The value of arterial spin labelling (ASL) perfusion MRI in the assessment of post-treatment progression in adult glioma: A systematic review and meta-analysis

BACKGROUND: The distinction between viable tumor and therapy-induced changes is crucial for the clinical management of patients with gliomas. This study aims to quantitatively assess the efficacy of arterial spin labeling (ASL) biomarkers, including relative cerebral blood flow (rCBF) and absolute cerebral blood flow (CBF), for the discrimination of progressive disease (PD) and treatment-related effects. METHODS: Eight articles were included in the synthesis after searching the literature systematically. Data have been extracted and a meta-analysis using the random-effect model was subsequently carried out. Diagnostic accuracy assessment was also performed. RESULTS: This study revealed that there is a significant difference in perfusion measurements between groups with PD and therapy-induced changes. The rCBF yielded a standardized mean difference (SMD) of 1.25 [95% CI 0.75, 1.75] (p < .00001). The maximum perfusion indices (rCBFmax and CBFmax) both showed equivalent discriminatory ability, with SMD of 1.35 [95% CI 0.78, 1.91] (p < .00001) and 1.56 [95% CI 0.79, 2.33] (p < .0001), respectively. Similarly, accuracy estimates were comparable among ASL-derived metrices. Pooled sensitivities [95% CI] were 0.85 [0.67, 0.94], 0.88 [0.71, 0.96], and 0.93 [0.73, 0.98], and pooled specificities [95% CI] were 0.83 [0.71, 0.91], 0.83 [0.67, 0.92], 0.84 [0.67, 0.93], for rCBF, rCBFmax and CBFmax, respectively. Corresponding HSROC area under curve (AUC) [95% CI] were 0.90 [0.87, 0.92], 0.92 [0.89, 0.94], and 0.93 [0.90, 0.95]. CONCLUSION: These results suggest that ASL quantitative biomarkers, particularly rCBFmax and CBFmax, have the potential to discriminate between glioma progression and therapy-induced changes

Brain tumour segmentation with incomplete data

Brain tumour segmentation remains a challenging task, complicated by the marked heterogeneity of imaging appearances and their distribution across multiple modalities: FLAIR, T1-weighted, T2-weighted, and contrast-enhanced T1-weighted sequences (T1CE). This has compelled a research focus on uniformly multimodal models trained on complete acquisition sets rare in real-world clinical practice. Consider, for example, patients with renal failure who cannot receive contrast, artefact-spoiled sequences, or patients undergoing single-sequence intraoperative imaging. How well do segmentation models perform with such incomplete data, and what features of the lesion are identifiable under these circumstances? In a large-scale analysis involving 30 distinct segmentation models, we answer these questions with a state-of-the-art tumour segmentation modelling ensemble, nnU-Net-derived (Isensee et al, Nature Methods, 2020), deployed across all possible combinations of imaging modalities, trained, and tested with five-fold cross-validation on the 2021 BraTS-RSNA glioma population of 1251 patients. Segmentation performances for whole lesions range from Dice scores of 0.907 (single sequence) to 0.945 (full datasets) (Figure 1). When segmenting lesions by tissue type (enhancing tumour, non-enhancing tumour and oedema), Dice scores range from 0.701 (single sequence) to 0.891 (full datasets). Models missing postcontrast imaging still achieve a Dice coefficient for the whole tumour of 0.942 and identify the enhancing tumour component with Dice of up to 0.790 (Figure 2). Segmentation models can identify tumours with missing data, and can be used in clinical situations where partial data is frequent

Compressed representation of brain genetic transcription

The architecture of the brain is too complex to be intuitively surveyable without the use of compressed representations that project its variation into a compact, navigable space. The task is especially challenging with high-dimensional data, such as gene expression, where the joint complexity of anatomical and transcriptional patterns demands maximum compression. Established practice is to use standard principal component analysis (PCA), whose computational felicity is offset by limited expressivity, especially at great compression ratios. Employing whole-brain, voxel-wise Allen Brain Atlas transcription data, here we systematically compare compressed representations based on the most widely supported linear and non-linear methods-PCA, kernel PCA, non-negative matrix factorization (NMF), t-stochastic neighbour embedding (t-SNE), uniform manifold approximation and projection (UMAP), and deep auto-encoding-quantifying reconstruction fidelity, anatomical coherence, and predictive utility with respect to signalling, microstructural, and metabolic targets. We show that deep auto-encoders yield superior representations across all metrics of performance and target domains, supporting their use as the reference standard for representing transcription patterns in the human brain.Comment: 21 pages, 5 main figures, 1 supplementary figur

The legibility of the imaged human brain

Our knowledge of the organisation of the human brain at the population-level is yet to translate into power to predict functional differences at the individual-level, limiting clinical applications, and casting doubt on the generalisability of inferred mechanisms. It remains unknown whether the difficulty arises from the absence of individuating biological patterns within the brain, or from limited power to access them with the models and compute at our disposal. Here we comprehensively investigate the resolvability of such patterns with data and compute at unprecedented scale. Across 23810 unique participants from UK Biobank, we systematically evaluate the predictability of 25 individual biological characteristics, from all available combinations of structural and functional neuroimaging data. Over 4526 GPU*hours of computation, we train, optimize, and evaluate out-of-sample 700 individual predictive models, including multilayer perceptrons of demographic, psychological, serological, chronic morbidity, and functional connectivity characteristics, and both uni- and multi-modal 3D convolutional neural network models of macro- and micro-structural brain imaging. We find a marked discrepancy between the high predictability of sex (balanced accuracy 99.7%), age (mean absolute error 2.048 years, R2 0.859), and weight (mean absolute error 2.609Kg, R2 0.625), for which we set new state-of-the-art performance, and the surprisingly low predictability of other characteristics. Neither structural nor functional imaging predicted individual psychology better than the coincidence of common chronic morbidity (p<0.05). Serology predicted common morbidity (p<0.05) and was best predicted by it (p<0.001), followed by structural neuroimaging (p<0.05). Our findings suggest either more informative imaging or more powerful models will be needed to decipher individual level characteristics from the brain.Comment: 36 pages, 6 figures, 1 table, 2 supplementary figure

Assessing initial MRI reports for suspected CJD patients

BACKGROUND: MRI is invaluable for the pre-mortem diagnosis of sporadic Creutzfeldt-Jakob disease (sCJD), demonstrating characteristic diffusion abnormalities. Previous work showed these changes were often not reported (low sensitivity), leading to eventual diagnosis at a more advanced state. Here, we reviewed the situation a decade later, on the presumption of improved access and awareness over time. METHODS: We reviewed initial MRI scans of 102 consecutive suspected sCJD patients recruited to the National Prion Monitoring Cohort study between 2015 and 2019, assessing for characteristic signal changes in the striatum, thalamus and cortical ribbon. We compared our findings to formal reports from referring centres. Requesting indications were studied to assess if they were suggestive of CJD. Patients were examined and their MRC Prion Disease Rating Scale scores recorded. RESULTS: We identified characteristic MRI abnormalities in 101 cases (99% sensitivity), whilst referring centres reported changes in 70 cases (69% sensitivity), which was a significant improvement in reporting sensitivity from 2012. Reporting sensitivity was associated with signal change in the cerebral cortex, and with the number of regions involved, but not significantly affected by clinical information on request forms, or referring centres being regional neuroscience/non-neuroscience centres. Similar to a previous study, patients with missed abnormalities on initial reporting possessed lower MRC Scale scores when referred to the NPC than those correctly identified. CONCLUSIONS: Whilst local MRI reporting of sCJD has improved with time, characteristic abnormalities remain significantly under detected on initial scans. Sensitivity is better when the cerebral cortex and multiple regions are involved. We re-emphasize the utility of MRI and encourage further efforts to improve awareness and sensitivity in the assessment of patients with rapidly progressive dementia

Adult cerebral malaria: acute and subacute imaging findings, long-term clinical consequences.

Cerebral malaria is an important cause of mortality and neurodisability in endemic regions. We show MRI features suggestive of cytotoxic and vasogenic cerebral edema followed by microhemorrhages in two adult UK cases, comparing them with an Indian cohort. Long-term follow-up images correlate ongoing changes with residual functional impairment

Steps on the Path to Clinical Translation: A workshop by the British and Irish Chapter of the ISMRM

The British and Irish Chapter of the International Society for Magnetic Resonance in Medicine (BIC-ISMRM) held a workshop entitled "Steps on the path to clinical translation" in Cardiff, UK, on 7th September 2022. The aim of the workshop was to promote discussion within the MR community about the problems and potential solutions for translating quantitative MR (qMR) imaging and spectroscopic biomarkers into clinical application and drug studies. Invited speakers presented the perspectives of radiologists, radiographers, clinical physicists, vendors, imaging Contract/Clinical Research Organizations (CROs), open science networks, metrologists, imaging networks, and those developing consensus methods. A round-table discussion was held in which workshop participants discussed a range of questions pertinent to clinical translation of qMR imaging and spectroscopic biomarkers. Each group summarized their findings via three main conclusions and three further questions. These questions were used as the basis of an online survey of the broader UK MR community

Steps on the Path to Clinical Translation: A workshop by the British and Irish Chapter of the ISMRM

The British and Irish Chapter of the International Society for Magnetic Resonance in Medicine (BIC‐ISMRM) held a workshop entitled “Steps on the path to clinical translation” in Cardiff, UK, on 7th September 2022. The aim of the workshop was to promote discussion within the MR community about the problems and potential solutions for translating quantitative MR (qMR) imaging and spectroscopic biomarkers into clinical application and drug studies. Invited speakers presented the perspectives of radiologists, radiographers, clinical physicists, vendors, imaging Contract/Clinical Research Organizations (CROs), open science networks, metrologists, imaging networks, and those developing consensus methods. A round‐table discussion was held in which workshop participants discussed a range of questions pertinent to clinical translation of qMR imaging and spectroscopic biomarkers. Each group summarized their findings via three main conclusions and three further questions. These questions were used as the basis of an online survey of the broader UK MR community

Clinical impairment in premanifest and early Huntington's disease is associated with regionally specific atrophy.

TRACK-HD is a multicentre longitudinal observational study investigating the use of clinical assessments and 3-Tesla magnetic resonance imaging as potential biomarkers for future therapeutic trials in Huntington's disease (HD). The cross-sectional data from this large well-characterized dataset provide the opportunity to improve our knowledge of how the underlying neuropathology of HD may contribute to the clinical manifestations of the disease across the spectrum of premanifest (PreHD) and early HD. Two hundred and thirty nine gene-positive subjects (120 PreHD and 119 early HD) from the TRACK-HD study were included. Using voxel-based morphometry (VBM), grey and white matter volumes were correlated with performance in four domains: quantitative motor (tongue force, metronome tapping, and gait); oculomotor [anti-saccade error rate (ASE)]; cognition (negative emotion recognition, spot the change and the University of Pennsylvania smell identification test) and neuropsychiatric measures (apathy, affect and irritability). After adjusting for estimated disease severity, regionally specific associations between structural loss and task performance were found (familywise error corrected, P < 0.05); impairment in tongue force, metronome tapping and ASE were all associated with striatal loss. Additionally, tongue force deficits and ASE were associated with volume reduction in the occipital lobe. Impaired recognition of negative emotions was associated with volumetric reductions in the precuneus and cuneus. Our study reveals specific associations between atrophy and decline in a range of clinical modalities, demonstrating the utility of VBM correlation analysis for investigating these relationships in HD

Biological and clinical characteristics of gene carriers far from predicted onset in the Huntington's disease Young Adult Study (HD-YAS): a cross-sectional analysis.

BACKGROUND: Disease-modifying treatments are in development for Huntington's disease; crucial to their success is to identify a timepoint in a patient's life when there is a measurable biomarker of early neurodegeneration while clinical function is still intact. We aimed to identify this timepoint in a novel cohort of young adult premanifest Huntington's disease gene carriers (preHD) far from predicted clinical symptom onset. METHODS: We did the Huntington's disease Young Adult Study (HD-YAS) in the UK. We recruited young adults with preHD and controls matched for age, education, and sex to ensure each group had at least 60 participants with imaging data, accounting for scan fails. Controls either had a family history of Huntington's disease but a negative genetic test, or no known family history of Huntington's disease. All participants underwent detailed neuropsychiatric and cognitive assessments, including tests from the Cambridge Neuropsychological Test Automated Battery and a battery assessing emotion, motivation, impulsivity and social cognition (EMOTICOM). Imaging (done for all participants without contraindications) included volumetric MRI, diffusion imaging, and multiparametric mapping. Biofluid markers of neuronal health were examined using blood and CSF collection. We did a cross-sectional analysis using general least-squares linear models to assess group differences and associations with age and CAG length, relating to predicted years to clinical onset. Results were corrected for multiple comparisons using the false discovery rate (FDR), with FDR 0·16). CSF neurofilament light protein (NfL), plasma NfL, and CSF YKL-40 were elevated in this far-from-onset preHD cohort compared with controls (FDR<0·0001, =0·01, and =0·03, respectively). CSF NfL elevations were more likely in individuals closer to expected clinical onset (FDR <0·0001). INTERPRETATION: We report normal brain function yet a rise in sensitive measures of neurodegeneration in a preHD cohort approximately 24 years from predicted clinical onset. CSF NfL appears to be a more sensitive measure than plasma NfL to monitor disease progression. This preHD cohort is one of the earliest yet studied, and our findings could be used to inform decisions about when to initiate a potential future intervention to delay or prevent further neurodegeneration while function is intact. FUNDING: Wellcome Trust, CHDI Foundation