Structural- and DTI- MRI enable automated prediction of IDH Mutation Status in CNS WHO Grade 2-4 glioma patients: a deep Radiomics Approach.

BACKGROUND: The role of isocitrate dehydrogenase (IDH) mutation status for glioma stratification and prognosis is established. While structural magnetic resonance image (MRI) is a promising biomarker, it may not be sufficient for non-invasive characterisation of IDH mutation status. We investigated the diagnostic value of combined diffusion tensor imaging (DTI) and structural MRI enhanced by a deep radiomics approach based on convolutional neural networks (CNNs) and support vector machine (SVM), to determine the IDH mutation status in Central Nervous System World Health Organization (CNS WHO) grade 2-4 gliomas. METHODS: This retrospective study analyzed the DTI-derived fractional anisotropy (FA) and mean diffusivity (MD) images and structural images including fluid attenuated inversion recovery (FLAIR), non-enhanced T1-, and T2-weighted images of 206 treatment-naïve gliomas, including 146 IDH mutant and 60 IDH-wildtype ones. The lesions were manually segmented by experienced neuroradiologists and the masks were applied to the FA and MD maps. Deep radiomics features were extracted from each subject by applying a pre-trained CNN and statistical description. An SVM classifier was applied to predict IDH status using imaging features in combination with demographic data. RESULTS: We comparatively assessed the CNN-SVM classifier performance in predicting IDH mutation status using standalone and combined structural and DTI-based imaging features. Combined imaging features surpassed stand-alone modalities for the prediction of IDH mutation status [area under the curve (AUC) = 0.846; sensitivity = 0.925; and specificity = 0.567]. Importantly, optimal model performance was noted following the addition of demographic data (patients' age) to structural and DTI imaging features [area under the curve (AUC) = 0.847; sensitivity = 0.911; and specificity = 0.617]. CONCLUSIONS: Imaging features derived from DTI-based FA and MD maps combined with structural MRI, have superior diagnostic value to that provided by standalone structural or DTI sequences. In combination with demographic information, this CNN-SVM model offers a further enhanced non-invasive prediction of IDH mutation status in gliomas.

The role of DSC MR perfusion in predicting IDH mutation and 1p19q codeletion status in gliomas: meta-analysis and technical considerations.

PURPOSE: Isocitrate dehydrogenase (IDH) mutation and 1p19q codeletion status are important for managing glioma patients. However, current practice dictates invasive tissue sampling for histomolecular classification. We investigated the current value of dynamic susceptibility contrast (DSC) MR perfusion imaging as a tool for the non-invasive identification of these biomarkers. METHODS: A systematic search of PubMed, Medline, and Embase up to 2023 was performed, and meta-analyses were conducted. We removed studies employing machine learning models or using multiparametric imaging. We used random-effects standardized mean difference (SMD) and bivariate sensitivity-specificity meta-analyses, calculated the area under the hierarchical summary receiver operating characteristic curve (AUC) and performed meta-regressions using technical acquisition parameters (e.g., time to echo [TE], repetition time [TR]) as moderators to explore sources of heterogeneity. For all estimates, 95% confidence intervals (CIs) are provided. RESULTS: Sixteen eligible manuscripts comprising 1819 patients were included in the quantitative analyses. IDH mutant (IDHm) gliomas had lower rCBV values compared to their wild-type (IDHwt) counterparts. The highest SMD was observed for rCBVmean, rCBVmax, and rCBV 75th percentile (SMD≈ - 0.8, 95% CI ≈ [- 1.2, - 0.5]). In meta-regression, shorter TEs, shorter TRs, and smaller slice thicknesses were linked to higher absolute SMDs. When discriminating IDHm from IDHwt, the highest pooled specificity was observed for rCBVmean (82% [72, 89]), and the highest pooled sensitivity (i.e., 92% [86, 93]) and AUC (i.e., 0.91) for rCBV 10th percentile. In the bivariate meta-regression, shorter TEs and smaller slice gaps were linked to higher pooled sensitivities. In IDHm, 1p19q codeletion was associated with higher rCBVmean (SMD = 0.9 [0.2, 1.5]) and rCBV 90th percentile (SMD = 0.9 [0.1, 1.7]) values. CONCLUSIONS: Identification of vascular signatures predictive of IDH and 1p19q status is a novel promising application of DSC perfusion. Standardization of acquisition protocols and post-processing of DSC perfusion maps are warranted before widespread use in clinical practice.

Multicenter DSC-MRI-Based Radiomics Predict IDH Mutation in Gliomas.

To address the current lack of dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI)-based radiomics to predict isocitrate dehydrogenase (IDH) mutations in gliomas, we present a multicenter study that featured an independent exploratory set for radiomics model development and external validation using two independent cohorts. The maximum performance of the IDH mutation status prediction on the validation set had an accuracy of 0.544 (Cohen's kappa: 0.145, F1-score: 0.415, area under the curve-AUC: 0.639, sensitivity: 0.733, specificity: 0.491), which significantly improved to an accuracy of 0.706 (Cohen's kappa: 0.282, F1-score: 0.474, AUC: 0.667, sensitivity: 0.6, specificity: 0.736) when dynamic-based standardization of the images was performed prior to the radiomics. Model explainability using local interpretable model-agnostic explanations (LIME) and Shapley additive explanations (SHAP) revealed potential intuitive correlations between the IDH-wildtype increased heterogeneity and the texture complexity. These results strengthened our hypothesis that DSC-MRI radiogenomics in gliomas hold the potential to provide increased predictive performance from models that generalize well and provide understandable patterns between IDH mutation status and the extracted features toward enabling the clinical translation of radiogenomics in neuro-oncology.

Longitudinal structural and perfusion MRI enhanced by machine learning outperforms standalone modalities and radiological expertise in high-grade glioma surveillance.

PURPOSE: Surveillance of patients with high-grade glioma (HGG) and identification of disease progression remain a major challenge in neurooncology. This study aimed to develop a support vector machine (SVM) classifier, employing combined longitudinal structural and perfusion MRI studies, to classify between stable disease, pseudoprogression and progressive disease (3-class problem). METHODS: Study participants were separated into two groups: group I (total cohort: 64 patients) with a single DSC time point and group II (19 patients) with longitudinal DSC time points (2-3). We retrospectively analysed 269 structural MRI and 92 dynamic susceptibility contrast perfusion (DSC) MRI scans. The SVM classifier was trained using all available MRI studies for each group. Classification accuracy was assessed for different feature dataset and time point combinations and compared to radiologists' classifications. RESULTS: SVM classification based on combined perfusion and structural features outperformed radiologists' classification across all groups. For the identification of progressive disease, use of combined features and longitudinal DSC time points improved classification performance (lowest error rate 1.6%). Optimal performance was observed in group II (multiple time points) with SVM sensitivity/specificity/accuracy of 100/91.67/94.7% (first time point analysis) and 85.71/100/94.7% (longitudinal analysis), compared to 60/78/68% and 70/90/84.2% for the respective radiologist classifications. In group I (single time point), the SVM classifier also outperformed radiologists' classifications with sensitivity/specificity/accuracy of 86.49/75.00/81.53% (SVM) compared to 75.7/68.9/73.84% (radiologists). CONCLUSION: Our results indicate that utilisation of a machine learning (SVM) classifier based on analysis of longitudinal perfusion time points and combined structural and perfusion features significantly enhances classification outcome (p value= 0.0001).

Clinical Non-Motor Phenotyping of Black and Asian Minority Ethnic Compared to White Individuals with Parkinson's Disease Living in the United Kingdom.

BACKGROUND: Ethnic phenotypic differences in Parkinson's disease (PD) are important to understand the heterogeneity of PD and develop biomarkers and clinical trials. OBJECTIVE: To investigate (i) whether there are non-motor symptoms (NMS)- and comorbidity-based phenotypic differences between Black, Asian and Minority Ethnic (BAME) and White PD patients and (ii) whether clinically available biomarkers may help differentiate and explain the differences between the groups. METHODS: This is a multicentre (four sites, London), real-life, cross-sectional study including PD patients of BAME or White ethnicity. The primary outcome was a detailed NMS assessment; additional measurements included disease and motor stage, comorbidity, sociodemographic parameters and brain MRI imaging. RESULTS: 271 PD patients (54 Asian, 71 Black, and 146 White) were included balanced for age, gender, and disease severity (HY). Black patients had a shorter disease duration compared to White and Asian populations. The SCOPA-Motor activities of daily living scores as well as the NMSS scores were significantly higher in both Black (total score and domain "miscellaneous") and Asian (total score and domains "sleep/fatigue", "mood/apathy" and "perception/hallucinations") than White individuals. Both BAME populations had higher prevalence of arterial hypertension, and the Black population had a higher prevalence of diabetes mellitus. Brain MRI revealed a greater severity of white matter changes in Black compared to the White and Asian cohorts. CONCLUSION: These findings suggest differences in phenotype of PD in BAME populations with greater burden of NMS and motor disability and a higher rate of cardiovascular comorbidities.

Localization of the Sphenopalatine Ganglion Within the Pterygopalatine Fossa on Computed Tomography Angiography-A Potential Role in the Setting of Sphenopalatine Ganglion Microstimulator Implantation.

OBJECTIVES: A recent approach to treatment of cluster headaches (CH) employs a microstimulator device for on-demand neuromodulation of the sphenopalatine ganglion (SPG) during an acute CH attack. A precise anatomical localization of the SPG within the pterygopalatine fossa (PPF) is optimal in order to position the SPG electrode array. This study aims to investigate a novel approach for SPG localization using computed tomography angiographic studies (CTA). MATERIALS AND METHODS: Two independent observers identified the location of the SPG on 54 computed tomography angiographic studies (CTA) and measured its position relative to the vidian canal (VC). The qualitative confidence of identification, morphology, position within the PPF and its relation to vascular structures were also recorded. RESULTS: The SPG was detectable in 88% of cases with a variable position. The most frequent positions were superior (56%) and lateral (99%) relative to the VC with a mean (±SD) craniocaudal distance of 0.34 mm (±1.38) and a mean mediolateral distance of 3.04 mm (±1.2). However, in a considerable proportion of cases, the SPG was identified inferiorly to the VC (33%). Interobserver and intraobserver agreement for SPG location were moderate and strong respectively. CONCLUSIONS: Since localization of SPG on CTAs is feasible and reproducible, it has future clinical potential to aid placement, optimal positioning and individualized programming of the electrode array.

The dynamics of biomarkers across the clinical spectrum of Alzheimer's disease.

BACKGROUND: Quantifying changes in the levels of biological and cognitive markers prior to the clinical presentation of Alzheimer's disease (AD) will provide a template for understanding the underlying aetiology of the clinical syndrome and, concomitantly, for improving early diagnosis, clinical trial recruitment and treatment assessment. This study aims to characterise continuous changes of such markers and determine their rate of change and temporal order throughout the AD continuum. METHODS: The methodology is founded on the development of stochastic models to estimate the expected time to reach different clinical disease states, for different risk groups, and synchronise short-term individual biomarker data onto a disease progression timeline. Twenty-seven markers are considered, including a range of cognitive scores, cerebrospinal (CSF) and plasma fluid proteins, and brain structural and molecular imaging measures. Data from 2014 participants in the Alzheimer's Disease Neuroimaging Initiative database is utilised. RESULTS: The model suggests that detectable memory dysfunction could occur up to three decades prior to the onset of dementia due to AD (ADem). This is closely followed by changes in amyloid-ß CSF levels and the first cognitive decline, as assessed by sensitive measures. Hippocampal atrophy could be observed as early as the initial amyloid-ß accumulation. Brain hypometabolism starts later, about 14 years before onset, along with changes in the levels of total and phosphorylated tau proteins. Loss of functional abilities occurs rapidly around ADem onset. Neurofilament light is the only protein with notable early changes in plasma levels. The rate of change varies, with CSF, memory, amyloid PET and brain structural measures exhibiting the highest rate before the onset of ADem, followed by a decline. The probability of progressing to a more severe clinical state increases almost exponentially with age. In accordance with previous studies, the presence of apolipoprotein E4 alleles and amyloid-ß accumulation can be associated with an increased risk of developing the disease, but their influence depends on age and clinical state. CONCLUSIONS: Despite the limited longitudinal data at the individual level and the high variability observed in such data, the study elucidates the link between the long asynchronous pathophysiological processes and the preclinical and clinical stages of AD.

Imaging Investigation of Thyroid Eye Disease.

INTRODUCTION: Thyroid eye disease (TED), is a term referring to the extrathyroidal manifestation of Grave's disease, a disorder which is currently the most common cause of hyperthyroidism and is characterised by underlying autoimmunity.The pathogenic course of the disease can be broadly classified into two stages, an early inflammatory and a late fibrotic stage. These stages are reflected in clinical severity and activity classifications, such as Clinical Activity Score and Class 0: No signs or symptoms, 1: Only signs, no symptoms (e. g. lid retraction), 2: Soft tissue involvement, 3: Proptosis, 4: Extraocular muscle involvement, 5: Corneal involvement, 6: Sight loss (NOSPECS). Classifications based on the latter, have important implications in treatment decisions since patients in the early active stage of the disease are more likely to respond to anti-inflammatory and immunosuppressive therapies, whereas patients in the late fibrotic stage require different therapeutic approaches, including rehabilitative surgery. METHODS: We reviewed cases of TED investigated with CT and Magnetic Resonance Imaging (MRI) in our department. We assessed the findings of imaging studies and their role in the clinical investigation of patients with TED as well as in the differential diagnosis from other disorders. RESULTS: Imaging has a significant role in the investigation of TED, however a consensus on the use of different imaging modalities in the course of disease has yet to be reached. Nevertheless, imaging and specifically CT and MRI can have a vital role in the initial diagnosis of clinically atypical presentation of TED, in surgical planning, as well as in the differential diagnosis from other orbital disorders. CONCLUSION: In this review, we attempt to present current trends in imaging investigation of TED. Rather than focusing on the findings of each imaging modality separately, we present the two main imaging modalities focusing on CT and MRI, in the context of pathogenic stages of the disease.

Amyloidosis: review and imaging findings.

Amyloidosis is a collection of pathophysiologically related disease entities caused by the extracellular deposition of abnormal fibrillar proteins called amyloid. The accumulation of amyloid may be systemic, involving many organs, or localized manifesting as infiltration of individual organs, or in the form of a focal, tumorlike lesion. Amyloidosis may develop in the setting of underlying conditions, usually chronic inflammatory diseases, in which case it is termed secondary, or it may involve no underlying disease and thus be primary or idiopathic. Amyloid infiltration leads to pathology through the disruption of normal tissue structure and function or through cytotoxic effects of intermediate forms of protein aggregates. Clinical manifestations of the disease vary and are nonspecific, increasing the need of imaging during the investigation of the disease. Imaging findings are diverse and not pathognomonic; however, combined with the patient's clinical history they can raise the suspicion of amyloidosis and direct toward its confirmation by biopsy. Radiologists should be familiar with the appearance of amyloidosis in various modalities to aid the early identification of the disease and direct toward prompt treatment planning. Such knowledge would provide the radiologist with an opportunity to contribute to patient care and aid reducing the high morbidity and mortality of the disease.