T2 Fluid-attenuated Inversion Recovery Research Articles

The Boston ASL Template and Simulator: Initial development and implementation.

Templates are a hallmark of image analysis in neuroimaging. However, while numerous structural templates exist and have facilitated single-subject and large group studies, templates based on functional contrasts, such as arterial spin labeling (ASL) perfusion, are scarce, have an inherently low spatial resolution, and are not as widely distributed. Having such tools at one's disposal is desirable, for example, in the case of studies not acquiring structural scans. We here propose an initial development of an ASL adult template based on high-resolution fast spin echo acquisitions. High-resolution single-delay ASL, low-resolution multi-delay ASL, T1 -weighted magnetization prepared rapid acquisition 2 gradient echoes, and T2 fluid attenuated inversion recovery data were acquired in a cohort of 10 healthy volunteers (6 males and 4 females, 30± 7 years old). After offline reconstruction of high-resolution perfusion arterial transit time (ATT) and T1 maps, we built a multi-contrast template relying on the Advanced Normalization Toolbox multivariate template nonlinear construction framework. We offer examples for the registration of ASL data acquired with different sequences. Finally, we propose an ASL simulator based on our templates and a standard kinetic model that allows generating synthetic ASL contrasts based on user-specified parameters. Boston ASL Template and Simulator (BATS) offers high-quality, high-resolution perfusion-weighted and quantitative perfusion templates accompanied by ATT and different anatomical contrasts readily available in the Montreal Neurological Institute space. In addition, examples of use for data registration and as a synthetic contrast generator show various applications in which BATS could be used. We propose a new ASL template collection, named BATS, that also includes a simulator allowing the generation of synthetic ASL contrasts. BATS is available at http://github.com/manueltaso/batsasltemplate.

Differentiating EGFR from ALK mutation status using radiomics signature based on MR sequences of brain metastasis

PurposeMore and more small brain metastases (BMs) in asymptomatic patients can be detected even prior to their primary lung cancer with the development of MRI. The aim of this study was to develop a predictive radiomics model to identify epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK) mutation status in BM and explore the optimal MR sequence for predication. MethodsThis retrospective study included 186 patients with proven BM of lung cancer (training cohort: 70 patients with EGFR mutations and 65 patients with ALK rearrangements; testing cohort: 26 patients with EGFR mutations and 25 patients with ALK rearrangements). Radiomics features were separately extracted from contrast-enhanced T1-weighted imaging (T1-CE), T2 fluid-attenuated inversion recovery (T2-FLAIR) and T2WI sequences. The model for three MR sequences were constructed using a random forest classifier. ROC curves were used to validate the capability of the models in the training and testing cohorts. ResultsThe AUCs of the T2-FLAIR model were significantly higher than those of the T1-CE model in training cohort (0.991 versus 0.954) and testing cohort (0.950 versus 0.867) and much higher than those of the T2WI model in training cohort (0.991 versus 0.880) and testing cohort (0.950 versus 0.731). Besides, the F1 scores of the T1-CE model were slightly higher than the T2-FLAIR model and much higher than the T2WI model in two cohorts. ConclusionT2-FLAIR and T1-CE radiomics models that can be used as noninvasive tools for identifying EGFR and ALK mutation status are helpful to guide therapeutic strategies.

The Application of DTCWT on MRI-Derived Radiomics for Differentiation of Glioblastoma and Solitary Brain Metastases.

Background: While magnetic resonance imaging (MRI) is the imaging modality of choice for the evaluation of patients with brain tumors, it may still be challenging to differentiate glioblastoma multiforme (GBM) from solitary brain metastasis (SBM) due to their similar imaging features. This study aimed to evaluate the features extracted of dual-tree complex wavelet transform (DTCWT) from routine MRI protocol for preoperative differentiation of glioblastoma (GBM) and solitary brain metastasis (SBM). Methods: A total of 51 patients were recruited, including 27 GBM and 24 SBM patients. Their contrast-enhanced T1-weighted images (CET1WIs), T2 fluid-attenuated inversion recovery (T2FLAIR) images, diffusion-weighted images (DWIs), and apparent diffusion coefficient (ADC) images were employed in this study. The statistical features of the pre-transformed images and the decomposed images of the wavelet transform and DTCWT were utilized to distinguish between GBM and SBM. Results: The support vector machine (SVM) showed that DTCWT images have a better accuracy (82.35%), sensitivity (77.78%), specificity (87.50%), and the area under the curve of the receiver operating characteristic curve (AUC) (89.20%) than the pre-transformed and conventional wavelet transform images. By incorporating DTCWT and pre-transformed images, the accuracy (86.27%), sensitivity (81.48%), specificity (91.67%), and AUC (93.06%) were further improved. Conclusions: Our studies suggest that the features extracted from the DTCWT images can potentially improve the differentiation between GBM and SBM.

A Novel Variant of the Short-Chain Enoyl-CoA Hydratase-1 Gene Presenting with a Mild Phenotype: The Second Case Report from India

AbstractA 9-year-old girl presented with asymmetric abnormal twisting movements affecting her left side more than the right side, initially action induced, but later persistent. Examination revealed generalized persistent dystonia with choreoathetosis and right partial tonic ocular tilt reaction. Brain magnetic resonance imaging showed T1 and T2 fluid-attenuated inversion recovery (FLAIR) hypointense and T2 hyperintense signal changes in bilateral globus pallidi. Clinical exome sequencing revealed compound heterozygous variatnts in enoyl-CoA hydratase-1 (ECHS1) gene: a novel pathogenic variant in exon 6, chr10:g.133366045G > A (p.Gln224Ter) and a likely pathogenic variant in exon 5, chr10:g.133366990G > A (p.Ala173Val). Metabolic testing and arterial lactate levels were normal. She was treated with valine restricted diet, trihexiphenidyl, clonazepam, N-acetyl cysteine and mitochondrial cocktail, without significant improvement over the 6 months follow-up period.

Radiomic Signatures for Predicting EGFR Mutation Status in Lung Cancer Brain Metastases.

BackgroundLung cancer is the most common primary tumor metastasizing to the brain. A significant proportion of lung cancer patients show epidermal growth factor receptor (EGFR) mutation status discordance between the primary cancer and the corresponding brain metastases, which can affect prognosis and therapeutic decision-making. However, it is not always feasible to obtain brain metastases samples. The aim of this study was to establish a radiomic model to predict the EGFR mutation status of lung cancer brain metastases.MethodsData from 162 patients with resected brain metastases originating from lung cancer (70 with mutant EGFR, 92 with wild-type EGFR) were retrospectively analyzed. Radiomic features were extracted using preoperative brain magnetic resonance (MR) images (contrast-enhanced T1-weighted imaging, T1CE; T2-weighted imaging, T2WI; T2 fluid-attenuated inversion recovery, T2 FLAIR; and combinations of these sequences), to establish machine learning-based models for predicting the EGFR status of excised brain metastases (108 metastases for training and 54 metastases for testing). The least absolute shrinkage selection operator was used to select informative features; radiomics models were built with logistic regression of the training cohort, and model performance was evaluated using an independent test set.ResultsThe best-performing model was a combination of 10 features selected from multiple sequences (two from T1CE, five from T2WI, and three from T2 FLAIR) in both the training and test sets, resulting in classification area under the curve, accuracy, sensitivity, and specificity values of 0.85 and 0.81, 77.8% and 75.9%, 83.7% and 73.1%, and 73.8% and 78.6%, respectively.ConclusionsRadiomic signatures integrating multi-sequence MR images have the potential to noninvasively predict the EGFR mutation status of lung cancer brain metastases.

Three-Dimensional Arterial Spin Labeling-Guided Sub-Volume Segmentation of Radiotherapy in Adult Non-Enhancing Low-Grade Gliomas.

ObjectiveThe present study aimed to evaluate the feasibility of sub-volume segmentation for radiotherapy planning of adult non-enhancing low-grade gliomas (NE-LGGs) guided by three-dimensional arterial spin labeling (3D-ASL). The differences in high- and low-perfusion areas of NE-LGGs were analyzed using multi-sequence magnetic resonance imaging (MRI) radiomics.MethodsFifteen adult patients with NE-LGGs were included in the study. MR images, including T1-weighted imaging (T1WI), T2 Propeller, T2 fluid-attenuated inversion recovery (T2 Flair), 3D-ASL, and contrast-enhanced T1WI (CE-T1WI), were obtained. The gross tumor volume (GTV) was delineated according to the hyperintensity on T2 Flair. The GTV was divided into high- and low-perfusion areas, namely GTV-ASL and GTV-SUB, respectively, based on the differences in cerebral blood flow (CBF) value. The volumes and CBF values of high- and low-perfusion areas were measured and compared. The least absolute shrinkage and selection operator (LASSO) regression was used to select the optimal features of all MR maps. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic accuracy of the absolute CBFmean (aCBFmean), relative CBFmean (rCBFmean, normalized by the CBF value of the normal gray matter), and screened features in differentiating high- and low-perfusion areas.ResultsAmong the enrolled patients, three (20%) patients with NE-LGGs showed focal intra- and post-radiotherapy contrast enhancement within a prior high-perfusion area of 3D-ASL. The volume ratio of the GTV-ASL to the GTV was (37.08% ± 17.88)% (46.26 ± 44.51 vs. 167.46 ± 209.64 cm3, P = 0.000). The CBFmean in the high-perfusion area was approximately two times of that in the edema area or normal gray matter (66.98 ± 18.03 vs. 35.19 ± 7.75 or 33.92 ± 8.48 ml/100g/min, P = 0.000). Thirteen features were screened, seven of which were extracted from 3D-ASL. The area undercurve (AUC) values of aCBFmean, rCBFmean, and firstorder_10Percentile from 3D-ASL were more than 0.9, of which firstorder_10Percentile was the highest. Their cut-off values were 44.16 ml/100 g/min, 1.49 and 31, respectively.ConclusionThe difference in blood perfusion in the GTV can be quantified and analyzed based on 3D-ASL images for NE-LGGs, which could guide the sub-volume segmentation of the GTV. 3D-ASL should become a routine method for NE-LGGs during simulation and radiotherapy.

Transformer-Based Deep-Learning Algorithm for Discriminating Demyelinating Diseases of the Central Nervous System With Neuroimaging.

BackgroundDifferential diagnosis of demyelinating diseases of the central nervous system is a challenging task that is prone to errors and inconsistent reading, requiring expertise and additional examination approaches. Advancements in deep-learning-based image interpretations allow for prompt and automated analyses of conventional magnetic resonance imaging (MRI), which can be utilized in classifying multi-sequence MRI, and thus may help in subsequent treatment referral.MethodsImaging and clinical data from 290 patients diagnosed with demyelinating diseases from August 2013 to October 2021 were included for analysis, including 67 patients with multiple sclerosis (MS), 162 patients with aquaporin 4 antibody-positive (AQP4+) neuromyelitis optica spectrum disorder (NMOSD), and 61 patients with myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD). Considering the heterogeneous nature of lesion size and distribution in demyelinating diseases, multi-modal MRI of brain and/or spinal cord were utilized to build the deep-learning model. This novel transformer-based deep-learning model architecture was designed to be versatile in handling with multiple image sequences (coronal T2-weighted and sagittal T2-fluid attenuation inversion recovery) and scanning locations (brain and spinal cord) for differentiating among MS, NMOSD, and MOGAD. Model performances were evaluated using the area under the receiver operating curve (AUC) and the confusion matrices measurements. The classification accuracy between the fusion model and the neuroradiological raters was also compared.ResultsThe fusion model that was trained with combined brain and spinal cord MRI achieved an overall improved performance, with the AUC of 0.933 (95%CI: 0.848, 0.991), 0.942 (95%CI: 0.879, 0.987) and 0.803 (95%CI: 0.629, 0.949) for MS, AQP4+ NMOSD, and MOGAD, respectively. This exceeded the performance using the brain or spinal cord MRI alone for the identification of the AQP4+ NMOSD (AUC of 0.940, brain only and 0.689, spinal cord only) and MOGAD (0.782, brain only and 0.714, spinal cord only). In the multi-category classification, the fusion model had an accuracy of 81.4%, which was significantly higher compared to rater 1 (64.4%, p=0.04<0.05) and comparable to rater 2 (74.6%, p=0.388).ConclusionThe proposed novel transformer-based model showed desirable performance in the differentiation of MS, AQP4+ NMOSD, and MOGAD on brain and spinal cord MRI, which is comparable to that of neuroradiologists. Our model is thus applicable for interpretating conventional MRI in the differential diagnosis of demyelinating diseases with overlapping lesions.

Radiomic Signatures for Predicting Receptor Status in Breast Cancer Brain Metastases.

BackgroundsA significant proportion of breast cancer patients showed receptor discordance between primary cancers and breast cancer brain metastases (BCBM), which significantly affected therapeutic decision-making. But it was not always feasible to obtain BCBM tissues. The aim of the present study was to analyze the receptor status of primary breast cancer and matched brain metastases and establish radiomic signatures to predict the receptor status of BCBM.MethodsThe receptor status of 80 matched primary breast cancers and resected brain metastases were retrospectively analyzed. Radiomic features were extracted using preoperative brain MRI (contrast-enhanced T1-weighted imaging, T2-weighted imaging, T2 fluid-attenuated inversion recovery, and combinations of these sequences) collected from 68 patients (45 and 23 for training and test sets, respectively) with BCBM excision. Using least absolute shrinkage selection operator and logistic regression model, the machine learning-based radiomic signatures were constructed to predict the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status of BCBM.ResultsDiscordance between the primary cancer and BCBM was found in 51.3% of patients, with 27.5%, 27.5%, and 5.0% discordance for ER, PR, and HER2, respectively. Loss of receptor expression was more common (33.8%) than gain (18.8%). The radiomic signatures built using combination sequences had the best performance in the training and test sets. The combination model yielded AUCs of 0.89, 0.88, and 0.87, classification sensitivities of 71.4%, 90%, and 87.5%, specificities of 81.2%, 76.9%, and 71.4%, and accuracies of 78.3%, 82.6%, and 82.6% for ER, PR, and HER2, respectively, in the test set.ConclusionsReceptor conversion in BCBM was common, and radiomic signatures show potential for noninvasively predicting BCBM receptor status.

Quantitative susceptibility mapping of both ring and non-ring white matter lesions in relapsing remitting multiple sclerosis

BackgroundRecent evidence suggests that the presence of magnetic susceptibility changes in MS lesions correlates with patients' expanded disability status scale (EDSS). PurposeThis study evaluated the presence of ring lesions (RLs) and non-RLs, as well as changes in lesion susceptibility and lesion volume over a two-year time period in relapsing-remitting multiple sclerosis (RRMS) patients and compared these measures to the EDSS. Study typeLongitudinal cohort. Materials and methodsA total of forty-three (43) patients with RRMS were recruited for this study. All subjects underwent 3 T MRI at baseline and forty-one (41) subjects had follow-up scans over a two-year period. The protocol included T2 fluid attenuated inversion recovery (FLAIR), pre- and post-contrast 2D T1-weighted gradient echo imaging and susceptibility weighted imaging (SWI) sequences. All data were acquired prior to gadolinium-based contrast agent injection except for SWI and post-contrast T1-weighted data. Mean and peak susceptibilities of lesions were measured using quantitative susceptibility mapping (QSM). For the RLs, inner−/outer-diameters from SWI data and absolute intensity from the FLAIR data were measured. The susceptibility and volumes of RLs and non-RLs were correlated with EDSS. ResultsThe number of RLs correlated positively with EDSS (p-value = 0.04), but the RL volumes did not show a correlation with EDSS. Measurements of the annular ring and regions with high susceptibility remained constant over time (p = 0.2). For non-ring QSM-positive (QSM+) lesions, the average susceptibility was significantly correlated to EDSS (p < 0.01.) This was also the case for lesion volume, as well as the product of volume and susceptibility when compared to EDSS (p < 0.01 for both). ConclusionsThe susceptibility distribution and lesion volumes for the RLs remained almost constant over time, suggesting that the changes in pathophysiology of the RLs is a gradual process. On the other hand, the presence of more than one RL along with both volume and susceptibility of non-RL QSM+ lesions were significantly associated with increased disability as measured by EDSS. These findings may strengthen the role of QSM in assessing MS patients radiologically.

Non-Invasive Prediction of Survival Time of Midline Glioma Patients Using Machine Learning on Multiparametric MRI Radiomics Features.

ObjectivesTo explore the feasibility of predicting overall survival (OS) of patients with midline glioma using multi-parameter magnetic resonance imaging (MRI) features.MethodsData 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.ResultsA 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.ConclusionThe combined radiomics model based on multi-parameter MRI features provided a reliable non-invasive method for the prognostic prediction of midline gliomas.

Machine Learning for Prediction of Recurrence in Parasagittal and Parafalcine Meningiomas: Combined Clinical and MRI Texture Features.

A subset of parasagittal and parafalcine (PSPF) meningiomas may show early progression/recurrence (P/R) after surgery. This study applied machine learning using combined clinical and texture features to predict P/R in PSPF meningiomas. A total of 57 consecutive patients with pathologically confirmed (WHO grade I) PSPF meningiomas treated in our institution between January 2007 to January 2019 were included. All included patients had complete preoperative magnetic resonance imaging (MRI) and more than one year MRI follow-up after surgery. Preoperative contrast-enhanced T1WI, T2WI, T1WI, and T2 fluid-attenuated inversion recovery (FLAIR) were analyzed retrospectively. The most significant 12 clinical features (extracted by LightGBM) and 73 texture features (extracted by SVM) were combined in random forest to predict P/R, and personalized radiomic scores were calculated. Thirteen patients (13/57, 22.8%) had P/R after surgery. The radiomic score was a high-risk factor for P/R with hazard ratio of 15.73 (p < 0.05) in multivariate hazards analysis. In receiver operating characteristic (ROC) analysis, an AUC of 0.91 with cut-off value of 0.269 was observed in radiomic scores for predicting P/R. Subtotal resection, low apparent diffusion coefficient (ADC) values, and high radiomic scores were associated with shorter progression-free survival (p < 0.05). Among different data input, machine learning using combined clinical and texture features showed the best predictive performance, with an accuracy of 91%, precision of 85%, and AUC of 0.88. Machine learning using combined clinical and texture features may have the potential to predict recurrence in PSPF meningiomas.

Automatic differentiation of Grade I and II meningiomas on magnetic resonance image using an asymmetric convolutional neural network

The Grade of meningioma has significant implications for selecting treatment regimens ranging from observation to surgical resection with adjuvant radiation. For most patients, meningiomas are diagnosed radiologically, and Grade is not determined unless a surgical procedure is performed. The goal of this study is to train a novel auto-classification network to determine Grade I and II meningiomas using T1-contrast enhancing (T1-CE) and T2-Fluid attenuated inversion recovery (FLAIR) magnetic resonance (MR) images. Ninety-six consecutive treatment naïve patients with pre-operative T1-CE and T2-FLAIR MR images and subsequent pathologically diagnosed intracranial meningiomas were evaluated. Delineation of meningiomas was completed on both MR images. A novel asymmetric 3D convolutional neural network (CNN) architecture was constructed with two encoding paths based on T1-CE and T2-FLAIR. Each path used the same 3 × 3 × 3 kernel with different filters to weigh the spatial features of each sequence separately. Final model performance was assessed by tenfold cross-validation. Of the 96 patients, 55 (57%) were pathologically classified as Grade I and 41 (43%) as Grade II meningiomas. Optimization of our model led to a filter weighting of 18:2 between the T1-CE and T2-FLAIR MR image paths. 86 (90%) patients were classified correctly, and 10 (10%) were misclassified based on their pre-operative MRs with a model sensitivity of 0.85 and specificity of 0.93. Among the misclassified, 4 were Grade I, and 6 were Grade II. The model is robust to tumor locations and sizes. A novel asymmetric CNN with two differently weighted encoding paths was developed for successful automated meningioma grade classification. Our model outperforms CNN using a single path for single or multimodal MR-based classification.

Revisiting the Concept of Recurrence of Primary Central Nervous System Lymphomas After Complete Response to Methotrexate-Based Therapy: Periventricular Reseeding as the Predominant Mechanism of Recurrence

PurposeUnderstanding patterns of relapse for primary central nervous system lymphoma (PCNSL) may inform mechanisms of recurrence and optimal consolidation strategies. In this study, we report patterns of relapse among patients with PCNSL who achieved a complete response to high-dose methotrexate (HD-MTX)-based chemotherapy with or without consolidation radiation therapy (RT). Methods and MaterialsWe conducted an institutional retrospective analysis of patients with PCNSL who received HD-MTX-based chemotherapy between November 2001 and May 2019. Relapses were characterized as in-field (within original T1 contrasted lesion), marginal (within T2 fluid-attenuated inversion recovery but not T1), local (in-field or marginal), distant brain (no overlap), or distant (distant brain, cerebrospinal fluid, vitreous or extra-axial) and further characterized with respect to periventricular location (≤10 mm of ventricles). ResultsSeventy-eight patients with PCNSL met inclusion criteria, of whom 29 (37%) underwent consolidation RT. Median progression-free survival and overall survival were 57.0 and 66.7 months, respectively. After a median follow-up of 38.9 months, a total of 32 patients (41%) experienced recurrence. Most patients (21 [65.6%]) had a periventricular failure. Surprisingly, local recurrences (n = 11) were exclusively observed within periventricular lesions, whereas distant recurrences (n = 21) were seen in both periventricular and nonperiventricular locations (P= .009). The median time to progression was shorter for locally recurrent lesions compared with distant recurrences (13.8 vs 26.1 months; P= .03). ConclusionsAfter complete response to HD-MTX, few failures occurred within initial T1 contrast-enhancing lesions and many of these may have been alternatively classified as periventricular failures. These observations argue against the use of purely focal RT consolidation for patients who achieve a complete response after HD-MTX-based chemotherapy and suggest that periventricular reseeding may have a central role in PCNSL recurrence.

T2-Fluid-Attenuated Inversion Recovery Mismatch Sign in Grade II and III Gliomas: Is There a Coexisting T2-Diffusion-Weighted Imaging Mismatch?

To determine whether the T2 fluid-attenuated inversion recovery (T2-FLAIR) mismatch sign in diffuse gliomas is associated with an equivalent pattern of disparity in signal intensities when comparing T2- and diffusion-weighted imaging (DWI). The level of correspondence between T2-FLAIR and T2-DWI evaluations in 34 World Health Organization grade II/III gliomas and interreader agreement among 3 neuroradiologists were assessed by calculating intraclass correlation coefficient and κ statistics, respectively. Tumoral apparent diffusion coefficient values were compared using t test. There was an almost perfect correspondence between the 2 mismatch signs (intraclass correlation coefficient = 0.824 [95% confidence interval, 0.68-0.91]) that were associated with higher mean tumoral apparent diffusion coefficient (P < 0.01). Interreader agreement was substantial for T2-FLAIR (Fleiss κ = 0.724) and moderate for T2-DWI comparisons (Fleiss κ = 0.589) (P < 0.001). The T2-FLAIR mismatch sign is usually reflected by a distinct microstructural pattern on DWI. The management of this tumor subtype may benefit from specifically tailored imaging assessments.

342 Long-Term Follow-up of 54 Cases of Recurrent Glioblastoma Treated with Magnetic Resonance-Guided Laser Interstitial Thermal Therapy

INTRODUCTION: Magnetic resonance guided laser-induced thermal therapy (MRgLITT) is a novel minimally invasive procedural treatment for progressive cerebral lesions and cerebral edema related to post-radiation treatment effect. METHODS: We conducted a single-center retrospective study of pre-operative imaging, clinical characteristics and survival outcomes of 54 patients with previously treated (WHO Grade IV) between January 2013 to December 2020. RESULTS: The average age of patients was 58.7 ± 12.0 years. Patients underwent MRgLITT a median time of 21.6 ± 23.7 months from original diagnosis. 20% (n = 11) of patients had previous history of stereotactic radiosurgery, 61% (n = 33) intensity-modulated radiation therapy, and 80% (n = 43) chemotherapy. There was an average of 1.2 ± 0.7 prior surgical resections before MRgLITT was performed. 41% (n = 22) of patients were on dexamethasone at the time of treatment. The median pre-operative enhancing lesion volume on T1 gadolinium-enhanced magnetic resonance imaging (MRI) was 9.7 cm3 (standard error [SE], 2.0 cm3) and 27.0 cm3 (SE, 5.6 cm3) on T2 fluid-attenuated inversion recovery MRI. The pathology of the biopsies taken during MRgLITT confirmed tumor recurrence in 61% (n = 33) of patients, and treatment effect or reactive changes in 37% (n = 20) of patients. 67% (n = 28) experienced radiographic evidence of progression of the previously treated lesion, while 21% (n =9) and 12% (n = 5) experienced progression of a new lesion or both, respectively. Progression-free survival after MRgLITT was 3.5 months (standard error [SE], 0.6; 95% CI, 2.4-4.6). At the time of the study, 78% (n = 42) of patients were deceased. Mean overall survival time was 29.9 months (SE, 4.3; 95% CI, 21.4-38.3). Mean survival time after MRgLITT was 8.3 months (SE, 1.2; 95% CI, 5.9-10.6. Of the 12 remaining living patients, 36% (n = 4) had a decline in their clinical status at the time of last follow up, while 64% (n = 7) and 9% (n = 1) were stable or improved, respectively. Patients were followed for a mean time interval of 9.25 ± 12.2 months from MRgLITT. CONCLUSION: MRgLITT may be a viable, minimally invasive option for patients with glioblastoma but is associated with relatively short post-treatment progression-free survival.

Application of Three-Dimensional Pseudocontinuous Arterial Spin Labeling Perfusion Imaging in the Brains of Children With Autism.

ObjectiveTo explore the application of three-dimensional pseudocontinuous arterial spin labeling (3D-PCASL) perfusion imaging in the brains of children with autism and to understand the characteristics of cerebral blood perfusion in children with autism.MethodsA total of 320 children with autism (160 men and 160 women) aged between 2 and 18 years and 320 age- and sex-matched healthy children participated in the study. All children were scanned by 3.0 T magnetic resonance axial T1 fluid-attenuated inversion recovery (FLAIR), T2 FLAIR, 3D-T1, and 3D-PCASL sequences. After postprocessing, cerebral blood flow (CBF) values in each brain region of children with autism and healthy children at the same age were compared and analyzed. Furthermore, CBF characteristics in each brain region of autistic children at various ages were determined.ResultsThe CBF values of the frontal lobe, hippocampus, temporal lobe, and caudate nucleus of children with autism are lower than those of healthy children (P < 0.05). Additionally, as the ages of children with autism increase, the number of brain regions with decreased CBF values gradually increases. A receiver operating characteristic (ROC) analysis results show that the CBF values of the frontal lobe, hippocampus, temporal lobe, and caudate nucleus can distinguish children with autism [area under the ROC curve (AUC) > 0.05, P < 0.05].ConclusionThe 3D-PCASL shows lower brain CBF values in children with autism.Clinical Trial Registrationwww.ClinicalTrials.gov, identifier: ChiCTR2000034356.

Mild encephalitis/encephalopathy with a reversible splenial lesion associated with systemic Mycoplasma pneumoniae infection in North America: a case report

BackgroundMild encephalitis/encephalopathy with reversible splenial lesion is a clinical-radiological entity found to occur in the setting of an acute systemic inflammatory state with isolated lesions of the splenium of the corpus callosum and mild encephalopathy. Mild encephalitis/encephalopathy with reversible splenial lesion is commonly found to occur in children in the setting of viral infections. It has rarely been associated with Mycoplasma pneumoniae in the United States, unlike in Eastern and Southern Asia where this is much more prominent.Case presentationsA 5-year-old African-American boy with autism spectrum disorder presented to our emergency department with acute onset intractable vomiting, diarrhea, and abnormal tensing movements for 2 days, following a 6-day period of fatigue, fever, and spastic abdominal pain. Emergent work-up in our department ruled out acute gastrointestinal pathologies. Given the high fevers and encephalopathy, there was concern for meningitis or encephalitis. His cerebrospinal fluid profile was concerning for viral meningitis, however extensive infectious workup was negative. Magnetic resonance imaging of his brain demonstrated a T2 fluid-attenuated inversion recovery sequence hyperintensity in the splenium of the corpus callosum, read as postictal changes by radiology. Continuous video electroencephalography demonstrated mild diffuse encephalopathy without electrographic correlate of his tensing episodes. He was determined to have mild encephalitis/encephalopathy with a reversible splenial lesion in the setting of a postinfectious etiology. He was treated with a single pulse-dose of intravenous methylprednisolone, following which he gradually returned to his baseline the next day. Repeat magnetic resonance imaging and cerebrospinal fluid evaluation demonstrated resolution of previous findings. He was ultimately diagnosed with an acute M. pneumoniae infection, which was determined to be the etiology of his mild encephalitis/encephalopathy with a reversible splenial lesion.ConclusionsThe presentation of mild encephalitis/encephalopathy with a reversible splenial lesion is often nonspecific, with behavioral symptoms ranging from irritability to disturbances in consciousness. Its prevalence is higher in the pediatric population, and is thought to be more of an infection-associated encephalopathy syndrome in this group. The infections are typically viral, more so than bacterial. M. pneumoniae, a small, atypical bacterium lacking a peptidoglycan cell wall, is a common respiratory tract pathogen found in children. Despite infection being so rampant in the pediatric community, very few cases of M. pneumoniae-associated mild encephalitis/encephalopathy with a reversible splenial lesion in the United States have been reported. In Eastern and Southern Asian countries, however, M. pneumoniae-associated mild encephalitis/encephalopathy with a reversible splenial lesion is much more commonly reported. This difference may potentially lie in the prevalence of macrolide-resistant M. pneumoniae, which is significantly higher in Asian countries given more liberal antibiotic use in M. pneumoniae infections. Infections with macrolide-resistant M. pneumoniae are reportedly greater in severity and duration. This amplified state may suggest a correlation between intensity of inflammatory response and the development of mild encephalitis/encephalopathy with a reversible splenial lesion. Given the rarity of M. pneumoniae-associated mild encephalitis/encephalopathy with a reversible splenial lesion in the United States, much remains unknown regarding predilection and optimum treatment strategy. As rates of macrolide-resistant M. pneumoniae begin to rise in the United States, maintaining a high level of suspicion remains key in better understanding this unique phenomenon.

A nomogram strategy for identifying the subclassification of IDH mutation and ATRX expression loss in lower-grade gliomas.

To construct a radiomics nomogram based on multiparametric MRI data for predicting isocitrate dehydrogenase 1 mutation (IDH +) and loss of nuclear alpha thalassemia/mental retardation syndrome X-linked expression (ATRX -) in patients with lower-grade gliomas (LrGG; World Health Organization [WHO] 2016 grades II and III). A total of 111 LrGG patients (76 mutated IDH and 35 wild-type IDH) were enrolled, divided into a training set (n = 78) and a validation set (n = 33) for predicting IDH mutation. IDH + LrGG patients were further stratified into the ATRX - (n = 38) and ATRX + (n = 38) subtypes. A total of 250 radiomics features were extracted from the region of interest of each tumor, including that from T2 fluid-attenuated inversion recovery (T2 FLAIR), contrast-enhanced T1 WI, ASL-derived cerebral blood flow (CBF), DWI-derived ADC, and exponential ADC (eADC). A radiomics signature was selected using the Elastic Net regression model, and a radiomics nomogram was finally constructed using the age, gender information, and above features. The radiomics nomogram identified LrGG patients for IDH mutation (C-index: training sets = 0.881, validation sets = 0.900) and ATRX loss (C-index: training sets = 0.863, validation sets = 0.840) with good calibration. Decision curve analysis further confirmed the clinical usefulness of the two nomograms for predicting IDH and ATRX status. The nomogram incorporating age, gender, and the radiomics signature provided a clinically useful approach in noninvasively predicting IDH and ATRX mutation status for LrGG patients. The proposed method could facilitate MRI-based clinical decision-making for the LrGG patients. • Non-invasive determination of IDH and ATRX gene status of LrGG patients can be obtained with a radiomics nomogram. • The proposed nomogram is constructed by radiomics signature selected from 250 radiomics features, combined with age and gender. • The proposed radiomics nomogram exhibited good calibration and discrimination for IDH and ATRX gene mutation stratification of LrGG patients in both training and validation sets.

Is the brain involved in patients with late-onset Pompe disease?

Our objective was to investigate brain structure, cerebral vasculature, and cognitive function in a cohort of patients with late‐onset Pompe disease, with particular reference to the differences from those with the classic infantile phenotype, where extensive white‐matter abnormalities (WMA) and impaired cognition on long‐term enzyme treatment are reported in a subset of patients. Brain imaging (T1, T2, T2 fluid‐attenuated inversion recovery, susceptibility‐weighted images, and magnetic resonance angiography–time of flight) was combined with extensive cognitive testing of general intelligence (Wechsler IQ Test, Montreal Cognitive Assessment [MoCA]) and specific neuropsychological domains (verbal fluency, cognitive flexibility, attention, memory, and visuospatial abilities). We included 19 patients with late‐onset Pompe disease (age range 11‐56 years). Two patients showed mild punctate WMA within normal range for age, with a Fazekas score (FS) of 1 to 2. Magnetic resonance angiography revealed a slight vertebrobasilar dolichoectasia in two patients yet did not show any aneurysms or vascular dissections. Most patients had age‐adjusted scores within the normal range for the Wechsler index scores (verbal comprehension, perceptual reasoning, working memory, and processing speed) and combined total intelligence (IQ) score (median 101, interquartile range 91‐111; one patient had a below‐average score for total IQ) as well as for the specific domains verbal fluency, attention, and memory. A subset of patients performed suboptimally on the Rey Complex Figure Test (9/14 patients) or cube‐copying/clock‐drawing test of the MoCA (8/10 patients). We therefore concluded that our study showed no brain abnormalities, other than minor microvascular lesions considered within normal range for age, nor general cognitive impairment in late‐onset Pompe patients. These findings are in sharp contrast with the widespread WMA and cognitive problems found in some classic infantile patients.

Radiomics and Qualitative Features From Multiparametric MRI Predict Molecular Subtypes in Patients With Lower-Grade Glioma.

BackgroundIsocitrate dehydrogenase (IDH) mutation and 1p19q codeletion status have been identified as significant markers for therapy and prognosis in lower-grade glioma (LGG). The current study aimed to construct a combined machine learning-based model for predicting the molecular subtypes of LGG, including (1) IDH wild-type astrocytoma (IDHwt), (2) IDH mutant and 1p19q non-codeleted astrocytoma (IDHmut-noncodel), and (3) IDH-mutant and 1p19q codeleted oligodendroglioma (IDHmut-codel), based on multiparametric magnetic resonance imaging (MRI) radiomics, qualitative features, and clinical factors.MethodsA total of 335 patients with LGG (WHO grade II/III) were retrospectively enrolled. The sum of 5,929 radiomics features were extracted from multiparametric MRI. Selected robust, non-redundant, and relevant features were used to construct a random forest model based on a training cohort (n = 269) and evaluated on a testing cohort (n = 66). Meanwhile, preoperative MRIs of all patients were scored in accordance with Visually Accessible Rembrandt Images (VASARI) annotations and T2-fluid attenuated inversion recovery (T2-FLAIR) mismatch sign. By combining radiomics features, qualitative features (VASARI annotations and T2-FLAIR mismatch signs), and clinical factors, a combined prediction model for the molecular subtypes of LGG was built.ResultsThe 17-feature radiomics model achieved area under the curve (AUC) values of 0.6557, 0.6830, and 0.7579 for IDHwt, IDHmut-noncodel, and IDHmut-codel, respectively, in the testing cohort. Incorporating qualitative features and clinical factors into the radiomics model resulted in improved AUCs of 0.8623, 0.8056, and 0.8036 for IDHwt, IDHmut-noncodel, and IDHmut-codel, with balanced accuracies of 0.8924, 0.8066, and 0.8095, respectively.ConclusionThe combined machine learning algorithm can provide a method to non-invasively predict the molecular subtypes of LGG preoperatively with excellent predictive performance.