T2 Fluid-attenuated Inversion Recovery Research Articles

Real-World Validation of Coregistration and Structured Reporting for Magnetic Resonance Imaging Monitoring in Multiple Sclerosis.

The objectives of this research were to assess the effectiveness of computer-assisted detection reading (CADR) and structured reports in monitoring patients with multiple sclerosis (MS) and to evaluate the role of radiology technicians in this context. Eighty-seven patients with MS who underwent at least 2 sequential magnetic resonance imaging (MRI) follow-ups analyzed by 2 radiologists and a technician. Progression of disease (POD) was identified through the emergence of T2 fluid-attenuated inversion recovery white matter hyperintensities or contrast enhancements and evaluated both qualitatively (progression vs stability) and quantitatively (count of new white matter hyperintensities). CADR increased the accuracy by 11%, enhancing interobserver consensus on qualitative progression and saving approximately 2 minutes per examination. Although structured reports did not improve these metrics, it may improve clinical communication and permit technicians to achieve approximately 80% accuracy in MRI readings. The use of CADR improves the accuracy, agreement, and interpretation time in MRI follow-ups of MS. With the help of computer tools, radiology technicians could represent a significant aid in the follow-up of these patients.

Evaluation of preoperative magnetic resonance imaging features and diagnostic effectiveness of grades II and III intracranial solitary fibroma

ObjectivesTo explore the value of preoperative magnetic resonance imaging (MRI) characterization of intracranial solitary fibrous tumors (ISFT) and to evaluate the effectiveness of preoperative MRI features in predicting pathological grading.Materials and methodsThis retrospective analysis comprised the clinical and preoperative MRI characterization of 55 patients with ISFT in our hospital, including 27 grade II cases and 28 grade III cases confirmed by postoperative pathology. Variables included age, sex, tumor location, cross-midline status, signal characteristics of T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), T2-fluid-attenuated inversion recovery (T2-FLAIR), and diffusion‑weighted imaging (DWI), peritumoral edema, intralesional hemorrhage, focal necrosis/cystic degeneration, tumor empty vessel, maximum tumor diameter, maximum, minimum, and average values of apparent diffusion coefficient (ADCmax, ADCmin, and ADCmean), tumors enhancement mode, meningeal tail sign, skull invasion, cerebral parenchymal invasion, and venous sinus involvement. The independent samples t test or Mann–Whitney U test was performed to compare continuous data between the two groups, and the Pearson chi-squared test or Fisher’s exact test was used to compare categorical data. In addition, bivariate logistic regression was performed to construct a comprehensive model, and receiver operating characteristic (ROC) curves were generated to calculate the areas under the curve (AUCs), thereby determining the value of each parameter in the differential diagnosis of grades II and III ISFT.ResultsThe mean age at onset was similar between patients with grades II and III ISFT (46.77 ± 14.66 years and 45.82 ± 12.07 years, respectively). The proportions of men among patients with grades II and III ISFT were slightly higher than those of female patients (male/female: 1.25 [15/12] and 1.33 [16/12], respectively). There were significant differences between grades II and III ISFT in the T2-FLAIR and DWI signal characteristics, maximum, minimum, and average values of the apparent diffusion coefficient (ADCmax, ADCmin, and ADCmean), tumor location, and skull invasion (P = 0.001, P = 0.018, P = 0.000, P = 0.000, P = 0.000, P = 0.010, and P = 0.032, respectively). However, no significant differences were noted between grades II and III ISFT in age, sex, cross-midline status, T1WI and T2WI signal characteristics, peritumoral edema, intralesional hemorrhage, focal necrosis/cystic degeneration, tumor empty vessel shadow, enhancement mode, meningeal tail sign, maximum tumor diameter, brain parenchyma invasion, or venous sinus involvement (all P > 0.05). Moreover, binary logistic regression analysis showed that the model accuracy was 89.1% when ADCmin was included in the regression equation. Moreover, ROC curve analysis showed that the AUC of ADCmin was 0.805 (0.688, 0.922), sensitivity was 74.1%, specificity was 75.0%, and the cutoff value was 672 mm2/s.ConclusionsGrade III ISFT patients displayed more mixed T2-FLAIR signal characteristics and DWI signal characteristics than grade II patients, as shown by higher skull invasion and tumor mass collapse midline distribution and lower ADCmax, ADCmean, and ADCmin values. The ADCmin value was significant in the preoperative assignment of grades II and III ISFT, thereby contributing to enhanced accuracy in the imaging grading diagnosis of the disease.

Ultra-High Contrast MRI: The Whiteout Sign Shown with Divided Subtracted Inversion Recovery (dSIR) Sequences in Post-Insult Leukoencephalopathy Syndromes (PILS).

Ultra-high contrast (UHC) MRI describes forms of MRI in which little or no contrast is seen on conventional MRI images but very high contrast is seen with UHC techniques. One of these techniques uses the divided subtracted inversion recovery (dSIR) sequence, which, in modelling studies, can produce ten times the contrast of conventional inversion recovery (IR) sequences. When used in cases of mild traumatic brain injury (mTBI), the dSIR sequence frequently shows extensive abnormalities in white matter that appears normal when imaged with conventional T2-fluid-attenuated IR (T2-FLAIR) sequences. The changes are bilateral and symmetrical in white matter of the cerebral and cerebellar hemispheres. They partially spare the anterior and posterior central corpus callosum and peripheral white matter of the cerebral hemispheres and are described as the whiteout sign. In addition to mTBI, the whiteout sign has also been seen in methamphetamine use disorder and Grinker's myelinopathy (delayed post-hypoxic leukoencephalopathy) in the absence of abnormalities on T2-FLAIR images, and is a central component of post-insult leukoencephalopathy syndromes. This paper describes the concept of ultra-high contrast MRI, the whiteout sign, the theory underlying the use of dSIR sequences and post-insult leukoencephalopathy syndromes.

Multiparameter MRI-based radiomics nomogram for preoperative prediction of brain invasion in atypical meningioma:a multicentre study

ObjectiveTo develop a nomogram based on tumor and peritumoral edema (PE) radiomics features extracted from preoperative multiparameter MRI for predicting brain invasion (BI) in atypical meningioma (AM).MethodsIn this retrospective study, according to the 2021 WHO classification criteria, a total of 469 patients with pathologically confirmed AM from three medical centres were enrolled and divided into training (n = 273), internal validation (n = 117) and external validation (n = 79) cohorts. BI was diagnosed based on the histopathological examination. Preoperative contrast-enhanced T1-weighted MR images (T1C) and T2-weighted MR images (T2) for extracting meningioma features and T2-fluid attenuated inversion recovery (FLAIR) sequences for extracting meningioma and PE features were obtained. The multiple logistic regression was applied to develop separate multiparameter radiomics models for comparison. A nomogram was developed by combining radiomics features and clinical risk factors, and the clinical usefulness of the nomogram was verified using decision curve analysis.ResultsAmong the clinical factors, PE volume and PE/tumor volume ratio are the risk of BI in AM. The combined nomogram based on multiparameter MRI radiomics features of meningioma and PE and clinical indicators achieved the best performance in predicting BI in AM, with area under the curve values of 0.862 (95% CI, 0.819–0.905) in the training cohort, 0.834 (95% CI, 0.780–0.908) in the internal validation cohort and 0.867 (95% CI, 0.785–0.950) in the external validation cohort, respectively.ConclusionsThe nomogram based on tumor and PE radiomics features extracted from preoperative multiparameter MRI and clinical factors can predict the risk of BI in patients with AM.

MR-based radiomics predictive modelling of EGFR mutation and HER2 overexpression in metastatic brain adenocarcinoma: a two-centre study

ObjectivesMagnetic resonance (MR)-based radiomics features of brain metastases are utilised to predict epidermal growth factor receptor (EGFR) mutation and human epidermal growth factor receptor 2 (HER2) overexpression in adenocarcinoma, with the aim to identify the most predictive MR sequence.MethodsA retrospective inclusion of 268 individuals with brain metastases from adenocarcinoma across two institutions was conducted. Utilising T1-weighted imaging (T1 contrast-enhanced [T1-CE]) and T2 fluid-attenuated inversion recovery (T2-FLAIR) sequences, 1,409 radiomics features were extracted. These sequences were randomly divided into training and test sets at a 7:3 ratio. The selection of relevant features was done using the least absolute shrinkage selection operator, and the training cohort’s support vector classifier model was employed to generate the predictive model. The performance of the radiomics features was evaluated using a separate test set.ResultsFor contrast-enhanced T1-CE cohorts, the radiomics features based on 19 selected characteristics exhibited excellent discrimination. No significant differences in age, sex, and time to metastasis were observed between the groups with EGFR mutations or HER2 + and those with wild-type EGFR or HER2 (p > 0.05). Radiomics feature analysis for T1-CE revealed an area under the curve (AUC) of 0.98, classification accuracy of 0.93, sensitivity of 0.92, and specificity of 0.93 in the training cohort. In the test set, the AUC was 0.82. The 19 radiomics features for the T2-FLAIR sequence showed AUCs of 0.86 in the training set and 0.70 in the test set.ConclusionsThis study developed a T1-CE signature that could serve as a non-invasive adjunctive tool to determine the presence of EGFR mutations and HER2 + status in adenocarcinoma, aiding in the direction of treatment plans.Clinical relevance statementWe propose radiomics features based on T1-CE brain MR sequences that are both evidence-based and non-invasive. These can be employed to guide clinical treatment planning in patients with brain metastases from adenocarcinoma.

Surgical outcomes in children with drug-resistant epilepsy and hippocampal sclerosis

BackgroundHippocampal sclerosis (HS) is a common surgical substrate in adult epilepsy surgery cohorts but variably reported in various pediatric cohorts. ObjectiveWe aimed to study the epilepsy phenotype, radiological and pathological variability, seizure and neurocognitive outcomes in children with drug-resistant epilepsy and hippocampal sclerosis (HS) with or without additional subtle signal changes in anterior temporal lobe who underwent surgery. MethodsThis retrospective study enrolled children with drug-resistant focal epilepsy and hippocampal sclerosis with or without additional subtle T2-Fluid Attenuated Inversion Recovery (FLAR)/Proton Density (PD) signal changes in anterior temporal lobe who underwent anterior temporal lobectomy with amygdalohippocampectomy. Their clinical, EEG, neuropsychological, radiological and pathological data were reviewed and summarized. ResultsThirty-six eligible patients were identified. The mean age at seizure onset was 3.7 years; 25% had daily seizures at time of surgery. Isolated HS was noted in 22 (61.1%) cases and additional subtle signal changes in ipsilateral temporal lobe in 14 (38.9%) cases. Compared to the normative population, the group mean performance in intellectual functioning and most auditory and visual memory tasks were significantly lower than the normative sample. The mean age at surgery was 12.3 years; 22 patients (61.1%) had left hemispheric surgeries. ILAE class 1 outcomes was seen in 28 (77.8%) patients after a mean follow up duration of 2.3 years. Hippocampal sclerosis was noted pathologically in 32 (88.9%) cases; type 2 (54.5%) was predominant subtype where further classification was possible. Additional pathological abnormalities were seen in 11 cases (30.6%); these had had similar rates of seizure freedom as compared to children with isolated hippocampal sclerosis/gliosis (63.6% vs 84%, p=0.21). Significant reliable changes were observed across auditory and visual memory tasks at an individual level post surgery. ConclusionsFavourable seizure outcomes were seen in most children with isolated radiological hippocampal sclerosis. Patients with additional pathological abnormalities had similar rates of seizure freedom as compared to children with isolated hippocampal sclerosis/gliosis.

Neuroinvasive West Nile Virus Infection in Immunosuppressed and Immunocompetent Adults

West Nile virus (WNV) is the leading cause of human arboviral disease in the US, peaking during summer. The incidence of WNV, including its neuroinvasive form (NWNV), is increasing, largely due to the expanding distribution of its vector, the Culex mosquito, and climatic changes causing heavy monsoon rains. However, the distinct characteristics and outcomes of NWNV in individuals who are immunosuppressed (IS) and individuals who are not IS remain underexplored. To describe and compare clinical and radiographic features, treatment responses, and outcomes of NWNV infection in individuals who are IS and those who are not IS. This retrospective cohort study used data from the Mayo Clinic Hospital system collected from July 2006 to December 2021. Participants were adult patients (age ≥18 years) with established diagnosis of NWNV infection. Data were analyzed from May 12, 2020, to July 20, 2023. Immunosuppresion. Outcomes of interest were clinical and radiographic features and 90-day mortality among patients with and without IS. Of 115 participants with NWNV infection (mean [SD] age, 64 [16] years; 75 [66%] male) enrolled, 72 (63%) were not IS and 43 (37%) were IS. Neurologic manifestations were meningoencephalitis (98 patients [85%]), encephalitis (10 patients [9%]), and myeloradiculitis (7 patients [6%]). Patients without IS, compared with those with IS, more frequently reported headache (45 patients [63%] vs 18 patients [42%]) and myalgias (32 patients [44%] vs 9 patients [21%]). In contrast, patients with IS, compared with those without, had higher rates of altered mental status (33 patients [77%] vs 41 patients [57%]) and myoclonus (8 patients [19%] vs 8 patients [4%]). Magnetic resonance imaging revealed more frequent thalamic T2 fluid-attenuated inversion recovery hyperintensities in individuals with IS than those without (4 patients [11%] vs 0 patients). Individuals with IS had more severe disease requiring higher rates of intensive care unit admission (26 patients [61%] vs 24 patients [33%]) and mechanical ventilation (24 patients [56%] vs 22 patients [31%]). The 90-day all-cause mortality rate was higher in the patients with IS compared with patients without IS (12 patients [28%] vs 5 patients [7%]), and this difference in mortality persisted after adjusting for Glasgow Coma Scale score (adjusted hazard ratio, 2.22; 95% CI, 1.07-4.27; P = .03). Individuals with IS were more likely to receive intravenous immunoglobulin than individuals without IS (12 individuals [17%] vs 24 individuals [56%]), but its use was not associated with survival (hazard ratio, 1.24; 95% CI, 0.50-3.09; P = .64). In this cohort study of individuals with NWNV infection, individuals with IS had a higher risk of disease complications and poor outcomes than individuals without IS, highlighting the need for innovative and effective therapies to improve outcomes in this high-risk population.

Aided Diagnosis Model Based on Deep Learning for Glioblastoma, Solitary Brain Metastases, and Primary Central Nervous System Lymphoma with Multi-Modal MRI.

(1) Background: Diagnosis of glioblastoma (GBM), solitary brain metastases (SBM), and primary central nervous system lymphoma (PCNSL) plays a decisive role in the development of personalized treatment plans. Constructing a deep learning classification network to diagnose GBM, SBM, and PCNSL with multi-modal MRI is important and necessary. (2) Subjects: GBM, SBM, and PCNSL were confirmed by histopathology with the multi-modal MRI examination (study from 1225 subjects, average age 53 years, 671 males), 3.0 T T2 fluid-attenuated inversion recovery (T2-Flair), and Contrast-enhanced T1-weighted imaging (CE-T1WI). (3) Methods: This paper introduces MFFC-Net, a classification model based on the fusion of multi-modal MRIs, for the classification of GBM, SBM, and PCNSL. The network architecture consists of parallel encoders using DenseBlocks to extract features from different modalities of MRI images. Subsequently, an L1-norm feature fusion module is applied to enhance the interrelationships among tumor tissues. Then, a spatial-channel self-attention weighting operation is performed after the feature fusion. Finally, the classification results are obtained using the full convolutional layer (FC) and Soft-max. (4) Results: The ACC of MFFC-Net based on feature fusion was 0.920, better than the radiomics model (ACC of 0.829). There was no significant difference in the ACC compared to the expert radiologist (0.920 vs. 0.924, p = 0.774). (5) Conclusions: Our MFFC-Net model could distinguish GBM, SBM, and PCNSL preoperatively based on multi-modal MRI, with a higher performance than the radiomics model and was comparable to radiologists.

Abstract TP16: Dental Caries Associated With Cerebral Small Vessel Disease

Background: Dental caries is a chronic oral infection distinct from periodontal disease in that it is associated with both ischemic stroke and intracerebral hemorrhage (ICH). Few studies have investigated the relationship between dental caries and cerebral small vessel disease (CSVD). White matter hyperintensities (WMH) and cerebral microbleeds (CMB) are manifestations of CSVD. Methods: Using the Atherosclerosis Risk in Communities (ARIC) database, we extracted data from 1112 participants assessed for dental caries and vascular risk factors. Of those, 203 (18.3%) had caries and 909 (81.7%) did not. Brain imaging was performed on 3T scanners. WMH was imaged by T2 fluid-attenuated inversion recovery, with volumes derived by a semiautomated algorithm. Multinomial logistic regression to test the association between caries and WMH volume categorized into quartiles. Quartiles (Q) were defined as Q1: <6.4 cm 3 , Q2: 6.4 - <11.6 cm 3 , Q3: 11.6 - <21.4 cm 3 , Q4: ≥21.4 cm 3 . CMBs were graded using a T2* gradient echo. We used binary logistic regression to test the association between caries and CMB dichotomized as >5 or ≤5. Results: Participants with caries were more likely to be African American than white (54% vs. 46%, p<.001), more likely to have hypertension (41% vs. 26%, p<.001) and diabetes (15% vs. 9%, p=.005). There were no differences detected in age (61 + 5 with caries vs. 62 + 5 without caries, p=0.08), gender (54% female vs. 46% male, p=0.66), and smoking history (48% vs. 52%, p=0.71). Dental caries were associated with WMH volume quartiles: Q1 (reference), Q2 OR 1.43 (95% 0.95 to 2.16), Q3 OR 1.78 (1.16 to 2.72), Q4 OR 1.80 (1.17 to 2.77). These associations remained after adjusting for age, gender, race, hypertension, diabetes, and smoking: Q1 (reference), Q2 Adj. OR 1.52 (95% 0.96 to 2.41), Q3 Adj. OR 1.76 (1.11 to 2.78), Q4 Adj. OR 1.63 (1.03 to 2.60). Caries were also associated with CMB>5 (Crude OR 2.78, 95% CI 1.12-6.44), the association strengthened after adjustment (Adj. OR 3.15, 95% CI 1.33-7.47). Conclusion: We report significant independent associations of caries with higher WMH quartiles and CMB>5. This suggests that ischemic stroke and ICH risk in participants with caries may be mediated by CSVD.

Abstract WMP20: Deep Learning Radiomics Identifies White Matter Hyperintensity-Related Cognitive Decline Based on T2-FLAIR

Introduction: Early identification of white matter hyperintensity-related cognitive impairment (WMH-CI) through normal MRI images is of great significance for early clinical intervention to reduce the occurrence of dementia. The aim of this study was to develop a generalizable and interpretable deep learning model for screening WMH-related CI using radiomic features (RFs) from T2 fluid-attenuated inversion recovery (T2-FLAIR) images. Methods: A total of 783 subjects were enrolled from three medical centres. RFs for WMH lesions were extracted from T2-FLAIR images in each subject. A deep learning model with a hierarchical transformer architecture was used to leverage all extracted RFs, instead of feature selection, to develop and cross-validate the diagnostic model for cognitive dysfunction. Unsupervised domain adaptation was used to address the cross-centre data inconsistencies without labelling the outer centre data. A gradient-weighted class activation mapping approach was adopted to identify the RFs that contributed most to the diagnosis. Subgroup analysis, correlation analysis and mediation analysis were performed to confirm the clinical relevance of the model. Besides, we also verify the clinical importance of the model by microstructural pathology of WMH detected by diffusion tensor imaging. Results: The deep learning model showed robust diagnostic power for WMH related CI, with an area under the receiver operating curve (AUC) of 0.841±0.016 in the development cohort. The prediction accuracy, sensitivity, specificity, precision and recall were 0.793 ± 0.108, 0.798 ± 0.021, 0.800 ± 0.065, 0.716±0.055 and 0.793 ± 0.108, respectively. The model generalized well across different subgroups. It also performed well in other two external verification cohorts, with an AUC of 0.859 and 0.749, respectively. The visual representation showed that the most important features were textural features, which were also significantly correlated with clinical assessment scale and diffusion parameters. Conclusions: This study presents a non-invasive imaging biomarker that can identify WMH-CI patients and is applicable to all levels of hospitals since only conventional MRI images are needed.

Predicting cerebral glioma enhancement pattern using a machine learning-based magnetic resonance imaging radiomics model

To establish a machine learning radiomics model that can accurately predict MRI enhancement patterns of glioma based on T2 fluid attenuated inversion recovery (T2-FLAIR) images for optimizing the workflow of magnetic resonance imaging (MRI) examinations of glioma patients. We retrospectively collected preoperative MR T2-FLAIR images from 385 patients with pathologically confirmed glioma, who were divided into enhancing and non-enhancing groups according to the enhancement pattern. Predictive radiomics models were established using Gaussian Process, Linear Regression, Linear Regression-Least absolute shrinkage and selection operator, Support Vector Machine, Linear Discriminant Analysis or Naive Bayes as the classifiers in the training cohort (n=201)and tested both in the internal (n=85) and external validation cohorts (n=99). The receiver-operating characteristic curve was used to assess the predictive performance of the models. The predictive model constructed based on 15 radiomics features using Gaussian Process as the classifier had the best predictive performance in both the training cohort and the internal validation cohort, with areas under the curve (AUC) of 0.88 (95% CI: 0.81-0.94) and 0.80 (95% CI: 0.71-0.88), respectively. In the external validation cohort, the model showed an AUC of 0.81 (95% CI: 0.71-0.90) with sensitivity, specificity, positive predictive value and negative predictive value of 0.98, 0.61, 0.76 and 0.96, respectively. The T2-FLAIR-based machine learning radiomics model can accurately predict the enhancement pattern of gliomas on MRI.

Multisequence MRI-based radiomics signature as potential biomarkers for differentiating KRAS mutations in non-small cell lung cancer with brain metastases

BackgroundKirsten rat sarcoma virus (KRAS) has evolved from a genotype with predictive value to a therapeutic target recently. The study aimed to establish non-invasive radiomics models based on MRI to discriminate KRAS from epidermal growth factor receptor (EGFR) or anaplastic lymphoma kinase (ALK) mutations in lung cancer patients with brain metastases (BM), then further explore the optimal sequence for prediction. MethodsThis retrospective study involved 317 patients (218 patients in training cohort and 99 patients in testing cohort) who had confirmed of KRAS, EGFR or ALK mutations. Radiomics features were separately extracted from T2WI, T2 fluid-attenuated inversion recovery (T2-FLAIR), diffusion weighted imaging (DWI) and contrast-enhanced T1-weighted imaging (T1-CE) sequences. The maximal information coefficient and recursive feature elimination method were used to select informative features. Then we built four radiomics models for differentiating KRAS from EGFR or ALK using random forest classifier. ROC curves were used to validate the capability of the models. ResultsThe four radiomics models for discriminating KRAS from EGFR all worked well, especially DWI and T2WI models (AUCs: 0.942, 0.942 in training cohort, 0.949, 0.954 in testing cohort). When KRAS compared to ALK, DWI and T2-FLAIR models showed excellent performance in two cohorts (AUCs: 0.947, 0.917 in training cohort, 0.850, 0.824 in testing cohort). ConclusionsRadiomics classifiers integrating MRI have potential to discriminate KRAS from EGFR or ALK, which are helpful to guide treatment and facilitate the discovery of new approaches capable of achieving this long-sought goal of cure in lung cancer patients with KRAS.

A Case Report in the Management of Dysphagia in Osmotic Demyelination Syndrome Using Mann Assessment of Swallowing Ability (MASA).

Osmotic Demyelination Syndrome (ODS) is one of the primary cause of demyelination in alcoholics and malnourished resulting in various kind of clinical manifestations in pontine symptoms, neuro-behavioural symptoms movement disorders as well as speech and language and swallowing difficulties. The study was done to introspect the prognosis of swallowing therapy in a patient with ODS using MASA (Mann Assessment of Swallowing Ability). A 36years old male with a history of regular alcohol intake and hypertensiom reported in our healthcare centre with hypoaklemia and speech, language and swallowing difficulty. Magnetic resonance imaging of the brain showed bilateral symmetrical T2 and T2 FLAIR (fluid attenuated inversion recovery) hyperintensity lesion in the bilateral basal ganglia involving the caudate nuclei, putaminal region and bilateral thalami with similar lesion along the mid brain. There were no areas of acute restriction in diffusion study. The findings suggested of Osmotic Demyelination Syndrome (ODS).When accessed with N-DAT, WAB and MASA; patient was diagnosed with Spastic Dysarthria, Transcortical Motor Aphasia and Moderate Dysphagia. Intervention was provided using speech and swallowing therapy and when introspected with MASA scores, improvement was seen within 5days and statistically 97% of variance was seen inferring the progressing trend in the MASA scores. This study concludes that MASA can be an effective tool in introspecting the prognosis of Dysphagia in ODS and early intervention in the management of dysphasia shows positive results.

Day one postoperative MRI findings following electrode placement for deep brain stimulation: analysis of a large case series.

This study sought to characterize postoperative day one MRI findings in deep brain stimulation (DBS) patients. DBS patients were identified by CPT and had their reviewed by a trained neuroradiologist and neurosurgeon blinded to MR sequence and patient information. The radiographic abnormalities of interest were track microhemorrhage, pneumocephalus, hematomas, and edema, and the occurrence of these findings in compare the detection of these complications between T1/T2 gradient-echo (GRE) and T1/T2 fluid-attenuated inversion recovery (FLAIR) magnetic resonance (MR) sequences was compared. The presence, size, and association of susceptibility artifact with other radiographic abnormalities was also described. Lastly, the association of multiple microelectrode cannula passes with each radiographic finding was evaluated. Ad-hoc investigation evaluated hemisphere-specific associations. Multiple logistic regression with Bonferroni correction (corrected p = 0.006) was used for all analysis. Out of 198 DBS patients reviewed, 115 (58%) patients showed entry microhemorrhage; 77 (39%) track microhemorrhage; 44 (22%) edema; 69 (35%) pneumocephalus; and 12 (6%) intracranial hematoma. T2 GRE was better for detecting microhemorrhage (OR = 14.82, p < 0.0001 for entry site and OR = 4.03, p < 0.0001 for track) and pneumocephalus (OR = 11.86, p < 0.0001), while T2 FLAIR was better at detecting edema (OR = 123.6, p < 0.0001). The relatively common findings of microhemorrhage and edema were best visualized by T2 GRE and T2 FLAIR sequences, respectively. More passes intraoperatively was associated with detection of ipsilateral track microhemorrhage (OR = 7.151, p < 0.0001 left; OR = 8.953, p < 0.0001 right). Susceptibility artifact surrounding electrodes possibly interfered with further detection of ipsilateral edema (OR = 4.323, p = 0.0025 left hemisphere only). Day one postoperative magnetic resonance imaging (MRI) for DBS patients can be used to detect numerous radiographic abnormalities not identifiable on a computed tomographic (CT) scan. For this cohort, multiple stimulating cannula passes intraoperatively was associated with increased microhemorrhage along the electrode track. Further studies should be performed to evaluate the clinical relevance of these observations.

Association between impaired dynamic cerebral autoregulation and BBB disruption in reversible cerebral vasoconstriction syndrome

BackgroundHalf of the sufferers of reversible cerebral vasoconstriction syndrome (RCVS) exhibit imaging-proven blood-brain barrier disruption. The pathogenesis of blood-brain barrier disruption in RCVS remains unclear and mechanism-specific intervention is lacking. We speculated that cerebrovascular dysregulation might be associated with blood-brain barrier disruption in RCVS. Hence, we aimed to evaluate whether the dynamic cerebral autoregulation is altered in patients with RCVS and could be associated with blood-brain barrier disruption.MethodsA cross-sectional study was conducted from 2019 to 2021 at headache clinics of a national tertiary medical center. Dynamic cerebral autoregulation was evaluated in all participants. The capacity of the dynamic cerebral autoregulation to damp the systemic hemodynamic changes, i.e., phase shift and gain between the cerebral blood flow and blood pressure waveforms in the very-low- and low-frequency bands were calculated by transfer function analysis. The mean flow correlation index was also calculated. Patients with RCVS received 3-dimensional isotropic contrast-enhanced T2 fluid-attenuated inversion recovery imaging to visualize blood-brain barrier disruption.ResultsForty-five patients with RCVS (41.9 ± 9.8 years old, 29 females) and 45 matched healthy controls (41.4 ± 12.5 years old, 29 females) completed the study. Nineteen of the patients had blood-brain barrier disruption. Compared to healthy controls, patients with RCVS had poorer dynamic cerebral autoregulation, indicated by higher gain in very-low-frequency band (left: 1.6 ± 0.7, p = 0.001; right: 1.5 ± 0.7, p = 0.003; healthy controls: 1.1 ± 0.4) and higher mean flow correlation index (left: 0.39 ± 0.20, p = 0.040; right: 0.40 ± 0.18, p = 0.017; healthy controls: 0.31 ± 0.17). Moreover, patients with RCVS with blood-brain barrier disruption had worse dynamic cerebral autoregulation, as compared to those without blood-brain barrier disruption, by having less phase shift in very-low- and low-frequency bands, and higher mean flow correlation index.ConclusionsDysfunctional dynamic cerebral autoregulation was observed in patients with RCVS, particularly in those with blood-brain barrier disruption. These findings suggest that impaired cerebral autoregulation plays a pivotal role in RCVS pathophysiology and may be relevant to complications associated with blood-brain barrier disruption by impaired capacity of maintaining stable cerebral blood flow under fluctuating blood pressure.Graphical

Meningioma consistency assessment based on the fusion of deep learning features and radiomics features

PurposeThis study aims to combine deep learning features with radiomics features for the computer-assisted preoperative assessment of meningioma consistency. Methods202 patients with surgery and pathological diagnosis of meningiomas at our institution between December 2016 and December 2018 were retrospectively included in the study. The T2-fluid attenuated inversion recovery (T2-Flair) images were evaluated to classify meningioma as soft or hard by professional neurosurgeons based on Zada’s consistency grading system. All the patients were split randomly into a training cohort (n = 162) and a testing cohort (n = 40). A convolutional neural network (CNN) model was proposed to extract deep learning features. These deep learning features were combined with radiomics features. After multiple feature selections, selected features were used to construct classification models using four classifiers. AUC was used to evaluate the performance of each classifier. A signature was further constructed by using the least absolute shrinkage and selection operator (LASSO). A nomogram based on the signature was created for predicting meningioma consistency. ResultsThe logistic regression classifier constructed using 17 radiomics features and 9 deep learning features provided the best performance with a precision of 0.855, a recall of 0.854, an F1-score of 0.852 and an AUC of 0.943 (95 % CI, 0.873–1.000) in the testing cohort. The C-index of the nomogram was 0.822 (95 % CI, 0.758–0.885) in the training cohort and 0.943 (95 % CI, 0.873–1.000) in the testing cohort with good calibration. Decision curve analysis further confirmed the clinical usefulness of the nomogram for predicting meningioma consistency. ConclusionsThe proposed method for assessing meningioma consistency based on the fusion of deep learning features and radiomics features is potentially clinically valuable. It can be used to assist physicians in the preoperative determination of tumor consistency.

A Prediction Model for Deciphering Intratumoral Heterogeneity Derived from the Microglia/Macrophages of Glioma Using Non-Invasive Radiogenomics.

Microglia and macrophages play a major role in glioma immune responses within the glioma microenvironment. We aimed to construct a prognostic prediction model for glioma based on microglia/macrophage-correlated genes. Additionally, we sought to develop a non-invasive radiogenomics approach for risk stratification evaluation. Microglia/macrophage-correlated genes were identified from four single-cell datasets. Hub genes were selected via lasso-Cox regression, and risk scores were calculated. The immunological characteristics of different risk stratifications were assessed, and radiomics models were constructed using corresponding MRI imaging to predict risk stratification. We identified eight hub genes and developed a relevant risk score formula. The risk score emerged as a significant prognostic predictor correlated with immune checkpoints, and a relevant nomogram was drawn. High-risk groups displayed an active microenvironment associated with microglia/macrophages. Furthermore, differences in somatic mutation rates, such as IDH1 missense variant and TP53 missense variant, were observed between high- and low-risk groups. Lastly, a radiogenomics model utilizing five features from magnetic resonance imaging (MRI) T2 fluid-attenuated inversion recovery (Flair) effectively predicted the risk groups under a random forest model. Our findings demonstrate that risk stratification based on microglia/macrophages can effectively predict prognosis and immune functions in glioma. Moreover, we have shown that risk stratification can be non-invasively predicted using an MRI-T2 Flair-based radiogenomics model.

Multisequence MRI-based radiomics analysis for early prediction of the risk of T790M resistance in new brain metastases.

Predicting whether T790M emerges early is crucial to the adjustment of targeted drugs for non-small cell lung cancer (NSCLC) patients. This study aimed to evaluate the risk of T790M resistance in progressive new brain metastases (BMs) based on multisequence magnetic resonance imaging (MRI) radiomics. This retrospective study included 405 consecutive patients (training cohort: 294 patients; testing cohort: 111 patients) with proven NSCLC with disease progression of new BM. The radiomics features were separately extracted from T2-weighted imaging (T2WI), T2 fluid-attenuated inversion recovery (T2-FLAIR), diffusion-weighted imaging (DWI), and contrast-enhanced T1-weighted imaging (T1-CE) sequence of baseline MRI. Then, we calculated radiomics scores (rad-score) of the 4 sequences respectively and established predictive models (lesion- or patient-level) to evaluate T790M resistance within up to 14 months using random forest classifier. Receiver operating characteristic (ROC) curves and F1 scores were used to validate the performance of two models in both the training and testing cohort. There were significant differences in rad-scores of the four sequences between T790M-positive and negative groups whether in the training or testing cohort (P<0.05). The lesion-level model consisting of rad-scores showed excellent discrimination, with an area under the curve (AUC) and F1-score of 0.879 and 0.798 in the training cohort, and 0.834 and 0.742 in the testing cohort, respectively. The patient-level model also showed a favorable discriminatory ability with an AUC and F1 score of 0.851 and 0.837, which was confirmed with an AUC and F1 score of 0.734 and 0.716 in the testing cohort. The MRI-based radiomics signatures may be new markers to identify patients at high risk of developing resistance in the early period.

MRI motion artifact reduction using a conditional diffusion probabilistic model (MAR-CDPM).

High-resolution magnetic resonance imaging (MRI) with excellent soft-tissue contrast is a valuable tool utilized for diagnosis and prognosis. However, MRI sequences with long acquisition time are susceptible to motion artifacts, which can adversely affect the accuracy of post-processingalgorithms. This study proposes a novel retrospective motion correction method named "motion artifact reduction using conditional diffusion probabilistic model" (MAR-CDPM). The MAR-CDPM aimed to remove motion artifacts from multicenter three-dimensional contrast-enhanced T1 magnetization-prepared rapid acquisition gradient echo (3D ceT1 MPRAGE) brain dataset with different brain tumor types. This study employed two publicly accessible MRI datasets: one containing 3D ceT1 MPRAGE and 2D T2-fluid attenuated inversion recovery (FLAIR) images from 230 patients with diverse brain tumors, and the other comprising 3D T1-weighted (T1W) MRI images of 148 healthy volunteers, which included real motion artifacts. The former was used to train and evaluate the model using the in silico data, and the latter was used to evaluate the model performance to remove real motion artifacts. A motion simulation was performed in k-space domain to generate an in silico dataset with minor, moderate, and heavy distortion levels. The diffusion process of the MAR-CDPM was then implemented in k-space to convert structure data into Gaussian noise by gradually increasing motion artifact levels. A conditional network with a Unet backbone was trained to reverse the diffusion process to convert the distorted images to structured data. The MAR-CDPM was trained in two scenarios: one conditioning on the time step of the diffusion process, and the other conditioning on both and T2-FLAIR images. The MAR-CDPM was quantitatively and qualitatively compared with supervised Unet, Unet conditioned on T2-FLAIR, CycleGAN, Pix2pix, and Pix2pix conditioned on T2-FLAIR models. To quantify the spatial distortions and the level of remaining motion artifacts after applying the models, quantitative metrics were reported including normalized mean squared error (NMSE), structural similarity index (SSIM), multiscale structural similarity index (MS-SSIM), peak signal-to-noise ratio (PSNR), visual information fidelity (VIF), and multiscale gradient magnitude similarity deviation (MS-GMSD). Tukey's Honestly Significant Difference multiple comparison test was employed to quantify the difference between the models where p-value was considered statisticallysignificant. Qualitatively, MAR-CDPM outperformed these methods in preserving soft-tissue contrast and different brain regions. It also successfully preserved tumor boundaries for heavy motion artifacts, like the supervised method. Our MAR-CDPM recovered motion-free in silico images with the highest PSNR and VIF for all distortion levels where the differences were statistically significant (p-values ). In addition, our method conditioned on t and T2-FLAIR outperformed (p-values ) other methods to remove motion artifacts from the in silico dataset in terms of NMSE, MS-SSIM, SSIM, and MS-GMSD. Moreover, our method conditioned on only t outperformed generative models (p-values ) and had comparable performances compared with the supervised model (p-values ) to remove real motionartifacts. The MAR-CDPM could successfully remove motion artifacts from 3D ceT1 MPRAGE. It is particularly beneficial for elderly who may experience involuntary movements during high-resolution MRI imaging with long acquisitiontimes.

The T2-FLAIR mismatch sign in oncologic neuroradiology: History, current use, emerging data, and future directions.

The T2-Fluid-Attenuated Inversion Recovery (T2-FLAIR) mismatch sign is a radiogenomic marker that is easily discernible on preoperative conventional MR imaging. Application of strict criteria (adult population, cerebral hemisphere location, and classic imaging morphology) permits the noninvasive preoperative diagnosis of isocitrate dehydrogenase (IDH)-mutant 1p/19q-non-codeleted diffuse astrocytoma with near-perfect specificity, albeit with variably low sensitivity. This leads to improved preoperative planning and patient counseling. More recent research has shown that the application of less strict criteria compromises the near-perfect specificity of the sign but remains adequate for ruling out IDH-wildtype (glioblastoma) phenotype, which bears a far grimmer prognosis compared to IDH-mutant diffuse astrocytic disease. In this review, we elaborate on the various definitions of the T2-FLAIR mismatch sign present in the literature, illustrate these with images obtained at a comprehensive cancer center, discuss the potential of the mismatch sign for application to certain pediatric-type brain tumors, namely dysembryoplastic neuroepithelial tumor and diffuse midline glioma, and elaborate upon the clinical, histologic, and molecular associations of the T2-FLAIR mismatch sign as recognized to date. Finally, the sign's correlates in diffusion- and perfusion-weighted imaging are presented, and opportunities to further maximize the diagnostic and prognostic applications of the sign in the context of the 2021 revision of the WHO Classification of Central Nervous System Tumors are discussed.