Initial findings using high-resolution magnetic resonance imaging for visualisation of the sural nerve and surrounding anatomy in healthy volunteers at 7 Tesla.

BACKGROUND AND AIMS: Histopathological diagnosis is the gold standard in many acquired inflammatory, infiltrative and amyloid based peripheral nerve diseases and a sensory nerve biopsy of sural or superficial peroneal nerve is favoured where a biopsy is deemed necessary. The ability to determine nerve pathology by high-resolution imaging techniques resolving anatomy and imaging characteristics might improve diagnosis and obviate the need for biopsy in some. The sural nerve is anatomically variable and occasionally adjacent vessels can be sent for analysis in error. Knowing the exact position and relationships of the nerve prior to surgery could be clinically useful and thus reliably resolving nerve position has some utility. METHODS: 7T images of eight healthy volunteers' (HV) right ankle were acquired in a pilot study using a double-echo in steady-state sequence for high-resolution anatomy images. Magnetic Transfer Ratio images were acquired of the same area. Systematic scoring of the sural, tibial and deep peroneal nerve around the surgical landmark 7 cm from the lateral malleolus was performed (number of fascicles, area in voxels and mm2, diameter and location relative to nearby vessels and muscles). RESULTS: The sural and tibial nerves were visualised in the high-resolution double-echo in steady-state (DESS) image in all HV. The deep peroneal nerve was not always visualised at level of interest. The MTR values were tightly grouped except in the sural nerve where the nerve was not visualised in two HV. The sural nerve location was found to be variable (e.g., lateral or medial to, or crossing behind, or found positioned directly posterior to the saphenous vein). INTERPRETATION: High-resolution high-field images have excellent visualisation of the sural nerve and would give surgeons prior knowledge of the position before surgery. Basic imaging characteristics of the sural nerve can be acquired, but more detailed imaging characteristics are not easily evaluable in the very small sural and further developments and specific studies are required for any diagnostic utility at 7T.

Glioblastoma and radiotherapy: A multicenter AI study for Survival Predictions from MRI (GRASP study).

BACKGROUND: The aim was to predict survival of glioblastoma at 8 months after radiotherapy (a period allowing for completing a typical course of adjuvant temozolomide), by applying deep learning to the first brain MRI after radiotherapy completion. METHODS: Retrospective and prospective data were collected from 206 consecutive glioblastoma, isocitrate dehydrogenase -wildtype patients diagnosed between March 2014 and February 2022 across 11 UK centers. Models were trained on 158 retrospective patients from 3 centers. Holdout test sets were retrospective (n = 19; internal validation), and prospective (n = 29; external validation from 8 distinct centers). Neural network branches for T2-weighted and contrast-enhanced T1-weighted inputs were concatenated to predict survival. A nonimaging branch (demographics/MGMT/treatment data) was also combined with the imaging model. We investigated the influence of individual MR sequences; nonimaging features; and weighted dense blocks pretrained for abnormality detection. RESULTS: The imaging model outperformed the nonimaging model in all test sets (area under the receiver-operating characteristic curve, AUC P = .038) and performed similarly to a combined imaging/nonimaging model (P > .05). Imaging, nonimaging, and combined models applied to amalgamated test sets gave AUCs of 0.93, 0.79, and 0.91. Initializing the imaging model with pretrained weights from 10 000s of brain MRIs improved performance considerably (amalgamated test sets without pretraining 0.64; P = .003). CONCLUSIONS: A deep learning model using MRI images after radiotherapy reliably and accurately determined survival of glioblastoma. The model serves as a prognostic biomarker identifying patients who will not survive beyond a typical course of adjuvant temozolomide, thereby stratifying patients into those who might require early second-line or clinical trial treatment.

Early MRI Predictors of Relapse in Primary Central Nervous System Lymphoma Treated with MATRix Immunochemotherapy.

Primary Central Nervous System Lymphoma (PCNSL) is a highly malignant brain tumour. We investigated dynamic changes in tumour volume and apparent diffusion coefficient (ADC) measurements for predicting outcome following treatment with MATRix chemotherapy in PCNSL. Patients treated with MATRix (n = 38) underwent T1 contrast-enhanced (T1CE) and diffusion-weighted imaging (DWI) before treatment, after two cycles and after four cycles of chemotherapy. Response was assessed using the International PCNSL Collaborative Group (IPCG) imaging criteria. ADC histogram parameters and T1CE tumour volumes were compared among response groups, using one-way ANOVA testing. Logistic regression was performed to examine those imaging parameters predictive of response. Response after two cycles of chemotherapy differed from response after four cycles; of the six patients with progressive disease (PD) after four cycles of treatment, two (33%) had demonstrated a partial response (PR) or complete response (CR) after two cycles. ADCmean at baseline, T1CE at baseline and T1CE percentage volume change differed between response groups (0.005 < p < 0.038) and were predictive of MATRix treatment response (area under the curve: 0.672-0.854). Baseline ADC and T1CE metrics are potential biomarkers for risk stratification of PCNSL patients early during remission induction therapy with MATRix. Standard interim response assessment (after two cycles) according to IPCG imaging criteria does not reliably predict early disease progression in the context of a conventional treatment approach.

Functional MRI but not white matter fibre dissection identifies language dominance.

OBJECTIVES: Lateralisation of some language pathways has been reported in the literature using diffusion tractography, which is more feasible than functional magnetic resonance imaging (fMRI) in challenging patients. Our retrospective study investigates whether a correlation exists between threshold-independent fMRI language lateralisation and structural lateralisation using tractography in healthy controls and brain tumour patients. METHODS: Fifteen healthy subjects and 61 patients underwent language fMRI and diffusion-weighted MRI. A regional fMRI laterality index (LI) was calculated. Tracts dissected were the arcuate fasciculus (long direct and short indirect tracts), uncinate fasciculus, inferior longitudinal fasciculus, inferior fronto-occipital fasciculus and frontal aslant tract. An asymmetry index (AI) for each tract was calculated using tract volume analysed with single tensor (ST) and spherical deconvolution (SD) models, as well as hindrance modulated orientational anisotropy (HMOA) for SD tracts. Linear regression assessed the correlation between LI and AI. RESULTS: In all subjects, there was no significant correlation between LI and AI for any of the dissected tracts. Significant correlations were only found when handedness for controls and tumour volume for patients were included as covariates. In handedness subgroups, the average AI of some tracts showed the same laterality as LI, and some the opposite. Discordant results were observed for ST- and SD-based AIs. CONCLUSIONS: Our results do not support using tractography in the assessment of language lateralisation. The discordant results between ST and SD indicate that either the structural lateralisation of dissected tracts is less robust than functional lateralisation, or tractography is not sensitive methodology. Other diffusion analysis approaches should be developed. CLINICAL RELEVANCE STATEMENT: Although diffusion tractography may be more feasible than fMRI in challenging tumour patients and where sedation or anaesthesia is required, our results do not currently recommend replacing fMRI with tractography using volume or HMOA in the assessment of language lateralisation. KEY POINTS: • No correlation found between fMRI and tractography in language lateralisation. • Discordance between asymmetry indices of different tractography models and metrics. • Tractography not currently recommended in language lateralisation assessment.

Overcoming challenges of translating deep-learning models for glioblastoma: the ZGBM consortium.

OBJECTIVE: To report imaging protocol and scheduling variance in routine care of glioblastoma patients in order to demonstrate challenges of integrating deep-learning models in glioblastoma care pathways. Additionally, to understand the most common imaging studies and image contrasts to inform the development of potentially robust deep-learning models. METHODS: MR imaging data were analysed from a random sample of five patients from the prospective cohort across five participating sites of the ZGBM consortium. Reported clinical and treatment data alongside DICOM header information were analysed to understand treatment pathway imaging schedules. RESULTS: All sites perform all structural imaging at every stage in the pathway except for the presurgical study, where in some sites only contrast-enhanced T1-weighted imaging is performed. Diffusion MRI is the most common non-structural imaging type, performed at every site. CONCLUSION: The imaging protocol and scheduling varies across the UK, making it challenging to develop machine-learning models that could perform robustly at other centres. Structural imaging is performed most consistently across all centres. ADVANCES IN KNOWLEDGE: Successful translation of deep-learning models will likely be based on structural post-treatment imaging unless there is significant effort made to standardise non-structural or peri-operative imaging protocols and schedules.

Filtration-Histogram Based Magnetic Resonance Texture Analysis (MRTA) for the Distinction of Primary Central Nervous System Lymphoma and Glioblastoma.

Primary central nervous system lymphoma (PCNSL) has variable imaging appearances, which overlap with those of glioblastoma (GBM), thereby necessitating invasive tissue diagnosis. We aimed to investigate whether a rapid filtration histogram analysis of clinical MRI data supports the distinction of PCNSL from GBM. Ninety tumours (PCNSL n = 48, GBM n = 42) were analysed using pre-treatment MRI sequences (T1-weighted contrast-enhanced (T1CE), T2-weighted (T2), and apparent diffusion coefficient maps (ADC)). The segmentations were completed with proprietary texture analysis software (TexRAD version 3.3). Filtered (five filter sizes SSF = 2-6 mm) and unfiltered (SSF = 0) histogram parameters were compared using Mann-Whitney U non-parametric testing, with receiver operating characteristic (ROC) derived area under the curve (AUC) analysis for significant results. Across all (n = 90) tumours, the optimal algorithm performance was achieved using an unfiltered ADC mean and the mean of positive pixels (MPP), with a sensitivity of 83.8%, specificity of 8.9%, and AUC of 0.88. For subgroup analysis with >1/3 necrosis masses, ADC permitted the identification of PCNSL with a sensitivity of 96.9% and specificity of 100%. For T1CE-derived regions, the distinction was less accurate, with a sensitivity of 71.4%, specificity of 77.1%, and AUC of 0.779. A role may exist for cross-sectional texture analysis without complex machine learning models to differentiate PCNSL from GBM. ADC appears the most suitable sequence, especially for necrotic lesion distinction.

Imaging characteristics of H3 K27M histone-mutant diffuse midline glioma in teenagers and adults.

BACKGROUND: To assess anatomical and quantitative diffusion-weighted MR imaging features in a recently classified lethal neoplasm, H3 K27M histone-mutant diffuse midline glioma [World Health Organization (WHO) IV]. METHODS: Fifteen untreated gliomas in teenagers and adults (median age 19, range, 14-64) with confirmed H3 K27M histone-mutant genotype were analysed at a national referral centre. Morphological characteristics including tumour epicentre(s), T2/FLAIR and Gadolinium enhancement patterns, calcification, haemorrhage and cyst formation were recorded. Multiple apparent diffusion coefficient (ADCmin, ADCmean) regions of interest were sited in solid tumour and normal appearing white matter (ADCNAWM) using post-processing software (Olea Sphere v2.3, Olea Medical). ADC histogram data (2nd, 5th, 10th percentile, median, mean, kurtosis, skewness) were calculated from volumetric tumour segmentations and tested against the regions of interest (ROI) data (Wilcoxon signed rank test). RESULTS: The median interval from imaging to tissue diagnosis was 9 (range, 0-74) days. The structural MR imaging findings varied between individuals and within tumours, often featuring signal heterogeneity on all MR sequences. All gliomas demonstrated contact with the brain midline, and 67% exhibited rim-enhancing necrosis. The mean ROI ADCmin value was 0.84 (±0.15 standard deviation, SD) ×10-3 mm2/s. In the largest tumour cross-section (excluding necrosis), an average ADCmean value of 1.12 (±0.25)×10-3 mm2/s was observed. The mean ADCmin/NAWM ratio was 1.097 (±0.149), and the mean ADCmean/NAWM ratio measured 1.466 (±0.299). With the exception of the 2nd centile, no statistical difference was observed between the regional and histogram derived ADC results. CONCLUSIONS: H3 K27M-mutant gliomas demonstrate variable morphology and diffusivity, commonly featuring moderately low ADC values in solid tumour. Regional ADC measurements appeared representative of volumetric histogram data in this study.

World Health Organization Grade II/III Glioma Molecular Status: Prediction by MRI Morphologic Features and Apparent Diffusion Coefficient.

Background A readily implemented MRI biomarker for glioma genotyping is currently lacking. Purpose To evaluate clinically available MRI parameters for predicting isocitrate dehydrogenase (IDH) status in patients with glioma. Materials and Methods In this retrospective study of patients studied from July 2008 to February 2019, untreated World Health Organization (WHO) grade II/III gliomas were analyzed by three neuroradiologists blinded to tissue results. Apparent diffusion coefficient (ADC) minimum (ADCmin) and mean (ADCmean) regions of interest were defined in tumor and normal appearing white matter (ADCNAWM). A visual rating of anatomic features (T1 weighted, T1 weighted with contrast enhancement, T2 weighted, and fluid-attenuated inversion recovery) was performed. Interobserver comparison (intraclass correlation coefficient and Cohen κ) was followed by nonparametric (Kruskal-Wallis analysis of variance) testing of associations between ADC metrics and glioma genotypes, including Bonferroni correction for multiple testing. Descriptors with sufficient concordance (intraclass correlation coefficient, >0.8; κ > 0.6) underwent univariable analysis. Predictive variables (P < .05) were entered into a multivariable logistic regression and tested in an additional test sample of patients with glioma. Results The study included 290 patients (median age, 40 years; interquartile range, 33-52 years; 169 male patients) with 82 IDH wild-type, 107 IDH mutant/1p19q intact, and 101 IDH mutant/1p19q codeleted gliomas. Two predictive models incorporating ADCmean-to-ADCNAWM ratio, age, and morphologic characteristics, with model A mandating calcification result and model B recording cyst formation, classified tumor type with areas under the receiver operating characteristic curve of 0.94 (95% confidence interval [CI]: 0.91, 0.97) and 0.96 (95% CI: 0.93, 0.98), respectively. In the test sample of 49 gliomas (nine IDH wild type, 21 IDH mutant/1p19q intact, and 19 IDH mutant/1p19q codeleted), the classification accuracy was 40 of 49 gliomas (82%; 95% CI: 71%, 92%) for model A and 42 of 49 gliomas (86%; 95% CI: 76%, 96%) for model B. Conclusion Two algorithms that incorporated apparent diffusion coefficient values, age, and tumor morphologic characteristics predicted isocitrate dehydrogenase status in World Health Organization grade II/III gliomas on the basis of standard clinical MRI sequences alone. © RSNA, 2020 Online supplemental material is available for this article.

Improving fMRI in signal drop-out regions at 7 T by using tailored radio-frequency pulses: application to the ventral occipito-temporal cortex.

OBJECTIVE: Signal drop-off occurs in echo-planar imaging in inferior brain areas due to field gradients from susceptibility differences between air and tissue. Tailored-RF pulses based on a hyperbolic secant (HS) have been shown to partially recover signal at 3 T, but have not been tested at higher fields. MATERIALS AND METHODS: The aim of this study was to compare the performance of an optimized tailored-RF gradient-echo echo-planar imaging (TRF GRE-EPI) sequence with standard GRE-EPI at 7 T, in a passive viewing of faces or objects fMRI paradigm in healthy subjects. RESULTS: Increased temporal-SNR (tSNR) was observed in the middle and inferior temporal lobes and orbitofrontal cortex of all subjects scanned, but elsewhere tSNR decreased relative to the standard acquisition. In the TRF GRE-EPI, increased functional signal was observed in the fusiform, lateral occipital cortex, and occipital pole, regions known to be part of the visual pathway involved in face-object perception. CONCLUSION: This work highlights the potential of TRF approaches at 7 T. Paired with a reversed-gradient distortion correction to compensate for in-plane susceptibility gradients, it provides an improved acquisition strategy for future neurocognitive studies at ultra-high field imaging in areas suffering from static magnetic field inhomogeneities.