Fluid-attenuated Inversion Recovery Research Articles

An exploratory prospective phase II study of preoperative neoadjuvant bevacizumab and temozolomide for newly diagnosed glioblastoma

PurposeThis multi-institutional phase I/II study was conducted to confirm the safety and explore the clinical utility of preoperative Bevacizumab (Bev) for newly diagnosed glioblastoma (GB).MethodsPatients were enrolled based on magnetic resonance imaging (MRI) findings typically suggestive of GB. Preoperative Bev and temozolomide (TMZ) were administered at doses of 10 mg/kg on day 0 and 150 mg/m2 on days 1–5, respectively. Surgical resection was performed between days 21 and 30, inclusive. The safety and efficacy were evaluated in a total of 15 cases by progression-free survival (PFS), changes in tumor volume, Karnofsky Performance Scale (KPS) and Mini-Mental State Examination (MMSE) scores after preoperative therapy.ResultsTumor resection was performed on a mean of day 23.7. Pathological diagnosis was GB, isocitrate dehydrogenase (IDH)-wildtype in 14 cases and GB, IDH-mutant in 1 case. Severe adverse events possibly related to preoperative Bev and TMZ were observed in 2 of the 15 patients, as wound infection and postoperative hematoma and thrombocytopenia. KPS and MMSE scores were significantly improved with preoperative therapy. Tumor volume was decreased in all but one case on T1-weighted imaging with contrast-enhancement (T1CE) and in all cases on fluid-attenuated inversion recovery, with mean volume decrease rates of 36.2% and 54.0%, respectively. Median PFS and overall survival were 9.5 months and 16.5 months, respectively.ConclusionPreoperative Bev and TMZ is safe as long as the instructions are followed. The strategy might be useful for GB in some patients, not only reducing tumor burden, but also improving patient KPS preoperatively.Trial Registration Number: UMIN000025579, jRCT1031180233 https://jrct.niph.go.jp/latest-detail/jRCT1031180233. Registration Date: Jan. 16, 2017

FLAIR signal intensity ratio predicts small subcortical infarct early neurologic deterioration: a cross-sectional study.

Prior studies have used the fluid-attenuated inversion recovery sequence signal intensity ratio (FLAIR-SIR) to predict those with an incomplete infarct that may safely receive acute thrombolytics. Clinical early neurologic deterioration (END) of small subcortical infarcts (SSIs) is suspected to occur due to delayed infarct completion. We aimed to understand if a lower FLAIR-SIR, suggestive of an incomplete infarct, would have a higher likelihood of SSI-related END. A cross-sectional retrospective study was performed of those with an acute SSI (anterior or posterior circulation) without significant parent vessel steno-occlusive disease. END was defined as a new or worsened disabling neurologic deficit during the index hospitalization. Standard-of-care brain MRIs were reviewed from the hospitalization, and a FLAIR-SIR cutoff of ≤ 1.15 was used based on prior studies. Adjusted logistic regression models were used for analysis. We identified 252 patients meeting inclusion criteria: median (IQR) age 68 (12) years, 38.5% (97/252) female, and 11% (28/252) with END. Tobacco use was more common in those without END (32%) compared with END (55%, p = 0.03). In adjusted analyses, a FLAIR-SIR cutoff of ≤ 1.15 yielded an odds ratio of 2.8 (95% CI 1.23-6.13, p = 0.012) of early neurological deterioration. Those with a FLAIR-SIR ≤ 1.15 are nearly threefold more likely to develop SSI-related END.

Evaluation of the quality and the productivity of neuroradiological reading of multiple sclerosis follow-up MRI scans using an intelligent automation software

PurposeThe assessment of multiple sclerosis (MS) lesions on follow-up magnetic resonance imaging (MRI) is tedious, time-consuming, and error-prone. Automation of low-level tasks could enhance the radiologist in this work. We evaluate the intelligent automation software Jazz in a blinded three centers study, for the assessment of new, slowly expanding, and contrast-enhancing MS lesions.MethodsIn three separate centers, 117 MS follow-up MRIs were blindly analyzed on fluid attenuated inversion recovery (FLAIR), pre- and post-gadolinium T1-weighted images using Jazz by 2 neuroradiologists in each center. The reading time was recorded. The ground truth was defined in a second reading by side-by-side comparison of both reports from Jazz and the standard clinical report. The number of described new, slowly expanding, and contrast-enhancing lesions described with Jazz was compared to the lesions described in the standard clinical report.ResultsA total of 96 new lesions from 41 patients and 162 slowly expanding lesions (SELs) from 61 patients were described in the ground truth reading. A significantly larger number of new lesions were described using Jazz compared to the standard clinical report (63 versus 24). No SELs were reported in the standard clinical report, while 95 SELs were reported on average using Jazz. A total of 4 new contrast-enhancing lesions were found in all reports. The reading with Jazz was very time efficient, taking on average 2min33s ± 1min0s per case. Overall inter-reader agreement for new lesions between the readers using Jazz was moderate for new lesions (Cohen kappa = 0.5) and slight for SELs (0.08).ConclusionThe quality and the productivity of neuroradiological reading of MS follow-up MRI scans can be significantly improved using the dedicated software Jazz.

Association of Vascular Risk Factors and CSF and Imaging Biomarkers With White Matter Hyperintensities in Former American Football Players.

Recent data link exposure to repetitive head impacts (RHIs) from American football with increased white matter hyperintensity (WMH) burden. WMH might have unique characteristics in the context of RHI beyond vascular risk and normal aging processes. We evaluated biological correlates of WMH in former American football players, including markers of amyloid, tau, inflammation, axonal injury, neurodegeneration, and vascular health. Participants underwent clinical interviews, MRI, and lumbar puncture as part of the Diagnostics, Imaging, and Genetics Network for the Objective Study and Evaluation of Chronic Traumatic Encephalopathy Research Project. Structural equation modeling tested direct and indirect effects between log-transformed total fluid-attenuated inversion recovery (FLAIR) lesion volumes (TLV) and the revised Framingham stroke risk profile (rFSRP), MRI-derived global metrics of cortical thickness and fractional anisotropy (FA), and CSF levels of amyloid β1-42, p-tau181, soluble triggering receptor expressed on myeloid cells 2 (sTREM2), and neurofilament light. Covariates included age, race, education, body mass index, APOE ε4 carrier status, and evaluation site. Models were performed separately for former football players and a control group of asymptomatic men unexposed to RHI. In 180 former football players (mean age = 57.2, 36% Black), higher log(TLV) had direct associations with the following: higher rFSRP score (B = 0.26, 95% CI 0.07-0.40), higher p-tau181 (B = 0.17, 95% CI 0.01-0.43), lower FA (B = -0.28, 95% CI -0.42 to -0.13), and reduced cortical thickness (B = -0.25, 95% CI -0.45 to -0.08). In 60 asymptomatic unexposed men (mean age = 59.3, 40% Black), there were no direct effects on log(TLV) (rFSRP: B = -0.03, 95% CI -0.48 to 0.57; p-tau181: B = -0.30, 95% CI -1.14 to 0.37; FA: B = -0.07, 95% CI -0.48 to 0.42; or cortical thickness: B = -0.28, 95% CI -0.64 to 0.10). The former football players showed stronger associations between log(TLV) and rFSRP (1,069% difference in estimates), p-tau181 (158%), and FA (287%) than the unexposed men. Risk factors and biological correlates of WMH differed between former American football players and asymptomatic unexposed men. In addition to vascular health, p-tau181 and diffusion tensor imaging indices of white matter integrity showed stronger associations with WMH in the former football players. FLAIR WMH may have specific risk factors and pathologic underpinnings in RHI-exposed individuals.

Association between Brain White Matter Lesions and Disease Activity in HAM/TSP Patients.

Human T-cell leukemia virus type 1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) patients may have brain white matter (WM) lesions, but the association of these lesions with disease activity is poorly understood. We retrospectively evaluated the brain WM lesions of 22 HAM/TSP patients (male 4: female 18) including 5 rapid progressors, 16 slow progressors, and 1 very slow progressor. The severity of WM brain lesions on axial Fluid Attenuated Inversion Recovery images was evaluated utilizing the Fazekas scale, cerebrospinal fluid biomarkers, and proviral load in peripheral blood mononuclear cells. Imaging and biological data were compared at the first visit and a subsequent visit more than 4 years later. Patients with comorbidities including adult T-cell leukemia-lymphoma and cerebrovascular disease were excluded. The results revealed that brain WM lesions in the rapid progressors group were more pronounced than those in slow progressors. In patients with HAM/TSP, severe and persistent inflammation of the spinal cord may cause brain WM lesions.

Generative AI in glioma: Ensuring diversity in training image phenotypes to improve diagnostic performance for IDH mutation prediction.

This study evaluated whether generative artificial intelligence (AI)-based augmentation (GAA) can provide diverse and realistic imaging phenotypes and improve deep learning-based classification of isocitrate dehydrogenase (IDH) type in glioma compared with neuroradiologists. For model development, 565 patients (346 IDH-wildtype, 219 IDH-mutant) with paired contrast-enhanced T1 and FLAIR MRI scans were collected from tertiary hospitals and The Cancer Imaging Archive. Performance was tested on internal (119, 78 IDH-wildtype, 41 IDH-mutant [IDH1 and 2]) and external test sets (108, 72 IDH-wildtype, 36 IDH-mutant). GAA was developed using a score-based diffusion model and ResNet50 classifier. The optimal GAA was selected in comparison with the null model. Two neuroradiologists (R1, R2) assessed realism, diversity of imaging phenotypes, and predicted IDH mutation. The performance of a classifier trained with optimal GAA was compared with that of neuroradiologists using the area under the receiver operating characteristics curve (AUC). The effect of tumor size and contrast enhancement on GAA performance was tested. Generated images demonstrated realism (Turing's test: 47.5-50.5%) and diversity indicating IDH type. Optimal GAA was achieved with augmentation with 110 000 generated slices (AUC: 0.938). The classifier trained with optimal GAA demonstrated significantly higher AUC values than neuroradiologists in both the internal (R1, P = .003; R2, P < .001) and external test sets (R1, P < .01; R2, P < .001). GAA with large-sized tumors or predominant enhancement showed comparable performance to optimal GAA (internal test: AUC 0.956 and 0.922; external test: 0.810 and 0.749). The application of generative AI with realistic and diverse images provided better diagnostic performance than neuroradiologists for predicting IDH type in glioma.

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.

A multi-modal feature fusion classification model based on distance matching and discriminative representation learning for differentiation of high-grade glioma from solitary brain metastasis

To explore the performance of a new multimodal feature fusion classification model based on distance matching and discriminative representation learning for differentiating high-grade glioma (HGG) from solitary brain metastasis (SBM). We collected multi-parametric magnetic resonance imaging (MRI) data from 61 patients with HGG and 60 with SBM, and delineated regions of interest (ROI) on T1WI, T2WI, T2-weighted fluid attenuated inversion recovery (T2_FLAIR) and post-contrast enhancement T1WI (CE_T1WI) images. The radiomics features were extracted from each sequence using Pyradiomics and fused using a multimodal feature fusion classification model based on distance matching and discriminative representation learning to obtain a classification model. The discriminative performance of the classification model for differentiating HGG from SBM was evaluated using five-fold cross-validation with metrics of specificity, sensitivity, accuracy, and the area under the ROC curve (AUC) and quantitatively compared with other feature fusion models. Visual experiments were conducted to examine the fused features obtained by the proposed model to validate its feasibility and effectiveness. The five-fold cross-validation results showed that the proposed multimodal feature fusion classification model had a specificity of 0.871, a sensitivity of 0.817, an accuracy of 0.843, and an AUC of 0.930 for distinguishing HGG from SBM. This feature fusion method exhibited excellent discriminative performance in the visual experiments. The proposed multimodal feature fusion classification model has an excellent ability for differentiating HGG from SBM with significant advantages over other feature fusion classification models in discrimination and classification tasks between HGG and SBM.

Implications of Inflammatory Processes on a Developing Central Nervous System in Childhood-Onset Systemic Lupus Erythematosus.

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that is increasingly affecting pediatric and adult populations. Neuropsychiatric manifestations (ie, cognitive dysfunction and mood disorders) appear to occur with greater severity and poorer prognosis in childhood-onset SLE (cSLE) versus adult-onset SLE, negatively impacting school function, self-management, and psychosocial health, as well as lifelong health-related quality of life. In this review, we describe pathogenic mechanisms active in cSLE, such as maladaptive inflammatory processes and ischemia, which are hypothesized to underpin central phenotypes in patients with cSLE, and the role of alterations in protective central nervous system (CNS) barriers (ie, the blood-brain barrier) are also discussed. Recent findings derived from novel neuroimaging approaches are highlighted because the methods employed in these studies hold potential for identifying CNS abnormalities that would otherwise remain undetected with conventional multiple resonance imaging studies (eg, T2-weighted or fluid-attenuated inversion recovery sequences). Furthermore, we propose that a more robust presentation of neuropsychiatric symptoms in cSLE is in part due to the harmful impact of a chronic inflammatory insult on a developing CNS. Although the immature status of the CNS may leave patients with cSLE more vulnerable to harboring neuropsychiatric manifestations, the same property may represent a greater urgency to reverse the maladaptive effects associated with a proneuroinflammatory state, provided that effective diagnostic tools and treatment strategies are available. Finally, considering the crosstalk among the CNS and other organ systems affected in cSLE, we postulate that a finer understanding of this interconnectivity and its role in the clinical presentation in cSLE is warranted.

Three-dimensional heavily T2-weighted FLAIR in the detection of blood-labyrinthine barrier leakage in patients with sudden sensorineural hearing loss: comparison with T1 sequences and application of deep learning-based reconstruction.

Blood-labyrinthine barrier leakage has been reported in sudden sensorineural hearing loss (SSNHL). We compared immediate post-contrast 3D heavily T2-weighted fluid-attenuated inversion recovery (FLAIR), T1 spin echo (SE), and 3D T1 gradient echo (GRE) sequences, and heavily T2-weighted FLAIR (hvT2F) with and without deep learning-based reconstruction (DLR) in detecting perilymphatic enhancement. Fifty-four patients with unilateral SSNHL who underwent ear MRI with three sequences were included. We compared asymmetry scores, confidence scores, and detection rates of perilymphatic enhancement among the three sequences and obtained 3D hvT2F with DLR from 35 patients. The above parameters and subjective image quality between 3D hvT2F with and without DLR were compared. Asymmetry scores and detection rate of 3D hvT2F were significantly higher than 3D GRE T1 and SE T1 (respectively, 1.37, 0.11, 0.19; p < 0.001). Asymmetry scores significantly increased with DLR compared to 3D hvT2F for experienced and inexperienced readers (respectively, 1.77 vs. 1.40, p = 0.036; 1.49 vs. 1.03, p = 0.012). The detection rate significantly increased only for the latter (57.1% vs. 31.4%, p = 0.022). Patients with perilymphatic enhancement had significantly higher air conduction thresholds on initial (77.96 vs. 57.79, p = 0.002) and 5days after presentation (63.38 vs. 41.85, p = 0.019). 3D hvT2F significantly increased the detectability of perilymphatic enhancement compared to 3D GRE T1 and SE T1. DLR further improved the conspicuity of perilymphatic enhancement in 3D hvT2F. 3D hvT2F and DLR are useful for evaluating blood-labyrinthine barrier leakage; furthermore, they might provide prognostic value in the early post-treatment period. Ten-minute post-contrast 3D heavily T2-weighed FLAIR imaging is a potentially efficacious sequence in demonstrating perilymphatic enhancement in patients with sudden sensorineural hearing loss and may be further improved by deep learning-based reconstruction. • 3D heavily T2-weighted FLAIR (3D hvT2F) is a sequence sensitive in detecting low concentrations of contrast in the perilymphatic space. • 3D hvT2F sequences properly demonstrated perilymphatic enhancement in sudden sensorineural hearing loss compared to T1 sequences and were further improved by deep learning-based reconstruction (DLR). • 3D hvT2F and DLR are efficacious sequences in detecting blood-labyrinthine barrier leakage and with potential prognostic information.

Magnetic resonance imaging in acute meningoencephalitis of viral and unknown origin: frequent findings and prognostic potential.

Magnetic resonance imaging (MRI) findings in meningoencephalitis have mainly been described in terms of their diagnostic value rather than their prognostic potential, except for herpes simplex virus (HSV) encephalitis. The aims of our study were to describe frequency and anatomic locations of MRI abnormalities specific to limbic, circadian and motor systems in a cohort of meningoencephalitis patients, as well as to investigate the prognostic value of these MRI findings. A secondary, selective analysis of a retrospective database including all meningitis, meningoencephalitis and encephalitis cases treated between 2016 and 2018 in the University hospital of Bern, Switzerland was performed. Patients with meningitis of any cause, bacterial or autoimmune causes of encephalitis were excluded. MRI scans and clinical data from 129 meningoencephalitis cases found that the most frequent causes were tick-borne encephalitis (TBE, 42%), unknown pathogens (40%), VZV (7%), and HSV1 (5%). At discharge, median modified Rankin Score (mRS) was 3 (interquartile range, IQR, 1), 88% of patients had persisting signs and symptoms. After a median of 17 months, median Glasgow Outcome Score (GOS) was 5 (IQR 1), 39% of patients still had residual signs or symptoms. All patients with HSV, 27% with TBE and 31% of those with meningoencephalitis of unknown etiology had fluid-attenuated inversion recovery (FLAIR) and to a lesser extent diffusion-weighted imaging (DWI) lesions in their initial MRI, with highly overlapping anatomical distribution. In one fifth of TBE patients the limbic system was affected. Worse outcome was associated with presence of DWI and/or FLAIR lesions and lower normalized apparent diffusion coefficient (ADC) signal intensities. Presence of FLAIR lesions, restricted diffusion as well as the extent of ADC hypointensity in initial MRI are parameters which might be of prognostic value regarding the longterm clinical outcome for patients with meningoencephalitis of viral and of unknown origin. Although not described before, affection of limbic structures by TBE is possible as shown by our results: A substantial proportion of our TBE patients had FLAIR signal abnormalities in these regions.

Spatial normalization for voxel-based lesion symptom mapping: impact of registration approaches.

Voxel-based lesion symptom mapping (VLSM) assesses the relation of lesion location at a voxel level with a specific clinical or functional outcome measure at a population level. Spatial normalization, that is, mapping the patient images into an atlas coordinate system, is an essential pre-processing step of VLSM. However, no consensus exists on the optimal registration approach to compute the transformation nor are downstream effects on VLSM statistics explored. In this work, we evaluate four registration approaches commonly used in VLSM pipelines: affine (AR), nonlinear (NLR), nonlinear with cost function masking (CFM), and enantiomorphic registration (ENR). The evaluation is based on a standard VLSM scenario: the analysis of statistical relations of brain voxels and regions in imaging data acquired early after stroke onset with follow-up modified Rankin Scale (mRS) values. Fluid-attenuated inversion recovery (FLAIR) MRI data from 122 acute ischemic stroke patients acquired between 2 and 3 days after stroke onset and corresponding lesion segmentations, and 30 days mRS values from a European multicenter stroke imaging study (I-KNOW) were available and used in this study. The relation of the voxel location with follow-up mRS was assessed by uni- as well as multi-variate statistical testing based on the lesion segmentations registered using the four different methods (AR, NLR, CFM, ENR; implementation based on the ANTs toolkit). The brain areas evaluated as important for follow-up mRS were largely consistent across the registration approaches. However, NLR, CFM, and ENR led to distortions in the patient images after the corresponding nonlinear transformations were applied. In addition, local structures (for instance the lateral ventricles) and adjacent brain areas remained insufficiently aligned with corresponding atlas structures even after nonlinear registration. For VLSM study designs and imaging data similar to the present work, an additional benefit of nonlinear registration variants for spatial normalization seems questionable. Related distortions in the normalized images lead to uncertainties in the VLSM analyses and may offset the theoretical benefits of nonlinear registration.

Noninvasive Isocitrate Dehydrogenase 1 Status Prediction in Grade II/III Glioma Based on Magnetic Resonance Images: A Transfer Learning Strategy.

The aim of this study was to evaluate transfer learning combined with various convolutional neural networks (TL-CNNs) in predicting isocitrate dehydrogenase 1 ( IDH1 ) status of grade II/III gliomas. Grade II/III glioma patients diagnosed at the Tangdu Hospital (August 2009 to May 2017) were retrospectively enrolled, including 54 patients with IDH1 mutant and 56 patients with wild-type IDH1 . Convolutional neural networks, AlexNet, GoogLeNet, ResNet, and VGGNet were fine-tuned with T2-weighted imaging (T2WI), fluid attenuation inversion recovery (FLAIR), and contrast-enhanced T1-weighted imaging (T1CE) images. The single-modal networks were integrated with averaged sigmoid probabilities, logistic regression, and support vector machine. FLAIR-T1CE-fusion (FC-fusion), T2WI-T1CE-fusion (TC-fusion), and FLAIR-T2WI-T1CE-fusion (FTC-fusion) were used for fine-tuning TL-CNNs. IDH1 -mutant prediction accuracies using AlexNet, GoogLeNet, ResNet, and VGGNet achieved 70.0% (AUC = 0.660), 65.0% (AUC = 0.600), 70.0% (AUC = 0.700), and 80.0% (AUC = 0.730) for T2WI images, 70.0% (AUC = 0.660), 70.0% (AUC = 0.620), 70.0% (AUC = 0.710), and 80.0% (AUC = 0.720) for FLAIR images, and 73.7% (AUC = 0.744), 73.7% (AUC = 0.656), 73.7% (AUC = 0.633), and 73.7% (AUC = 0.700) for T1CE images, respectively. The highest AUC (0.800) was achieved using VGGNet and FC-fusion images. TL-CNNs (especially VGGNet) had a potential predictive value for IDH1 -mutant status of grade II/III gliomas.

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.

End-to-end volumetric segmentation of white matter hyperintensities using deep learning

Background and objectivesReliable detection of white matter hyperintensities (WMH) is crucial for studying the impact of diffuse white-matter pathology on brain health and monitoring changes in WMH load over time. However, manual annotation of 3D high-dimensional neuroimages is laborious and can be prone to biases and errors in the annotation procedure. In this study, we evaluate the performance of deep learning (DL) segmentation tools and propose a novel volumetric segmentation model incorporating self-attention via a transformer-based architecture. Ultimately, we aim to evaluate diverse factors that influence WMH segmentation, aiming for a comprehensive analysis of the state-of-the-art algorithms in a broader context. MethodsWe trained state-of-the-art DL algorithms, and incorporated advanced attention mechanisms, using structural fluid-attenuated inversion recovery (FLAIR) image acquisitions. The anatomical MRI data utilized for model training was obtained from healthy individuals aged 62–70 years in the Live active Successful Aging (LISA) project. Given the potential sparsity of lesion volume among healthy aging individuals, we explored the impact of incorporating a weighted loss function and ensemble models. To assess the generalizability of the studied DL models, we applied the trained algorithm to an independent subset of data sourced from the MICCAI WMH challenge (MWSC). Notably, this subset had vastly different acquisition parameters compared to the LISA dataset used for training. ResultsConsistently, DL approaches exhibited commendable segmentation performance, achieving the level of inter-rater agreement comparable to expert performance, ensuring superior quality segmentation outcomes. On the out of sample dataset, the ensemble models exhibited the most outstanding performance. ConclusionsDL methods generally surpassed conventional approaches in our study. While all DL methods performed comparably, incorporating attention mechanisms could prove advantageous in future applications with a wider availability of training data. As expected, our experiments indicate that the use of ensemble-based models enables the superior generalization in out-of-distribution settings. We believe that introducing DL methods in the WHM annotation workflow in heathy aging cohorts is promising, not only for reducing the annotation time required, but also for eventually improving accuracy and robustness via incorporating the automatic segmentations in the evaluation procedure.

Brain MRI Findings in CKD Patients with and without Parkinsonism, A Case-Control Study

Background: Chronic kidney disease (CKD) poses a major public health concern on a global scale. Patients with CKD have a heightened risk of developing multiple movement disorders like parkinsonism. Timely diagnosis through imaging and effective management may hold the key to mitigating disease symptoms. Methods: 28 individuals with CKD were categorized into two groups, nine patients showing signs of parkinsonism and a control group of nineteen patients without. All participants underwent non-contrast brain MRI scans, including T1, T2, DWI, and FLAIR sequences. We examined the presence or absence of brain MRI findings. The data obtained were analyzed using SPSS-24 and STATA for statistical analysis. Results: Nine patients exhibited parkinsonism symptoms, while nineteen did not. Brain MRI revealed hypointensities in structures like the putamen, globus pallidus, and cerebellar dentate nucleus in both groups. Comparative analysis showed no statistically significant differences in the percentages of these findings (p &gt; 0.05). Similarly, the presence of cerebral atrophy and cerebrovascular disease did not significantly differ between the groups. Factors such as diabetes, hypertension, and history of dialysis also failed to show significant distinctions (p less than 0.05 for all). Conclusion: The correlation between brain MRI alterations in CKD patients with parkinsonism symptoms and those without was not significant. Nevertheless, there was a strong association between the duration of CKD in patients and the presence of parkinsonism-related signs.

The effect of delay time after injecting gadobutrol on the diagnosis of endolymphatic hydrops

ObjectivesThe aim of this study was to reduce the time delay between gadolinium injection and 3D-FLAIR (three-dimensional fluid-attenuated inversion recovery) MRI by using a single dose of intravenous gadobutrol in Menière's disease patients. Methods17 patients diagnosed with definite unilateral Meniere's disease underwent 3D-FLAIR MRI scans at 2, 4, and 6 h post-intravenous administration of a single-dose of gadobutrol. The signal intensity ratio of bilateral inner ear, cochlear and vestibular hydrops was measured at 2 h, 4 h and 6 h, while the differences in signal intensity ratio and endolymphatic hydrops were evaluated at three time points. ResultsThe cochlea, vestibule, and semicircular canal exhibit clear structural features with distinct perilymph-endolymph boundaries at 2 h, 4 h, and 6 h. The signal intensity ratio of the affected ear was significantly higher than that of the unaffected ear at 2 h, 4 h, and 6 h. The signal intensity ratio at 4 h and 6 h in both the affected and unaffected ears was significantly higher than that at 2 h, but there was no significant difference between 4 h and 6 h. Cochlear hydrops and vestibular hydrops show no significant differences at these time points, demonstrating excellent consistency. ConclusionsWe have demonstrated that 3D-FLAIR images acquired 2 h after intravenous administration of a single-dose gadobutrol are of high quality and equally effective as those obtained at the conventional 4-h time point for diagnosing endolymphatic hydrops in Menière's disease. In clinical practice, the delay time can be safely shortened to 2 h.

A deep learning model integrating multisequence MRI to predict EGFR mutation subtype in brain metastases from non-small cell lung cancer.

To establish a predictive model based on multisequence magnetic resonance imaging (MRI) using deep learning to identify wild-type (WT) epidermal growth factor receptor (EGFR), EGFR exon 19 deletion (19Del), and EGFR exon 21-point mutation (21L858R) simultaneously. A total of 399 patients with proven brain metastases of non-small cell lung cancer (NSCLC) were retrospectively enrolled and divided into training (n = 306) and testing (n = 93) cohorts separately based on two timepoints. All patients underwent 3.0-T brain MRI including T2-weighted, T2-weighted fluid-attenuated inversion recovery, diffusion-weighted imaging, and contrast-enhanced T1-weighted sequences. Radiomics features were extracted from each lesion based on four sequences. An algorithm combining radiomics approach with graph convolutional networks architecture (Radio-GCN) was designed for the prediction of EGFR mutation status and subtype. The area under the curve (AUC) at receiver operating characteristic analysis was used to evaluate the predication capabilities of each model. We extracted 1,290 radiomics features from each MRI sequence. The AUCs of the Radio-GCN model for identifying EGFR 19Del, 21L858R, and WT for the lesion-wise analysis were 0.996 ± 0.004, 0.971 ± 0.013, and 1.000 ± 0.000 on the independent testing cohort separately. It also yielded AUCs of 1.000 ± 0.000, 0.991 ± 0.009, and 1.000 ± 0.000for predicting EGFR mutations respectively for the patient-wise analysis. The κ coefficients were 0.735 and 0.812, respectively. The constructed Radio-GCN model is a new potential tool to predict the EGFR mutation status and subtype in NSCLC patients with brain metastases. The study demonstrated that a deep learning approach based on multisequence MRI can help to predict the EGFR mutation status in NSCLC patients with brain metastases, which is beneficial to guide a personalized treatment. • This is the first study to predict the EGFR mutation subtype simultaneously. • The Radio-GCN model holds the potential to be used as a diagnostic tool. • This study provides an imaging surrogate for identifying the EGFR mutation subtype.

Brain Diffusion in Brainstem Infarctions and Lesion Segmentation using Fluidattenuation Inversion Recovery: A Diagnostic Accuracy Study

Introduction: Diffusion can efficiently detect infratentorial and brainstem infarctions. Fluid-attenuation Inversion Recovery (FLAIR) is a widely used Magnetic Resonance Imaging (MRI) technique for the segmentation of brain lesions and strokes. Aim: To investigate the sensitivity of the Lesion Prediction Algorithm (LPA) within the Lesion Segmentation Tool (LST) in the automated segmentation of acute and subacute brainstem infarctions. Materials and Methods: This study was a retrospective diagnostic accuracy study where 24 patients with brainstem infarctions were referred from the Emergency Department to the Department of Radiodiagnosis at Kasr Alainy Hospital, Cairo University, Egypt. The study was conducted from September 2016 to June 2023. It included 24 patients (14 males and 10 females) with acute and subacute brainstem infarctions. MRI of the brain and diffusion were performed on all patients. Automated segmentation of brainstem infarctions using FLAIR was conducted in all cases. Manual and segmented volumes of brainstem infarctions were calculated. Sensitivity and accuracy of the LPA within the LST in automated segmentation of acute and subacute brainstem infarctions were evaluated. Pearson correlation was used to correlate volumes of White Matter Lesion (WML) burden in patients with brainstem infarctions and patients’ age. Results: The mean age of the participants was 51.75±11.95 years. The sensitivity of the LPA in automated segmentation of acute and subacute brainstem infarctions was 63.6% and 76.9%, respectively. An insignificant correlation (r=0.08; p-value=0.68) between the volumes of WML burden in patients with brainstem infarctions and patients’ age was noted. Conclusion: The sensitivity of the automated method in the segmentation of subacute brainstem infarctions was higher than that in acute brainstem infarctions.