Quantitative evaluation of CTP derived time-density alterations versus CTP for collateral status prediction with stroke.

BACKGROUND AND OBJECTIVES: Collateral status is a pivotal determinant of clinical outcomes in acute ischemic stroke (AIS); however, its evaluation can be challenging. We investigated the predictive value of CT perfusion (CTP) derived time and density alterations versus CTP for collateral status prediction in AIS. METHODS: Consecutive patients with anterior circulation occlusion within 24 h were retrospectively included. Time-density curves of the CTP specified ischemic core, penumbra, and the corresponding contralateral unaffected brain were obtained. The collateral status was dichotomised into robust (4-5 scores) and poor (0-3 scores) using multiphase collateral scoring, as described by Menon et al.. Receiver operating characteristic curves and multivariable regression analysis were performed to assess the predictive ability of CTP-designated tissue time and density alterations, CTP for robust collaterals, and favourable outcomes (mRS score of 0-2 at 90 days). RESULTS: One-hundred patients (median age, 68 years; interquartile range, 57-80 years; 61 men) were included. A smaller ischemic core, shorter peak time delay, lower peak density decrease, lower cerebral blood volume ratio, and cerebral blood flow ratio in the CTP specified ischemic core were significantly associated with robust collaterals (PFDR ≤ 0.004). The peak time delay demonstrated the highest diagnostic value (AUC, 0.74; P < 0.001) with 66.7 % sensitivity and 73.7 % specificity. Furthermore, the peak time delay of less than 8.5 s was an independent predictor of robust collaterals and favourable clinical outcomes. CONCLUSIONS: Robust collateral status was significantly associated with the peak time delay in the ischemic core. It is a promising image marker for predicting collateral status and functional outcomes in AIS.

Altered large-scale individual-based morphological brain network in spinocerebellar ataxia type 3.

BACKGROUND: Accumulating evidences indicate regional gray matter (GM) morphology atrophy in spinocerebellar ataxia type 3 (SCA3); however, whether large-scale morphological brain networks (MBNs) undergo widespread reorganization in these patients remains unclear. OBJECTIVE: To investigate the topological organization of large-scale individual-based MBNs in SCA3 patients. METHODS: The individual-based MBNs were constructed based on the inter-regional morphological similarity of GM regions. Graph theoretical analysis was taken to assess GM structural connectivity in 76 symptomatic SCA3, 24 pre-symptomatic SCA3, and 54 healthy normal controls (NCs). Topological parameters of the resulting graphs and network-based statistics analysis were compared among symptomatic SCA3, pre-symptomatic SCA3, and NCs groups. The inner association between network properties and clinical variables was further analyzed. RESULTS: Compared to NCs and pre-symptomatic SCA3 patients, symptomatic SCA3 indicated significantly decreased integration and segregation, a shift to "weaker small-worldness", characterized by decreased Cp , lower Eloc, and Eglob (all p < 0.005). Regarding nodal properties, symptomatic SCA3 exhibited significantly decreased nodal profiles in the central executive network (CEN)-related left inferior frontal gyrus, limbic regions involving the bilateral amygdala, left hippocampus, and bilateral pallidum, thalamus; and increased nodal degree, efficiency in bilateral caudate (all pFDR <0.05). Meanwhile, clinical variables were correlated with altered nodal profiles (pFDR ≤0.029). SCA3-related subnetwork was closely interrelated with dorsolateral cortico-striatal circuitry extending to orbitofrontal-striatal circuits and dorsal visual systems (lingual gyrus-striatal). CONCLUSION: Symptomatic SCA3 patients undergo an extensive and significant reorganization in large-scale individual-based MBNs, probably due to disrupted prefrontal cortico-striato-thalamo-cortical loops, limbic-striatum circuitry, and enhanced connectivity in the neostriatum. This study highlights the crucial role of abnormal morphological connectivity alterations beyond the pattern of brain atrophy, which might pave the way for therapeutic development in the future.

Analysis and hierarchical clustering of infratentorial morphological MRI identifies SCAs phenogroups.

BACKGROUND: Clinical decision-making in spinocerebellar ataxia spectrum diseases (SCAs) has mainly been based on genetic tests, not considering the SCAs' imaging and clinical heterogenicity. OBJECTIVE: To identify SCAs phenogroups by analysis and hierarchical clustering of infratentorial morphological MRI for unveiling pathophysiological differences among common SCA subtypes. METHODS: We prospectively enrolled 119 (62 women; mean age 37 years) genetically diagnosed SCAs (SCA1 n = 21, SCA2 n = 10, symptomatic SCA3 n = 59, presymptomatic SCA3 n = 22, SCA6 n = 7) and 35 healthy controls (HCs). All patients underwent MRI and detailed neurological and neuropsychology examinations. The width of each cerebellar peduncle (CP) and anteroposterior diameter of the spinal cord and pontine were measured. Twenty-five SCAs patients (15 women; mean age 35 years) were followed for at least a year (17 (15, 24) months), whose MRI and the Scale for the Assessment and Rating of Ataxia (SARA) were collected. RESULTS: Infratentorial morphological MRI measurements could significantly discriminate SCAs from HCs, even among SCA subtypes. Two mutually exclusive and clinically distinct phenogroups were identified. Despite similar (CAG)n, phenogroup 1 (n = 66, 55.5%) presented more atrophied infratentorial brain structures and more severe clinical symptoms with older age and earlier age of onset when compared with phenogroup 2. More importantly, all SCA2, most of SCA1 (76%), and symptomatic SCA3 (68%) were classified into phenogroup 1, whereas all SCA6 and all presymptomatic SCA3 were in phenogroup 2. The right middle CP had the highest diagnostic value in predicting phenogroup 2 (AUC = 0.99; P < 0.01) with high specificity (95%). Consistent with the significantly increased SARA (7.5 vs 10, P = 0.021), the bilateral inferior CP, spinal cord, and pontine tegmentum were more atrophy during the follow-up (P < 0.05). CONCLUSION: SCAs were with significant infratentorial brain atrophy than HCs. We identified two different SCAs phenogroups associated with substantial differences in infratentorial brain atrophy, clinical presentation, and may reflect the underlying molecular profiles to some extent, paving the way for a more personalized diagnostic and treatment approach.

Image quality using synthetic brain MRI: an age-stratified study.

BACKGROUND: Synthetic magnetic resonance imaging (MRI) might replace the conventional MR sequences in brain evaluation to shorten scan time and obtain multiple quantitative parameters. PURPOSE: To evaluate the image quality of multiple-delay-multiple-echo (MDME) sequence-derived synthetic brain MR images compared to conventional images by considering a multi-age sample. MATERIAL AND METHODS: Image sets of conventional and synthetic MRI of 200 participants were included. On the basis of the presence of intracranial lesions, the participants were divided into a normal group and a pathological group. Two neuroradiologists compared the anonymous and unordered images. Image quality, artifacts, and diagnostic performance were analyzed. RESULTS: In the quantitative analysis, comparing with conventional images, MDME sequence-derived synthetic MRI demonstrated an equal/greater signal-to-noise ratio and contrast-to-noise ratio (CNR) in all age groups. Specifically, for participants aged ≤2 years, synthetic T2-fluid-attenuated inversion recovery imaging showed a significantly higher cerebellum gray/white matter CNR (P < 0.05). In the qualitative and artifact analyses, except for the superior sagittal sinus and cranial nerves, synthetic MRI showed good imaging quality (≥3 points) in all brain structures. On synthetic T1-weighted imaging, high signal intensity within the superior sagittal sinus was found in most of our participants (107/118, 90.7%). No difference was observed between synthetic and conventional MRI in diagnosing the lesions. CONCLUSION: MDME sequence-derived synthetic MRI showed similar image quality and diagnostic performance with a shorter acquisition time than conventional MRI. However, the high signal intensity within the superior sagittal sinus on synthetic T1-weighted images requires consideration.

Structural alterations of spinocerebellar ataxias type 3: from pre-symptomatic to symptomatic stage.

OBJECTIVES: To investigate and characterize the structural alterations of the brain in SCA3, and their correlations with the scale for the assessment and rating of ataxia (SARA) and normal brain ATXN3 expression. METHODS: We performed multimodal analyses in 52 SCA3 (15 pre-symptomatic) and healthy controls (HCs) (n = 35) to assess the abnormalities of gray and white matter (WM) of the cerebrum, brainstem, and cerebellum via FreeSurfer, SUIT, and TBSS, and their associations with disease severity. Twenty SCA3 patients (5 pre- and 15 symptomatic) were followed for at least a year. Besides, we uncovered the normal pattern of brain ATXN3 spatial distribution. RESULTS: Pre-symptomatic patients showed only WM damage, mainly in the cerebellar peduncles, compared to HCs. In the advanced stage, the WM damage followed a caudal-rostral pattern. Meanwhile, continuous nonlinear structure damage was characterized by brainstem volumetric reduction and relatively symmetric cerebellar and basal ganglia atrophy but spared the cerebral cortex, partially explained by the ATXN3 overexpression. The bilateral pallidum, brainstem, and cerebellar peduncles demonstrated a very large effect size. Besides, all these alterations were significantly correlated with SARA; the pons (r = -0.65) and superior cerebellar peduncle (r = -0.68) volume demonstrated a higher correlation than the cerebellum with SARA. The longitudinal study further uncovered progressive atrophy of pons in symptomatic SCA3. CONCLUSIONS: Significant WM damage starts before the ataxia onset. The bilateral pallidum, brainstem, and cerebellar peduncles are the most vulnerable targets. The volume of pons appears to be the most promising imaging biomarker for a longitudinal study. TRIAL REGISTRATION: ClinicalTrial ID: ChiCTR2100045857 ( http://www.chictr.org.cn/edit.aspx?pid=55652&htm=4 ) KEY POINTS: • Pre- SCA3 showed WM damage mainly in cerebellar peduncles. Continuous brain damage was characterized by brainstem, widespread, and relatively symmetric cerebellar and basal ganglia atrophy. • Volumetric abnormalities were most evident in the bilateral pallidum, brainstem, and cerebellar peduncles in SCA3. • The volume of pons might identify the disease progression longitudinally.

Diagnostic accuracy and potential covariates for machine learning to identify IDH mutations in glioma patients: evidence from a meta-analysis.

OBJECTIVES: To assess the diagnostic accuracy of machine learning (ML) in predicting isocitrate dehydrogenase (IDH) mutations in patients with glioma and to identify potential covariates that could influence the diagnostic performance of ML. METHODS: A systematic search of PubMed, Web of Science, and the Cochrane library up to 1 August 2019 was conducted to collect all the articles investigating the diagnostic performance of ML for prediction of IDH mutation in glioma. The search strategy combined synonyms for 'machine learning', 'glioma', and 'IDH'. Pooled sensitivity, specificity, and their 95% confidence intervals (CIs) were calculated, and the area under the receiver operating characteristic curve (AUC) was obtained. RESULTS: Nine original articles assessing a total of 996 patients with glioma were included. Among these studies, five divided the participants into training and validation sets, while the remaining four studies only had a training set. The AUC of ML for predicting IDH mutation in the training and validation sets was 93% (95% CI 91-95%) and 89% (95% CI 86-92%), respectively. The pooled sensitivity and specificity were, respectively, 87% (95% CI 82-91%) and 88% (95% CI 83-92%) in the training set and 87% (95% CI 76-93%) and 90% (95% CI 72-97%) in the validation set. In subgroup analyses in the training set, the combined use of clinical and imaging features with ML yielded higher sensitivity (90% vs. 83%) and specificity (90% vs. 82%) than the use of imaging features alone. In addition, ML performed better for high-grade gliomas than for low-grade gliomas, and ML that used conventional MRI sequences demonstrated higher specificity for predicting IDH mutation than ML using conventional and advanced MRI sequences. CONCLUSIONS: ML demonstrated an excellent diagnostic performance in predicting IDH mutation of glioma. Clinical information, MRI sequences, and glioma grade were the main factors influencing diagnostic specificity. KEY POINTS: • Machine learning demonstrated an excellent diagnostic performance for prediction of IDH mutation in glioma (the pooled sensitivity and specificity were 88% and 87%, respectively). • Machine learning that used conventional MRI sequences demonstrated higher specificity in predicting IDH mutation than that based on conventional and advanced MRI sequences (89% vs. 85%). • Integration of clinical and imaging features in machine learning yielded a higher sensitivity (90% vs. 83%) and specificity (90% vs. 82%) than that achieved by using imaging features alone.

Comparative analysis of the diffusion kurtosis imaging and diffusion tensor imaging in grading gliomas, predicting tumour cell proliferation and IDH-1 gene mutation status.

INTRODUCTION: Few studies have applied diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) for the comprehensive assessment of gliomas [tumour grade, isocitrate dehydrogenase-1 (IDH-1) mutation status and tumour proliferation rate (Ki-67)]. This study describes the efficacy of DKI and DTI to comprehensively evaluate gliomas, compares their results. METHODS: Fifty-two patients (18 females; median age, 47.5 years) with pathologically proved gliomas were prospectively included. All cases underwent DKI examination. DKI (mean kurtosis: MK, axial kurtosis: Ka, radial kurtosis: Kr) and DTI (mean diffusivity: MD, fractional anisotropy: FA) maps of each metric was derived. Three ROIs were manually drawn. RESULTS: MK, Ka, Kr and FA were significantly higher in HGGs than in LGGs, whereas MD was significantly lower in HGGs than in LGGs (P < 0.01). ROC analysis demonstrated that MK (specificity: 100% sensitivity: 79%) and Ka (specificity: 96% sensitivity: 82%) had the same and highest (AUC: 0.93) diagnostic value. Moreover, MK, Ka, and Kr were significantly higher in grade III than II gliomas (P ≦ 0.01). Further, DKI and DTI can significantly identify IDH-1 mutation status (P ≦ 0.03). Ka (sensitivity: 74%, specificity: 75%, AUC: 0.72) showed the highest diagnostic value. In addition, DKI metrics and MD showed significant correlations with Ki-67 (P ≦ 0.01) and Ka had the highest correlation coefficient (rs = 0.72). CONCLUSIONS: Compared with DTI, DKI has great advantages for the comprehensive assessment of gliomas. Ka might serve as a promising imaging index in predicting glioma grading, tumour cell proliferation rate and IDH-1 gene mutation status.