Education and training in neurology: developments and future challenges.

BACKGROUND AND PURPOSE: Training and education is essential for best practice medicine and is especially important in a rapidly evolving field such as neurology. Due to improved imaging techniques and laboratory testing, there is a better understanding of the pathophysiology of diseases. As a result more treatments have become available. The most important developments in neurology over the last two decades and their effect on training and education are described. In addition, how future training should be aware of the challenges ahead of us is described. METHODS: This is a narrative review describing developments and challenges based on personal experience and the literature. RESULTS: Due to major developments in radiological and immunological testing, major changes have been seen in different subspecialties of neurology, including but not limited to, the treatment of ischaemic stroke, the development of new entities in the field of demyelinating diseases and auto-immune encephalitis, and diffuse glioma. These developments challenge the education and training in neurology with, ahead of us, technological developments, an aging population, and potentially more superspecialization. CONCLUSION: Although there are differences in the training curricula between European countries, the developments and future challenges within the field of neurology are very similar. In the development of future curricula it is important to face these developments and challenges and to adapt to them.

Evaluating the Predictive Value of Glioma Growth Models for Low-Grade Glioma After Tumor Resection.

Tumor growth models have the potential to model and predict the spatiotemporal evolution of glioma in individual patients. Infiltration of glioma cells is known to be faster along the white matter tracts, and therefore structural magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) can be used to inform the model. However, applying and evaluating growth models in real patient data is challenging. In this work, we propose to formulate the problem of tumor growth as a ranking problem, as opposed to a segmentation problem, and use the average precision (AP) as a performance metric. This enables an evaluation of the spatial pattern that does not require a volume cut-off value. Using the AP metric, we evaluate diffusion-proliferation models informed by structural MRI and DTI, after tumor resection. We applied the models to a unique longitudinal dataset of 14 patients with low-grade glioma (LGG), who received no treatment after surgical resection, to predict the recurrent tumor shape after tumor resection. The diffusion models informed by structural MRI and DTI showed a small but significant increase in predictive performance with respect to homogeneous isotropic diffusion, and the DTI-informed model reached the best predictive performance. We conclude there is a significant improvement in the prediction of the recurrent tumor shape when using a DTI-informed anisotropic diffusion model with respect to istropic diffusion, and that the AP is a suitable metric to evaluate these models. All code and data used in this publication are made publicly available.

Combined molecular subtyping, grading, and segmentation of glioma using multi-task deep learning.

BACKGROUND: Accurate characterization of glioma is crucial for clinical decision making. A delineation of the tumor is also desirable in the initial decision stages but is time-consuming. Previously, deep learning methods have been developed that can either non-invasively predict the genetic or histological features of glioma, or that can automatically delineate the tumor, but not both tasks at the same time. Here, we present our method that can predict the molecular subtype and grade, while simultaneously providing a delineation of the tumor. METHODS: We developed a single multi-task convolutional neural network that uses the full 3D, structural, preoperative MRI scans to predict the IDH mutation status, the 1p/19q co-deletion status, and the grade of a tumor, while simultaneously segmenting the tumor. We trained our method using a patient cohort containing 1508 glioma patients from 16 institutes. We tested our method on an independent dataset of 240 patients from 13 different institutes. RESULTS: In the independent test set, we achieved an IDH-AUC of 0.90, an 1p/19q co-deletion AUC of 0.85, and a grade AUC of 0.81 (grade II/III/IV). For the tumor delineation, we achieved a mean whole tumor Dice score of 0.84. CONCLUSIONS: We developed a method that non-invasively predicts multiple, clinically relevant features of glioma. Evaluation in an independent dataset shows that the method achieves a high performance and that it generalizes well to the broader clinical population. This first-of-its-kind method opens the door to more generalizable, instead of hyper-specialized, AI methods.

IDH1/2 wildtype gliomas grade 2 and 3 with molecular glioblastoma-like profile have a distinct course of epilepsy compared to IDH1/2 wildtype glioblastomas.

BACKGROUND: IDH1/2 wildtype (IDHwt) glioma WHO grade 2 and 3 patients with pTERT mutation and/or EGFR amplification and/or + 7/-10 chromosome gain/loss have a similar overall survival time as IDHwt glioblastoma patients, and are both considered glioblastoma IDHwt according to the WHO 2021 classification. However, differences in seizure onset have been observed. This study aimed to compare the course of epilepsy in the 2 glioblastoma subtypes. METHODS: We analyzed epilepsy data of an existing cohort including IDHwt histologically lower-grade glioma WHO grade 2 and 3 with molecular glioblastoma-like profile (IDHwt hLGG) and IDHwt glioblastoma patients. Primary outcome was the incidence proportion of epilepsy during the disease course. Secondary outcomes included, among others, onset of epilepsy, number of seizure days, and antiepileptic drug (AED) polytherapy. RESULTS: Out of 254 patients, 78% (50/64) IDHwt hLGG and 68% (129/190) IDHwt glioblastoma patients developed epilepsy during the disease (P = .121). Epilepsy onset before histopathological diagnosis occurred more frequently in IDHwt hLGG compared to IDHwt glioblastoma patients (90% vs 60%, P < .001), with a significantly longer median time to diagnosis (3.5 vs 1.3 months, P < .001). Median total seizure days was also longer for IDHwt hLGG patients (7.0 vs 3.0, P = .005), and they received more often AED polytherapy (32% vs 17%, P = .028). CONCLUSIONS: Although the incidence proportion of epilepsy during the entire disease course is similar, IDHwt hLGG patients show a significantly higher incidence of epilepsy before diagnosis and a significantly longer median time between first seizure and diagnosis compared to IDHwt glioblastoma patients, indicating a distinct clinical course.

The Erasmus Glioma Database (EGD): Structural MRI scans, WHO 2016 subtypes, and segmentations of 774 patients with glioma.

The Erasmus Glioma Database (EGD) contains structural magnetic resonance imaging (MRI) scans, genetic and histological features (specifying the WHO 2016 subtype), and whole tumor segmentations of patients with glioma. Pre-operative MRI data of 774 patients with glioma (281 female, 492 male, 1 unknown, age range 19-86 years) treated at the Erasmus MC between 2008 and 2018 is available. For all patients a pre-contrast T1-weighted, post-contrast T1-weighted, T2-weighted, and T2-weighted FLAIR scan are available, made on a variety of scanners from four different vendors. All scans are registered to a common atlas and defaced. Genetic and histological data consists of the IDH mutation status (available for 467 patients), 1p/19q co-deletion status (available for 259 patients), and grade (available for 716 patients). The full WHO 2016 subtype is available for 415 patients. Manual segmentations are available for 374 patients and automatically generated segmentations are available for 400 patients. The dataset can be used to relate the visual appearance of the tumor on the scan with the genetic and histological features, and to develop automatic segmentation methods.

Survival of diffuse astrocytic glioma, IDH1/2 wildtype, with molecular features of glioblastoma, WHO grade IV: a confirmation of the cIMPACT-NOW criteria.

BACKGROUND: The Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy (cIMPACT-NOW) has recommended that isocitrate dehydrogenase 1 and 2 wildtype (IDH1/2wt) diffuse lower-grade gliomas (LGGs) World Health Organization (WHO) grade II or III that present with (i) a telomerase reverse transcriptase promoter mutation (pTERTmt), and/or (ii) gain of chromosome 7 combined with loss of chromosome 10, and/or (iii) epidermal growth factor receptor (EGFR) amplification should be reclassified as diffuse astrocytic glioma, IDH1/2 wildtype, with molecular features of glioblastoma, WHO grade IV (IDH1/2wt astrocytomas WHO IV). This paper describes the overall survival (OS) of IDH1/2wt astrocytoma WHO IV patients, and more in detail patients with tumors with pTERTmt only. METHODS: In this retrospective multicenter study, we compared the OS of 71 IDH1/2wt astrocytomas WHO IV patients, with radiological characteristics of LGGs, with the OS of 197 IDH1/2wt glioblastoma patients. Moreover, we compared the OS of 22 pTERTmt only astrocytoma patients with the OS of the IDH1/2wt glioblastoma patients. RESULTS: Median OS was similar for IDH1/2wt astrocytoma WHO IV patients (23.8 mo) and IDH1/2wt glioblastoma patients (19.2 mo) (Cox proportional hazards model: hazard ratio [HR] 1.27, 95% CI: 0.85-1.88, P = 0.242). OS was also similar in patients with IDH1/2wt astrocytomas WHO IV, pTERTmt only, and IDH1/2wt glioblastomas (HR 1.15, 95% CI: 0.64-2.10, P = 0.641). CONCLUSIONS: The presented data confirm the cIMPACT-NOW recommendation and we propose that IDH1/2wt astrocytomas WHO IV in the absence of other qualifying mutations should be classified as IDH1/2wt glioblastomas.

Predicting the 1p/19q Codeletion Status of Presumed Low-Grade Glioma with an Externally Validated Machine Learning Algorithm.

PURPOSE: Patients with 1p/19q codeleted low-grade glioma (LGG) have longer overall survival and better treatment response than patients with 1p/19q intact tumors. Therefore, it is relevant to know the 1p/19q status. To investigate whether the 1p/19q status can be assessed prior to tumor resection, we developed a machine learning algorithm to predict the 1p/19q status of presumed LGG based on preoperative MRI. EXPERIMENTAL DESIGN: Preoperative brain MR images from 284 patients who had undergone biopsy or resection of presumed LGG were used to train a support vector machine algorithm. The algorithm was trained on the basis of features extracted from post-contrast T1-weighted and T2-weighted MR images and on patients' age and sex. The performance of the algorithm compared with tissue diagnosis was assessed on an external validation dataset of MR images from 129 patients with LGG from The Cancer Imaging Archive (TCIA). Four clinical experts also predicted the 1p/19q status of the TCIA MR images. RESULTS: The algorithm achieved an AUC of 0.72 in the external validation dataset. The algorithm had a higher predictive performance than the average of the neurosurgeons (AUC 0.52) but lower than that of the neuroradiologists (AUC of 0.81). There was a wide variability between clinical experts (AUC 0.45-0.83). CONCLUSIONS: Our results suggest that our algorithm can noninvasively predict the 1p/19q status of presumed LGG with a performance that on average outperformed the oncological neurosurgeons. Evaluation on an independent dataset indicates that our algorithm is robust and generalizable.

Differences in spatial distribution between WHO 2016 low-grade glioma molecular subgroups.

BACKGROUND: Several studies reported a correlation between anatomic location and genetic background of low-grade gliomas (LGGs). As such, tumor location may contribute to presurgical clinical decision-making. Our purpose was to visualize and compare the spatial distribution of different WHO 2016 gliomas, frequently aberrated single genes and DNA copy number alterations within subgroups, and groups of postoperative tumor volume. METHODS: Adult grade II glioma patients (WHO 2016 classified) diagnosed between 2003 and 2016 were included. Tumor volume and location were assessed with semi-automatic software. All volumes of interest were mapped to a standard reference brain. Location heatmaps were created for each WHO 2016 glioma subgroup, frequently aberrated single genes and copy numbers (CNVs), as well as heatmaps according to groups of postoperative tumor volume. Differences between subgroups were determined using voxelwise permutation testing. RESULTS: A total of 110 IDH mutated astrocytoma patients, 92 IDH mutated and 1p19q co-deleted oligodendroglioma patients, and 22 IDH wild-type astrocytoma patients were included. We identified small regions in which specific molecular subtypes occurred more frequently. IDH-mutated LGGs were more frequently located in the frontal lobes and IDH wild-type tumors more frequently in the basal ganglia of the right hemisphere. We found no localizations of significant difference for single genes/CNVs in subgroups, except for loss of 9p in oligodendrogliomas with a predilection for the left parietal lobes. More extensive resections in LGG were associated with frontal locations. CONCLUSIONS: WHO low-grade glioma subgroups show differences in spatial distribution. Our data may contribute to presurgical clinical decision-making in LGG patients.

Prognostic relevance of mutations and copy number alterations assessed with targeted next generation sequencing in IDH mutant grade II glioma.

BACKGROUND: At current prognostication of low grade glioma remains suboptimal and might be improved with additional markers. These may guide treatment decisions, in particular on early adjuvant therapy versus wait and see after surgery. METHODS: We used a targeted Next-Generation Sequencing panel to assess mutational and copy number status of selected genes and chromosomes in a consecutive series of adult grade II supratentorial glioma, and assessed the impact of molecular markers of interest on overall survival. RESULTS: 207 IDH mutated grade II glioma samples were analyzed with a median follow-up of 6.9 years. Loss of region 9p21.3 did not show a correlation with outcome in IDH mutated 1p/19q-codeleted oligodendroglioma or IDH mutated astrocytoma. We found a significant shorter overall survival with univariable analysis in IDH mutated astrocytoma patients with trisomy of chromosome 7 (Log rank P = 0.044) and in IDH mutated 1p/19q-codeleted oligodendroglioma patients with a PTEN mutation (Log rank P = 0.033). We could not validate these findings in multivariate analysis or in the TCGA dataset. CONCLUSIONS: Loss of 9p21.3 is not associated with outcome in a molecularly defined cohort of grade II glioma and therefore it remains unclear if loss of 9p21.3 can be used as additional marker of anaplasia or to guide treatment decisions. Trisomy of chromosome 7 in IDH mutated astrocytoma and PTEN mutations in IDH mutated oligodendroglioma are potential markers of poor prognosis, but require confirmation in larger series.

The T2-FLAIR mismatch sign as an imaging marker for non-enhancing IDH-mutant, 1p/19q-intact lower-grade glioma: a validation study.

Background: The purpose of this study was to assess the reproducibility of the previously described T2-fluid attenuated inversion recovery (FLAIR) mismatch sign as a specific imaging marker in non-enhancing isocitrate dehydrogenase (IDH) mutant, 1p/19q non-codeleted lower-grade glioma (LGG), encompassing both diffuse and anaplastic astrocytoma. Methods: MR scans (n = 154) from 3 separate databases with genotyped LGG were evaluated by 2 independent reviewers to assess (i) presence/absence of "T2-FLAIR mismatch" sign and (ii) presence/absence of homogeneous signal on T2-weighted images. Interrater agreement with Cohen's kappa (κ) was calculated, as well as diagnostic test performance of the T2-FLAIR mismatch sign to identify IDH-mutant astrocytoma. Results: There was substantial interrater agreement for the T2-FLAIR mismatch sign [κ = 0.75 (0.64-0.87)], but only fair agreement for T2 homogeneity [κ = 0.38 (0.25-0.52)]. The T2-FLAIR mismatch sign was present in 38 cases (25%) and had a positive predictive value of 100%, negative predictive value of 68%, a sensitivity of 51%, and a specificity of 100%. Conclusions: With a robust interrater agreement, our study confirms that among non-enhancing LGG the T2-FLAIR mismatch sign represents a highly specific imaging marker for IDH-mutant astrocytoma. This non-invasive marker may enable a more informed patient counsel and can aid in the treatment decision processes in a significant proportion of patients presenting with non-enhancing, LGG-like lesions.

The impact of surgery in molecularly defined low-grade glioma: an integrated clinical, radiological, and molecular analysis.

Background: Extensive resections in low-grade glioma (LGG) are associated with improved overall survival (OS). However, World Health Organization (WHO) classification of gliomas has been completely revised and is now predominantly based on molecular criteria. This requires reevaluation of the impact of surgery in molecularly defined LGG subtypes. Methods: We included 228 adults who underwent surgery since 2003 for a supratentorial LGG. Pre- and postoperative tumor volumes were assessed with semiautomatic software on T2-weighted images. Targeted next-generation sequencing was used to classify samples according to current WHO classification. Impact of postoperative volume on OS, corrected for molecular profile, was assessed using a Cox proportional hazards model. Results: Median follow-up was 5.79 years. In 39 (17.1%) histopathologically classified gliomas, the subtype was revised after molecular analysis. Complete resection was achieved in 35 patients (15.4%), and in 54 patients (23.7%) only small residue (0.1-5.0 cm3) remained. In multivariable analysis, postoperative volume was associated with OS, with a hazard ratio of 1.01 (95% CI: 1.002-1.02; P = 0.016) per cm3 increase in volume. The impact of postoperative volume was particularly strong in isocitrate dehydrogenase (IDH) mutated astrocytoma patients, where even very small postoperative volumes (0.1-5.0 cm) already negatively affected OS. Conclusion: Our data provide the necessary reevaluation of the impact of surgery in molecularly defined LGG and support maximal resection as first-line treatment for molecularly defined LGG. Importantly, in IDH mutated astrocytoma, even small postoperative volumes have negative impact on OS, which argues for a second-look operation in this subtype to remove minor residues if safely possible.

Does early resection of presumed low-grade glioma improve survival? A clinical perspective.

Early resection is standard of care for presumed low-grade gliomas. This is based on studies including only tumors that were post-surgically confirmed as low-grade glioma. Unfortunately this does not represent the clinicians' situation wherein he/she has to deal with a lesion on MRI that is suspect for low-grade glioma (i.e. without prior knowledge on the histological diagnosis). We therefore aimed to determine the optimal initial strategy for patients with a lesion suspect for low-grade glioma, but not histologically proven yet. We retrospectively identified 150 patients with a resectable presumed low-grade-glioma and who were otherwise in good clinical condition. In this cohort we compared overall survival between three types of initital treatment strategy: a wait-and-scan approach (n = 38), early resection (n = 83), or biopsy for histopathological verification (n = 29). In multivariate analysis, no difference was observed in overall survival for early resection compared to wait-and-scan: hazard ratio of 0.92 (95% CI 0.43-2.01; p = 0.85). However, biopsy strategy showed a shorter overall survival compared to wait-and-scan: hazard ratio of 2.69 (95% CI 1.19-6.06; p = 0.02). In this cohort we failed to confirm superiority of early resection over a wait-and-scan approach in terms of overall survival, though longer follow-up is required for final conclusion. Biopsy was associated with shorter overall survival.

PI3 kinase mutations and mutational load as poor prognostic markers in diffuse glioma patients.

INTRODUCTION: Recent advances in molecular diagnostics allow diffuse gliomas to be classified based on their genetic changes into distinct prognostic subtypes. However, a systematic analysis of all molecular markers has thus far not been performed; most classification schemes use a predefined and select set of genes/molecular markers. Here, we have analysed the TCGA dataset (combined glioblastoma (GBM) and lower grade glioma (LGG) datasets) to identify all prognostic genetic markers in diffuse gliomas in order to generate a comprehensive classification scheme. RESULTS: Of the molecular markers investigated (all genes mutated at a population frequency >1.7 % and frequent chromosomal imbalances) in the entire glioma dataset, 57 were significantly associated with overall survival. Of these, IDH1 or IDH2 mutations are associated with lowest hazard ratio, which confirms IDH as the most important prognostic marker in diffuse gliomas. Subsequent subgroup analysis largely confirms many of the currently used molecular classification schemes for diffuse gliomas (ATRX or TP53 mutations, 1p19q codeletion). Our analysis also identified PI3-kinase mutations as markers of poor prognosis in IDH-mutated + ATRX/TP53 mutated diffuse gliomas, median survival 3.7 v. 6.3 years (P = 0.02, Hazard rate (HR) 2.93, 95 % confidence interval (CI) 1.16 - 7.38). PI3-kinase mutations were also prognostic in two independent datasets. In our analysis, no additional molecular markers were identified that further refine the molecular classification of diffuse gliomas. Interestingly, these molecular classifiers do not fully explain the variability in survival observed for diffuse glioma patients. We demonstrate that tumor grade remains an important prognostic factor for overall survival in diffuse gliomas, even within molecular glioma subtypes. Tumor grade was correlated with the mutational load (the number of non-silent mutations) of the tumor: grade II diffuse gliomas harbour fewer genetic changes than grade III or IV, even within defined molecular subtypes (e.g. ATRX mutated diffuse gliomas). CONCLUSION: We have identified PI3K mutations as novel prognostic markers in gliomas. We also demonstrate that the mutational load is associated with tumor grade. The increase in mutational load may partially explain the increased aggressiveness of higher grade diffuse gliomas when a subset of the affected genes actively contributes to gliomagenesis and/or progression.