Artificial Intelligence for the Detection of Focal Cortical Dysplasia: Challenges in Translating Algorithms into Clinical Practice

Focal cortical dysplasias (FCDs) are malformations of cortical development and one of the most common pathologies causing pharmacoresistant focal epilepsy. Resective neurosurgery yields high success rates, especially if the full extent of the lesion is correctly identified and completely removed. The visual assessment of magnetic resonance imaging does not pinpoint the FCD in 30%–50% of cases, and half of all patients with FCD are not amenable to epilepsy surgery, partly because the FCD could not be sufficiently localized. Computational approaches to FCD detection are an active area of research, benefitting from advancements in computer vision. Automatic FCD detection is a significant challenge and one of the first clinical grounds where the application of artificial intelligence may translate into an advance for patients' health. The emergence of new methods from the combination of health and computer sciences creates novel challenges. Imaging data need to be organized into structured, well-annotated datasets and combined with other clinical information, such as histopathological subtypes or neuroimaging characteristics. Algorithmic output, that is, model prediction, requires a technically correct evaluation with adequate metrics that are understandable and usable for clinicians. Publication of code and data is necessary to make research accessible and reproducible. This critical review introduces the field of automatic FCD detection, explaining underlying medical and technical concepts, highlighting its challenges and current limitations, and providing a perspective for a novel research environment

Advances in MRI Assessment of Gliomas and Response to Anti-VEGF Therapy

Bevacizumab is thought to normalize tumor vasculature and restore the blood–brain barrier, decreasing enhancement and peritumoral edema. Conventional measurements of tumor response rely upon dimensions of enhancing tumor. After bevacizumab treatment, glioblastomas are more prone to progress as nonenhancing tumor. The RANO (Response Assessment in Neuro-Oncology) criteria for glioma response use fluid-attenuated inversion recovery (FLAIR)/T2 hyperintensity as a surrogate for nonenhancing tumor; however, nonenhancing tumor can be difficult to differentiate from other causes of FLAIR/T2 hyperintensity (eg, radiation-induced gliosis). Due to these difficulties, recent efforts have been directed toward identifying new biomarkers that either predict treatment response or accurately measure response of both enhancing and nonenhancing tumor shortly after treatment initiation. This will allow for earlier treatment decisions, saving patients from the adverse effects of ineffective therapies while allowing them to try alternative therapies sooner. An active area of research is the use of physiologic imaging, which can potentially detect treatment effects before changes in tumor size are evident

Molecular matched targeted therapies for primary brain tumors—a single center retrospective analysis

PURPOSE: Molecular diagnostics including next generation gene sequencing are increasingly used to determine options for individualized therapies in brain tumor patients. We aimed to evaluate the decision-making process of molecular targeted therapies and analyze data on tolerability as well as signals for efficacy. METHODS: Via retrospective analysis, we identified primary brain tumor patients who were treated off-label with a targeted therapy at the University Hospital Frankfurt, Goethe University. We analyzed which types of molecular alterations were utilized to guide molecular off-label therapies and the diagnostic procedures for their assessment during the period from 2008 to 2021. Data on tolerability and outcomes were collected. RESULTS: 413 off-label therapies were identified with an increasing annual number for the interval after 2016. 37 interventions (9%) were targeted therapies based on molecular markers. Glioma and meningioma were the most frequent entities treated with molecular matched targeted therapies. Rare entities comprised e.g. medulloblastoma and papillary craniopharyngeoma. Molecular targeted approaches included checkpoint inhibitors, inhibitors of mTOR, FGFR, ALK, MET, ROS1, PIK3CA, CDK4/6, BRAF/MEK and PARP. Responses in the first follow-up MRI were partial response (13.5%), stable disease (29.7%) and progressive disease (46.0%). There were no new safety signals. Adverse events with fatal outcome (CTCAE grade 5) were not observed. Only, two patients discontinued treatment due to side effects. Median progression-free and overall survival were 9.1/18 months in patients with at least stable disease, and 1.8/3.6 months in those with progressive disease at the first follow-up MRI. CONCLUSION: A broad range of actionable alterations was targeted with available molecular therapeutics. However, efficacy was largely observed in entities with paradigmatic oncogenic drivers, in particular with BRAF mutations. Further research on biomarker-informed molecular matched therapies is urgently necessary. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11060-022-04049-w

Comparative analysis of machine learning algorithms for multi-syndrome classification of neurodegenerative syndromes

Importance: The entry of artificial intelligence into medicine is pending. Several methods have been used for the predictions of structured neuroimaging data, yet nobody compared them in this context. Objective: Multi-class prediction is key for building computational aid systems for differential diagnosis. We compared support vector machine, random forest, gradient boosting, and deep feed-forward neural networks for the classification of different neurodegenerative syndromes based on structural magnetic resonance imaging. Design, setting, and participants: Atlas-based volumetry was performed on multi-centric T1-weighted MRI data from 940 subjects, i.e., 124 healthy controls and 816 patients with ten different neurodegenerative diseases, leading to a multi-diagnostic multi-class classification task with eleven different classes. Interventions: N.A. Main outcomes and measures: Cohen's kappa, accuracy, and F1-score to assess model performance. Results: Overall, the neural network produced both the best performance measures and the most robust results. The smaller classes however were better classified by either the ensemble learning methods or the support vector machine, while performance measures for small classes were comparatively low, as expected. Diseases with regionally specific and pronounced atrophy patterns were generally better classified than diseases with widespread and rather weak atrophy. Conclusions and relevance: Our study furthermore underlines the necessity of larger data sets but also calls for a careful consideration of different machine learning methods that can handle the type of data and the classification task best

Management of Low-Grade Glioma

The optimal management of patients with low-grade glioma (LGG) is controversial. The controversy largely stems from the lack of well-designed clinical trials with adequate follow-up to account for the relatively long progression-free survival and overall survival of patients with LGG. Nonetheless, the literature increasingly suggests that expectant management is no longer optimal. Rather, there is mounting evidence supporting active management including consideration of surgical resection, radiotherapy, chemotherapy, molecular and histopathologic characterization, and use of modern imaging techniques for monitoring and prognostication. In particular, there is growing evidence favoring extensive surgical resection and increasing interest in the role of chemotherapy (especially temozolomide) in the management of these tumors. In this review, we critically analyze emerging trends in the literature with respect to management of LGG, with particular emphasis on reports published during the past year

Elevated Pontine and Putamenal GABA Levels in Mild-Moderate Parkinson Disease Detected by 7 Tesla Proton MRS

Background: Parkinson disease (PD) is characterized by the degeneration of nigrostriatal dopaminergic neurons. However, postmortem evidence indicates that the pathology of lower brainstem regions, such as the pons and medulla, precedes nigral involvement. Consistently, pontomedullary damage was implicated by structural and PET imaging in early PD. Neurochemical correlates of this early pathological involvement in PD are unknown. Methodology/Principal Finding: To map biochemical alterations in the brains of individuals with mild-moderate PD we quantified neurochemical profiles of the pons, putamen and substantia nigra by 7 tesla (T) proton magnetic resonance spectroscopy. Thirteen individuals with idiopathic PD (Hoehn & Yahr stage 2) and 12 age- and gender-matched healthy volunteers participated in the study. c-Aminobutyric acid (GABA) concentrations in the pons and putamen were significantly higher in patients (N = 11, off medications) than controls (N = 11, p,0.001 for pons and p,0.05 for putamen). The GABA elevation was more pronounced in the pons (64%) than in the putamen (32%). No other neurochemical differences were observed between patients and controls. Conclusion/Significance: The GABA elevation in the putamen is consistent with prior postmortem findings in patients with PD, as well as with in vivo observations in a rodent model of PD, while the GABA finding in the pons is novel. The more significant GABA elevation in the pons relative to the putamen is consistent with earlier pathological involvement of th

Development of randomized trials in adults with medulloblastoma - the example of EORTC 1634-BTG/NOA-23

Simple Summary Medulloblastoma is rare after puberty. Among several molecular subgroups that have been described, the sonic hedgehog (SHH) subgroup is highly overrepresented in the post-pubertal population and can be targeted with smoothened (SMO) inhibitors. However, no practice-changing prospective clinical trials have been published in adults to date. Tumors often recur, and treatment toxicity is relevant. Thus, the EORTC 1634-BTG/NOA-23 trial for post-pubertal patients with standard risk medulloblastoma will aim to increase treatment efficacy and to decrease treatment toxicity. Patients will be randomized between standard-dose vs. reduced-dosed radiotherapy, and SHH-subgroup patients will also be randomized between the SMO inhibitor sonidegib (Odomzo(TM,), Sun Pharmaceuticals Industries, Inc., New York, USA) in addition to standard radio-chemotherapy vs. standard radio-chemotherapy alone. In ancillary studies, we will investigate tumor tissue, blood and cerebrospinal fluid samples, magnetic resonance images, and radiotherapy plans to gain information that may improve future treatment. Patients will also be monitored long-term for late side effects of therapy, health-related quality of life, cognitive function, social and professional live outcomes, and reproduction and fertility. In summary, EORTC 1634-BTG/NOA-23 is a unique multi-national effort that will help to council patients and clinical scientists for the appropriate design of treatments and future clinical trials for post-pubertal patients with medulloblastoma. Medulloblastoma is a rare brain malignancy. Patients after puberty are rare and bear an intermediate prognosis. Standard treatment consists of maximal resection plus radio-chemotherapy. Treatment toxicity is high and produces disabling long-term side effects. The sonic hedgehog (SHH) subgroup is highly overrepresented in the post-pubertal and adult population and can be targeted by smoothened (SMO) inhibitors. No practice-changing prospective randomized data have been generated in adults. The EORTC 1634-BTG/NOA-23 trial will randomize patients between standard-dose vs. reduced-dosed craniospinal radiotherapy and SHH-subgroup patients between the SMO inhibitor sonidegib (Odomzo(TM), Sun Pharmaceuticals Industries, Inc., New York, USA) in addition to standard radio-chemotherapy vs. standard radio-chemotherapy alone to improve outcomes in view of decreased radiotherapy-related toxicity and increased efficacy. We will further investigate tumor tissue, blood, and cerebrospinal fluid as well as magnetic resonance imaging and radiotherapy plans to generate information that helps to further improve treatment outcomes. Given that treatment side effects typically occur late, long-term follow-up will monitor classic side effects of therapy, but also health-related quality of life, cognition, social and professional outcome, and reproduction and fertility. In summary, we will generate unprecedented data that will be translated into treatment changes in post-pubertal patients with medulloblastoma and will help to design future clinical trials.Neurolog