología , Circulación Cerebrovascular , Femenino , Glioma/irrigación sanguínea , Glioma/diagnóstico por imagen , Glioma/patología , Humanos , Imagen por Resonancia Magnética/métodos , Masculino , Persona de Mediana Edad , Clasificación del Tumor , Periodo Preoperatorio , Curva ROC , Marcadores de Spin , Proteína p53 Supresora de Tumor/análisisRESUMEN
Using computed tomography myocardial perfusion imaging (CTP) to investigate resting myocardial perfusion alterations in uncomplicated type 2 diabetes mellitus (T2DM) patients without obstructive coronary artery disease (CAD).A total of 34 participants with 544 myocardial segments were included prospectively: 17 uncomplicated T2DM patients with no significant coronary artery stenosis on coronary computed tomography angiography and 17 healthy controls. Myocardial perfusion was evaluated by transmural perfusion ratio (TPR). Parameters of cardiac structure and function were measured for cardiac comprehensive assessment. Analyses included descriptive statistics and group comparisons.TPR of segments 5, 7, 9, 10 to 14 were significantly reduced in T2DM group compared with controls (Pâ<â.05). When 16 myocardial segments were localized into different areas according to the wall orientations, axial levels of left ventricle and coronary artery territories, respectively, TPR of each area in T2DM group were significantly lower than those in the control group (Pâ<â.05). No significant differences were found in cardiac anatomy and function analyses between 2 groups.In uncomplicated T2DM patients without obstructive CAD, myocardial perfusion impairments were present and may develop prior to cardiac morphological and functional abnormalities, which can be early detected by CTP.