Development of End-to-End AI-Based MRI Image Analysis System for Predicting IDH Mutation Status of Patients with Gliomas: Multicentric Validation.

Radiogenomics has shown potential to predict genomic phenotypes from medical images. The development of models using standard-of-care pre-operative MRI images, as opposed to advanced MRI images, enables a broader reach of such models. In this work, a radiogenomics model for IDH mutation status prediction from standard-of-care MRIs in patients with glioma was developed and validated using multicentric data. A cohort of 142 (wild-type: 32.4%) patients with glioma retrieved from the TCIA/TCGA was used to train a logistic regression model to predict the IDH mutation status. The model was evaluated using retrospective data collected in two distinct hospitals, comprising 36 (wild-type: 63.9%) and 53 (wild-type: 75.5%) patients. Model development utilized ROC analysis. Model discrimination and calibration were used for validation. The model yielded an AUC of 0.741 vs. 0.716 vs. 0.938, a sensitivity of 0.784 vs. 0.739 vs. 0.875, and a specificity of 0.657 vs. 0.692 vs. 1.000 on the training, test cohort 1, and test cohort 2, respectively. The assessment of model fairness suggested an unbiased model for age and sex, and calibration tests showed a p < 0.05. These results indicate that the developed model allows the prediction of the IDH mutation status in gliomas using standard-of-care MRI images and does not appear to hold sex and age biases.

Preoperative neurocognitive function as an independent survival prognostic marker in primary glioblastoma.

Background: Aim of the present study is to investigate whether preoperative neurocognitive status is prognostically associated with overall survival (OS) in newly diagnosed glioblastoma (GBM) patients. Methods: Ninety patients with dominant-hemisphere IDH-wild-type GBM were assessed by Mini Mental Status Exam (MMSE), Trail Making Test (TMT) A and B parts, and Control Word Association Test (COWAT) phonemic and semantic subtests. Demographics, Karnofsky Performance Scale, tumor parameters, type of surgery, and adjuvant therapy data were available for patients. Results: According to Cox proportional hazards model the neurocognitive variables of TMT B (P < .01), COWAT semantic subset (P < .05), and the MMSE (P < .01) were found significantly associated with survival prediction. From all other factors, only tumor volume and operation type (debulking vs biopsy) showed a statistical association (P < .05) with survival prediction. Kaplan Meier Long rank test showed statistical significance (P < .01) between unimpaired and impaired groups for TMT B, with median survival for the unimpaired group 26 months and 10 months for the impaired group, for COWAT semantic (P < .01) with median survival 23 months and 12 months, respectively and for MMSE (P < .01) with medial survival 19 and 12 months respectively. Conclusions: Our study demonstrates that neurocognitive status at baseline-prior to treatment-is an independent prognostic factor for OS in wild-type GBM patients, adding another prognostic tool to assist physicians in selecting the best treatment plan.

Association between preoperative neurocognitive status and IDH1 mutation status in high-grade gliomas.

Background: High-grade glioma (HGG) patients present with variable impairment in neurocognitive function (NCF). Based on that, isocitrate dehydrogenase 1 (IDH1) wild-type HGGs are more aggressive than IDH1 mutant-type ones, we hypothesized that patients with IDH1 wild-type HGG would exhibit more severe NCF deficits than their IDH1 mutant counterparts. Methods: NCF was assessed by Mini Mental Status Exam (MMSE), Trail Making Test (TMT), Digit Span (DS), and Controlled Word Association Test (COWAT) tests in 147 HGG patients preoperatively. Results: Analyses between IDH1 groups revealed a significant difference on MMSE concentration component (p ≤ .01), DS (p ≤ .01), TMTB (p ≤ .01), and COWAT (p ≤ .01) scores, with the IDH1 wild group performing worse than the IDH1 mutant one. Age and tumor volume were inversely correlated with MMSE concentration component (r = -4.78, p < .01), and with MMSE concentration (r = -.401, p < .01), TMTB (r = -.328, p < .01), and COWAT phonemic scores (r = -.599, p < .01), respectively, but only for the IDH1 wild-type group. Analyses between age-matched subsamples of IDH1 groups revealed no age effect on NCF. Tumor grade showed nonsignificance on NCF (p > .05) between the 2 IDH1 mutation subgroups of grade IV tumor patients. On the contrary, grade III group showed a significant difference in TMTB (p < .01) and DS backwards (p < .01) between IDH1 subgroups, with the mutant one outperforming the IDH1 wild one. Conclusions: Our findings indicate that IDH1 wild-type HGG patients present greater NCF impairment, in executive functions particularly, compared to IDH1 mutant ones, suggesting that tumor growth kinetics may play a more profound role than other tumor and demographic parameters in clinical NCF of HGG patients.

Neuroscience20 (BRAIN20, SPINE20, and MENTAL20) Health Initiative: A Global Consortium Addressing the Human and Economic Burden of Brain, Spine, and Mental Disorders Through Neurotech Innovations and Policies.

Neurological disorders significantly impact the world's economy due to their often chronic and life-threatening nature afflicting individuals which, in turn, creates a global disease burden. The Group of Twenty (G20) member nations, which represent the largest economies globally, should come together to formulate a plan on how to overcome this burden. The Neuroscience-20 (N20) initiative of the Society for Brain Mapping and Therapeutics (SBMT) is at the vanguard of this global collaboration to comprehensively raise awareness about brain, spine, and mental disorders worldwide. This paper aims to provide a comprehensive review of the various brain initiatives worldwide and highlight the need for cooperation and recommend ways to bring down costs associated with the discovery and treatment of neurological disorders. Our systematic search revealed that the cost of neurological and psychiatric disorders to the world economy by 2030 is roughly $16T. The cost to the economy of the United States is $1.5T annually and growing given the impact of COVID-19. We also discovered there is a shortfall of effective collaboration between nations and a lack of resources in developing countries. Current statistical analyses on the cost of neurological disorders to the world economy strongly suggest that there is a great need for investment in neurotechnology and innovation or fast-tracking therapeutics and diagnostics to curb these costs. During the current COVID-19 pandemic, SBMT, through this paper, intends to showcase the importance of worldwide collaborations to reduce the population's economic and health burden, specifically regarding neurological/brain, spine, and mental disorders.

The Open Brain Consent: Informing research participants and obtaining consent to share brain imaging data.

Having the means to share research data openly is essential to modern science. For human research, a key aspect in this endeavor is obtaining consent from participants, not just to take part in a study, which is a basic ethical principle, but also to share their data with the scientific community. To ensure that the participants' privacy is respected, national and/or supranational regulations and laws are in place. It is, however, not always clear to researchers what the implications of those are, nor how to comply with them. The Open Brain Consent (https://open-brain-consent.readthedocs.io) is an international initiative that aims to provide researchers in the brain imaging community with information about data sharing options and tools. We present here a short history of this project and its latest developments, and share pointers to consent forms, including a template consent form that is compliant with the EU general data protection regulation. We also share pointers to an associated data user agreement that is not only useful in the EU context, but also for any researchers dealing with personal (clinical) data elsewhere.

Dissociation between visuospatial neglect assessment tasks and its neuroanatomical substrates: a case report.

Visuospatial neglect possesses significant heterogeneity in clinical features and neuroanatomical substrates. Behavioral dissociations on different neglect tasks have been reported in the past, and the investigation of their respective anatomical correlates at cortical and, to a lesser degree, subcortical levels has been attempted in stroke studies. We report a patient with a neoplasm occupying the right ventral post-central gyrus and anterior supramarginal gyrus. The patient was admitted preoperatively with dissociation on the performance of neglect tasks, showing clinical deficits in the line bisection task and clock drawing, but not on the cancelation task. The patient underwent an awake craniotomy for tumor excision. Intraoperative visuospatial mapping was employed by applying direct electrical stimulation (DES) to the supramarginal gyrus and the ventral branch of the superior longitudinal fasciculus (SLF III) during the line bisection task. According to our findings, DES was ineffective at the cortical level, but it induced strong rightward bias when applied subcortically at the SLF III. By combining our preoperative and intraoperative anatomical and clinical data, we suggest that the posterior part of the SLF III might have a distinct role in the perceptual component of neglect. Our findings are discussed within the context of previous literature supporting the notion that particular behavioral features of spatial neglect are mediated by different white-matter connections.

Machine learning assisted DSC-MRI radiomics as a tool for glioma classification by grade and mutation status.

BACKGROUND: Combining MRI techniques with machine learning methodology is rapidly gaining attention as a promising method for staging of brain gliomas. This study assesses the diagnostic value of such a framework applied to dynamic susceptibility contrast (DSC)-MRI in classifying treatment-naïve gliomas from a multi-center patients into WHO grades II-IV and across their isocitrate dehydrogenase (IDH) mutation status. METHODS: Three hundred thirty-three patients from 6 tertiary centres, diagnosed histologically and molecularly with primary gliomas (IDH-mutant = 151 or IDH-wildtype = 182) were retrospectively identified. Raw DSC-MRI data was post-processed for normalised leakage-corrected relative cerebral blood volume (rCBV) maps. Shape, intensity distribution (histogram) and rotational invariant Haralick texture features over the tumour mask were extracted. Differences in extracted features across glioma grades and mutation status were tested using the Wilcoxon two-sample test. A random-forest algorithm was employed (2-fold cross-validation, 250 repeats) to predict grades or mutation status using the extracted features. RESULTS: Shape, distribution and texture features showed significant differences across mutation status. WHO grade II-III differentiation was mostly driven by shape features while texture and intensity feature were more relevant for the III-IV separation. Increased number of features became significant when differentiating grades further apart from one another. Gliomas were correctly stratified by mutation status in 71% and by grade in 53% of the cases (87% of the gliomas grades predicted with distance less than 1). CONCLUSIONS: Despite large heterogeneity in the multi-center dataset, machine learning assisted DSC-MRI radiomics hold potential to address the inherent variability and presents a promising approach for non-invasive glioma molecular subtyping and grading.

Motor and language deficits correlate with resting state functional magnetic resonance imaging networks in patients with brain tumors.

BACKGROUND AND PURPOSE: Evidence of pre-operative resting state functional magnetic resonance (RS-fMRI) validation by correlating it with clinical pre-operative status in brain tumor patients is scarce. Our aim was to validate the functional relevance of RS-fMRI by investigating the association between RS-fMRI and pre-operative motor and language function performance in patients with brain tumor. MATERIALS AND METHODS: Sixty-nine patients with brain tumors were prospectively recruited. Patients with tumors near precentral gyrus (n = 49) underwent assessment for apparent (paresis) and subtle (finger tapping) deficits. Patients with left frontal tumors in the vicinity of the inferior frontal gyrus (n = 29) underwent assessment for gross (aphasia) and mild language (phonological verbal fluency) deficits. RS-fMRI results were extracted by spatial independent component analysis (ICA). RESULTS: Motor group: paretic patients showed significantly (P = 0.01) decreased BOLD signal in ipsilesional precentral gyrus when compared to contralesional one. Significantly (P < 0.01) lower BOLD signal was also observed in ipsilesional precentral gyrus of paretics when compared with the non-paretics. In asymptomatic patients, a strong positive correlation (r = 0.68, P < 0.01) between ipsilesional motor cortex BOLD signal and contralesional finger tapping performance was observed. Language group: patients with aphasia showed significantly (P = 0.01) decreased RS-fMRI BOLD signal in left BA 44 when compared with non- aphasics. In asymptomatic patients, a strong positive correlation (r = 0.72, P < 0.01) between BA 44 BOLD signal and phonological fluency performance was observed. CONCLUSIONS: Our results showed that RS-fMRI BOLD signal of motor and language networks were significantly affected by the tumors implying the usefulness of the method for assessment of the underlying functions in brain tumors patients.

Texture analysis- and support vector machine-assisted diffusional kurtosis imaging may allow in vivo gliomas grading and IDH-mutation status prediction: a preliminary study.

We sought to investigate, whether texture analysis of diffusional kurtosis imaging (DKI) enhanced by support vector machine (SVM) analysis may provide biomarkers for gliomas staging and detection of the IDH mutation. First-order statistics and texture feature extraction were performed in 37 patients on both conventional (FLAIR) and mean diffusional kurtosis (MDK) images and recursive feature elimination (RFE) methodology based on SVM was employed to select the most discriminative diagnostic biomarkers. The first-order statistics demonstrated significantly lower MDK values in the IDH-mutant tumors. This resulted in 81.1% accuracy (sensitivity = 0.96, specificity = 0.45, AUC 0.59) for IDH mutation diagnosis. There were non-significant differences in average MDK and skewness among the different tumour grades. When texture analysis and SVM were utilized, the grading accuracy achieved by DKI biomarkers was 78.1% (sensitivity 0.77, specificity 0.79, AUC 0.79); the prediction accuracy for IDH mutation reached 83.8% (sensitivity 0.96, specificity 0.55, AUC 0.87). For the IDH mutation task, DKI outperformed significantly the FLAIR imaging. When using selected biomarkers after RFE, the prediction accuracy achieved 83.8% (sensitivity 0.92, specificity 0.64, AUC 0.88). These findings demonstrate the superiority of DKI enhanced by texture analysis and SVM, compared to conventional imaging, for gliomas staging and prediction of IDH mutational status.

Possible common neurological breakdowns for alexithymia and humour appreciation deficit: A case study.

This brief work is an attempt to point to the possible common neurological breakdowns in giving rise to alexithymia, and impaired appreciation of humour. In particular, we present the case of a patient who lost the ability to enjoy humour after the surgical removal of a frontal groove meningioma, although he was still able to detect it, while at the same time was diagnosed with organic alexithymia. Our results indicate that problems in the affective appreciation of humour and in emotionalizing (alexithymic symptoms) may be the result of damage to the ventral-rostral portions of the ACG/mPFC, which prevent the patient from assessing the salience of emotion and motivational information, and generating emotional reactions; as a result he has trouble experiencing emotions, knowing how he and others feel, and enjoy humour.

Prognostic value of preoperative dynamic contrast-enhanced MRI perfusion parameters for high-grade glioma patients.

INTRODUCTION: The prognostic value of the dynamic contrast-enhanced (DCE) MRI perfusion and its histogram analysis-derived metrics is not well established for high-grade glioma (HGG) patients. The aim of this prospective study was to investigate DCE perfusion transfer coefficient (Ktrans), vascular plasma volume fraction (vp), extracellular volume fraction (ve), reverse transfer constant (kep), and initial area under gadolinium concentration time curve (IAUGC) as predictors of progression-free (PFS) and overall survival (OS) in HGG patients. METHODS: Sixty-nine patients with suspected anaplastic astrocytoma or glioblastoma underwent preoperative DCE-MRI scans. DCE perfusion whole tumor region histogram parameters, clinical details, and PFS and OS data were obtained. Univariate, multivariate, and Kaplan-Meier survival analyses were conducted. Receiver operating characteristic (ROC) curve analysis was employed to identify perfusion parameters with the best differentiation performance. RESULTS: On univariate analysis, ve and skewness of vp had significant negative impacts, while kep had significant positive impact on OS (P < 0.05). ve was also a negative predictor of PFS (P < 0.05). Patients with lower ve and IAUGC had longer median PFS and OS on Kaplan-Meier analysis (P < 0.05). Ktrans and ve could also differentiate grade III from IV gliomas (area under the curve 0.819 and 0.791, respectively). CONCLUSIONS: High ve is a consistent predictor of worse PFS and OS in HGG glioma patients. vp skewness and kep are also predictive for OS. Ktrans and ve demonstrated the best diagnostic performance for differentiating grade III from IV gliomas.

Optimal differentiation of high- and low-grade glioma and metastasis: a meta-analysis of perfusion, diffusion, and spectroscopy metrics.

INTRODUCTION: To perform a meta-analysis of advanced magnetic resonance imaging (MRI) metrics, including relative cerebral blood volume (rCBV), normalized apparent diffusion coefficient (nADC), and spectroscopy ratios choline/creatine (Cho/Cr) and choline/N-acetyl aspartate (Cho/NAA), for the differentiation of high- and low-grade gliomas (HGG, LGG) and metastases (MTS). METHODS: For systematic review, 83 articles (dated 2000-2013) were selected from the NCBI database. Twenty-four, twenty-two, and eight articles were included respectively for spectroscopy, rCBV, and nADC meta-analysis. In the meta-analysis, we calculated overall means for rCBV, nADC, Cho/Cr (short TE-from 20 to 35 ms, medium-from 135 to 144 ms), and Cho/NAA for the HGG, LGG, and MTS groups. We used random effects model to obtain weighted averages and select thresholds. RESULTS: Overall means (with 95% CI) for rCBV, nADC, Cho/Cr (short and medium echo time, TE), and Cho/NAA were: for HGG 5.47 (4.78-6.15), 1.38 (1.16-1.60), 2.40 (1.67-3.13), 3.27 (2.78-3.77), and 4.71 (3.24-6.19); for LGG 2.00 (1.71-2.28), 1.61 (1.36-1.87), 1.46 (1.20-1.72), 1.71 (1.49-1.93), and 2.36 (1.50-3.23); for MTS 5.06 (3.85-6.27), 1.35 (1.06-1.64), 1.89 (1.72-2.06), 3.14 (1.57-4.72), (Cho/NAA was not available). LGG had significantly lower rCBV, Cho/Cr, and Cho/NAA values than HGG or MTS. No significant differences were found for nADC. CONCLUSIONS: Best differentiation between HGG and LGG is obtained from rCBV, Cho/Cr, and Cho/NAA metrics. MTS could not be reliably distinguished from HGG by the methods investigated.