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To make the best clinical judgements, surgeons need to integrate information acquired via multimodal imaging [...].To make the best clinical judgements, surgeons need to integrate information acquired via multimodal imaging [...].

This study aimed whether the uptake of amino tracer positron emission tomography (PET) can be used as an additional imaging biomarker to estimate the prognosis of glioma. Participants comprised 56 adult patients with newly diagnosed and untreated World Health Organization (WHO) grade II–IV astrocytic glioma who underwent surgical excision and were evaluated by 11C-methionine PET prior to the surgical excision at Osaka City University Hospital from July 2011 to March 2018. Clinical and imaging studies were retrospectively reviewed based on medical records at our institution. Preoperative Karnofsky Performance Status (KPS) only influenced progression-free survival (hazard ratio [HR] 0.20; 95% confidence interval [CI] 0.10–0.41, p  < 0.0001), whereas histology (anaplastic astrocytoma: HR 5.30, 95% CI 1.23–22.8, p  = 0.025; glioblastoma: HR 11.52, 95% CI 2.27–58.47, p  = 0.0032), preoperative KPS ≥ 80 (HR 0.23, 95% CI 0.09–0.62, p  = 0.004), maximum lesion-to-contralateral normal brain tissue (LN max) ≥ 4.03 (HR 0.24, 95% CI 0.08–0.71, p  = 0.01), and isocitrate dehydrogenase (IDH) status (HR 14.06, 95% CI 1.81–109.2, p  = 0.011) were factors influencing overall survival (OS) in multivariate Cox regression. OS was shorter in patients with LN max ≥ 4.03 (29.3 months) than in patients with LN max < 4.03 (not reached; p  = 0.03). OS differed significantly between patients with IDH mutant/LN max < 4.03 and patients with IDH mutant/LN max ≥ 4.03. LN max using 11C-methionine PET may be used in prognostic markers for newly identified and untreated WHO grade II–IV astrocytic glioma.

The Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy (cIMPACT-NOW) update 3 recommends that histologic grade II and III IDH-wildtype diffuse astrocytic gliomas that harbor EGFR amplification, the combination of whole chromosome 7 gain and whole chromosome 10 loss (7 + /10 −), or TERT promoter (pTERT) mutations should be considered as glioblastomas (GBM), World Health Organization grade IV. In this retrospective study, we examined the utility of molecular classification based on pTERT status and copy-number alterations (CNAs) in IDH-wildtype lower grade gliomas (LGGs, grade II, and III). The impact on survival was evaluated for the pTERT mutation and CNAs, including EGFR gain/amplification, PTEN loss, CDKN2A homozygous deletion, and PDGFRA gain/amplification. We analyzed 46 patients with IDH-wildtype/pTERT-mutant (mut) LGGs and 85 with IDH-wildtype/pTERT-wildtype LGGs. EGFR amplification and a combination of EGFR gain and PTEN loss (EGFR + /PTEN −) were significantly more frequent in pTERT-mut patients (p < 0.0001). Cox regression analysis showed that the pTERT mutation was a significant predictor of poor prognosis (hazard ratio [HR] 2.79, 95% confidence interval [CI] 1.55–4.89, p = 0.0008), but neither EGFR amplification nor EGFR + /PTEN − was an independent prognostic factor in IDH-wildtype LGGs. PDGFRA gain/amplification was a significant poor prognostic factor in IDH-wildtype/pTERT-wildtype LGGs (HR 2.44, 95% CI 1.09–5.27, p = 0.03, Cox regression analysis). The IDH-wildtype LGGs with either pTERT-mut or PDGFRA amplification were mostly clustered with GBM by DNA methylation analysis. Thus, our study suggests that analysis of pTERT mutation status is necessary and sufficient to diagnose IDH-wildtype diffuse astrocytic gliomas with molecular features of glioblastoma. The PDGFRA status may help further delineate IDH-wildtype/pTERT-wildtype LGGs. Methylation profiling showed that IDH-wildtype LGGs without molecular features of GBM were a heterogeneous group of tumors. Some of them did not fall into existing categories and had significantly better prognoses than those clustered with GBM.

Magnetoencephalography (MEG) provides crucial information in diagnosing focal epilepsy. However, dipole estimation, a commonly used analysis method for MEG, can be time-consuming since it necessitates neurophysiologists to manually identify epileptic spikes. To reduce this burden, we developed the automatic detection of spikes using deep learning in single center. In this study, we performed a multi-center study using six MEG centers to improve the performance of the automated detection of neuromagnetically recorded epileptic spikes, which we previously developed using deep learning. Data from four centers were used for training and evaluation (internal data), and the remaining two centers were used for evaluation only (external data). We used a five-fold subject-wise cross-validation technique to train and evaluate the models. A comparison showed that the multi-center model outperformed the single-center model in terms of performance. The multi-center model achieved an average ROC-AUC of 0.9929 and 0.9426 for the internal and external data, respectively. The median distance between the neurophysiologist-analyzed and automatically analyzed dipoles was 4.36 and 7.23 mm for the multi-center model for internal and external data, respectively, indicating accurate detection of epileptic spikes. By training data from multiple centers, automated analysis can improve spike detection and reduce the analysis workload for neurophysiologists. This study suggests that the multi-center model has the potential to detect spikes within 1 cm of a neurophysiologist’s analysis. This multi-center model can significantly reduce the number of hours required by neurophysiologists to detect spikes.

We attempted to establish a magnetic resonance imaging (MRI)-based radiomic model for stratifying prognostic subgroups of newly diagnosed glioblastoma (GBM) patients and predicting O (6)-methylguanine-DNA methyltransferase promotor methylation ( pMGMT -met) status of the tumor. Preoperative MRI scans from 201 newly diagnosed GBM patients were included in this study. A total of 489 texture features including the first-order feature, second-order features from 162 datasets, and location data from 182 datasets were collected. Supervised principal component analysis was used for prognostication and predictive modeling for pMGMT -met status was performed based on least absolute shrinkage and selection operator regression. 22 radiomic features that were correlated with prognosis were used to successfully stratify patients into high-risk and low-risk groups ( p  = 0.004, Log-rank test). The radiomic high- and low-risk stratification and pMGMT status were independent prognostic factors. As a matter of fact, predictive accuracy of the pMGMT methylation status was 67% when modeled by two significant radiomic features. A significant survival difference was observed among the combined high-risk group, combined intermediate-risk group (this group consists of radiomic low risk and pMGMT -unmet or radiomic high risk and pMGMT -met), and combined low-risk group ( p  = 0.0003, Log-rank test). Radiomics can be used to build a prognostic score for stratifying high- and low-risk GBM, which was an independent prognostic factor from pMGMT methylation status. On the other hand, predictive accuracy of the pMGMT methylation status by radiomic analysis was insufficient for practical use.

We report a case of developmental and epileptic encephalopathy with spike-and-wave activation during sleep with 22q11.2 deletion syndrome in a patient who had undergone hemispherotomy and achieved developmental improvement. A four-year-old male child with paralysis on the left side of his body since birth had a mild developmental delay. An MRI of the brain revealed polymicrogyria diffusely throughout the right hemisphere. He was diagnosed with the 22q11.2 deletion syndrome at one year of age. Focal impaired awareness seizure in the right hemisphere origin and focal to bilateral tonic-clonic seizure appeared by two years of age. At three years of age, myoclonic seizures occurred, which induced frequent falls. Simultaneously, developmental and epileptic encephalopathy with spike-and-wave activation during sleep were observed. At four years and seven months of age, the patient underwent a right hemispherotomy. Epileptic seizures and spike-and-wave activation during sleep disappeared, and cognitive improvement was observed one year after surgery. In spite of chromosomal abnormalities being present, drug-resistant epilepsy with localized regions on MRI should be evaluated to determine surgical options to improve cognitive function and development.

INTRODUCTION While the “T2-FLAIR mismatch sign” has gained considerable attention as being specific for IDHmt/nonCODEL astrocytomas for MRI-based preoperative molecular diagnosis of presumable WHO grade 2 and 3 gliomas in the era of “Radiogenomics, its low sensitivity hinders its use in real clinical practice. The authors recently reported that ”inversion time (TI)\" for FLAIR could impact the presence of the T2-FLAIR mismatch sign in IDHmt/nonCODEL astrocytomas. METHODS Objective 1: A total of 94 treatment-naive MR scans from 77 WHO grade 2, 3 IDHmt/1p19q-nonCODEL astrocytoma patients were collected. The T2-FLAIR mismatch sign was examined for each scan, and this was compared with the acquired FLAIR-TI.Objective 2: A total of 34 treatment-naive MR scans using TI shorter than 2400 msec for FLAIR acquisition were collected from 32 WHO grade 2, 3 glioma patients. The presence of the T2-FLAIR mismatch sign was examined for each scan, and the diagnostic accuracy of IDHmt/nonCODEL astrocytomas was calculated. RESULTS The presence of T2-FLAIR mismatch sign among IDHmt/1p19q-nonCODEL astrocytoma increased from 35.6% to 76.2% (P = .0012, Fisher's exact test, for those with FLAIR acquired using short-TI (<2400 msec). There were seven cases in which the T2-FLAIR mismatch sign was absent in conventional FLAIR but present in FLAIR acquired via short-TI.The sensitivity and specificity of the T2-FLAIR mismatch sign using FLAIR acquired via short-TI for identification to identify IDHmt/nonCODEL astrocytomas was 73% and 89%, respectively. CONCLUSION Shorting the inversion time to less than 2400 msec for FLAIR acquisition improves the detectability of the T2-FLAIR mismatch sign without compromising specificity for identifying IDHmt/nonCODEL astrocytomas.

Posterior Quadrant Disconnection is a surgical technique designed to suppress seizure propagation while preserving motor and sensory functions in patients with drug-resistant epilepsy. Although seizure outcomes following this procedure have been reported, detailed evaluations of its impact on higher cognitive functions remain limited. This study aimed to assess the long-term seizure and cognitive outcomes following PQD in the non-dominant hemisphere, thereby evaluating the efficacy and safety of the procedure. In this case, the patient with drug-resistant epilepsy underwent preoperative evaluation using stereo electroencephalography (SEEG) to identify seizure onset zones and functional mapping related to visuospatial cognition. Following this assessment, PQD was performed. Postoperative outcomes were monitored over a 2-years period, focusing on seizure control and higher cognitive function. The patient achieved Engel class I status postoperatively, indicating complete seizure cessation. While transient hemispatial neglect was observed immediately after surgery, gradual improvement was noted over time. Furthermore, visual memory and cognitive functions showed a tendency to improve, and there were no significant declines in facial recognition or scene recognition abilities. These findings suggest that PQD can effectively improve seizure outcomes while minimizing long-term impacts on cognitive functions. This case highlights the potential of PQD to offer substantial seizure control with limited permanent effects on higher cognitive functions. By providing valuable insights into the safety and efficacy of PQD in the non-dominant hemisphere, this study underscores its viability as a treatment option for selected cases of drug-resistant epilepsy.

Meningioma is a common intracranial tumor originating from arachnoid cap cells. Meningiomas are generally benign tumors curable by one-time resection. However, some meningiomas regrow and invade into the dura mater, and thus frequently require additional treatment. A useful marker to predict the regrowth of meningioma is desired. This study aimed to clarify the significance of p53 and Ki67 for postoperative recurrence of meningioma. The expression of p53 and Ki67 in 215 intracranial or intraspinal meningiomas was investigated by immunohistochemistry. Of the 215 meningiomas, 35 cases (16.3%) were p53-positive and 49 cases (22.8%) were Ki67-positive. Multivariate analysis revealed Ki67 and p53 status as being significantly correlated with recurrence. Positivity for either Ki67- or p53 was significantly associated with poor recurrence-free survival. Combined p53 and Ki67 status might represent a useful independent predictive marker for recurrence of meningioma.