Bi-allelic variants in SNF8 cause a disease spectrum ranging from severe developmental and epileptic encephalopathy to syndromic optic atrophy.

The endosomal sorting complex required for transport (ESCRT) machinery is essential for membrane remodeling and autophagy and it comprises three multi-subunit complexes (ESCRT I-III). We report nine individuals from six families presenting with a spectrum of neurodevelopmental/neurodegenerative features caused by bi-allelic variants in SNF8 (GenBank: NM_007241.4), encoding the ESCRT-II subunit SNF8. The phenotypic spectrum included four individuals with severe developmental and epileptic encephalopathy, massive reduction of white matter, hypo-/aplasia of the corpus callosum, neurodevelopmental arrest, and early death. A second cohort shows a milder phenotype with intellectual disability, childhood-onset optic atrophy, or ataxia. All mildly affected individuals shared the same hypomorphic variant, c.304G>A (p.Val102Ile). In patient-derived fibroblasts, bi-allelic SNF8 variants cause loss of ESCRT-II subunits. Snf8 loss of function in zebrafish results in global developmental delay and altered embryo morphology, impaired optic nerve development, and reduced forebrain size. In vivo experiments corroborated the pathogenicity of the tested SNF8 variants and their variable impact on embryo development, validating the observed clinical heterogeneity. Taken together, we conclude that loss of ESCRT-II due to bi-allelic SNF8 variants is associated with a spectrum of neurodevelopmental/neurodegenerative phenotypes mediated likely via impairment of the autophagic flux.

Reliability and Feasibility of Low-Field-Strength Fetal MRI at 0.55 T during Pregnancy.

Background The benefits of using low-field-strength fetal MRI to evaluate antenatal development include reduced image artifacts, increased comfort, larger bore size, and potentially reduced costs, but studies about fetal low-field-strength MRI are lacking. Purpose To evaluate the reliability and feasibility of low-field-strength fetal MRI to assess anatomic and functional measures in pregnant participants using a commercially available 0.55-T MRI scanner and a comprehensive 20-minute protocol. Materials and Methods This prospective study was performed at a large teaching hospital (St Thomas' Hospital; London, England) from May to November 2022 in healthy pregnant participants and participants with pregnancy-related abnormalities using a commercially available 0.55-T MRI scanner. A 20-minute protocol was acquired including anatomic T2-weighted fast-spin-echo, quantitative T2*, and diffusion sequences. Key measures like biparietal diameter, transcerebellar diameter, lung volume, and cervical length were evaluated by two radiologists and an MRI-experienced obstetrician. Functional organ-specific mean values were given. Comparison was performed with existing published values and higher-field MRI using linear regression, interobserver correlation, and Bland-Altman plots. Results A total of 79 fetal MRI examinations were performed (mean gestational age, 29.4 weeks ± 5.5 [SD] [age range, 17.6-39.3 weeks]; maternal age, 34.4 years ± 5.3 [age range, 18.4-45.5 years]) in 47 healthy pregnant participants (control participants) and in 32 participants with pregnancy-related abnormalities. The key anatomic two-dimensional measures for the 47 healthy participants agreed with large cross-sectional 1.5-T and 3-T control studies. The interobserver correlations for the biparietal diameter in the first 40 consecutive scans were 0.96 (95% CI: 0.7, 0.99; P = .002) for abnormalities and 0.93 (95% CI: 0.86, 0.97; P < .001) for control participants. Functional features, including placental and brain T2* and placental apparent diffusion coefficient values, strongly correlated with gestational age (mean placental T2* in the control participants: 5.2 msec of decay per week; R2 = 0.66; mean T2* at 30 weeks, 176.6 msec; P < .001). Conclusion The 20-minute low-field-strength fetal MRI examination protocol was capable of producing reliable structural and functional measures of the fetus and placenta in pregnancy. Clinical trial registration no. REC 21/LO/0742 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Gowland in this issue.

Inflammation in stroke: initial CRP levels can predict poor outcomes in endovascularly treated stroke patients.

Background and purpose: Inflammation has been linked to poor prognoses in cardio- and cerebrovascular conditions. As it is known to increase after ischemia, C-reactive protein (CRP) may serve as a surrogate for systemic inflammation and thus be a hallmark of increased tissue vulnerability. The question arises whether CRP in the acute phase of ischemic stroke, prior to mechanical thrombectomy (MT), might help predict outcomes. Materials and methods: A single-center collective of patients with large-vessel occlusion, who were treated via MT, was analyzed in this observational case-control study. Univariate and multivariate models were designed to test the prognostic value of inflammatory markers (CRP and leukocytosis) in predicting clinical outcomes (modified Rankin score >2) and all-cause mortality 90 days after MT. Results: A total of 676 ischemic stroke patients treated with MT were included. Of these, 313 (46.3%) showed elevated CRP levels (≥5 mg/l) on admission. Poor clinical outcome and mortality at 90 days occurred in 113 (16.7%) and 335 (49.6%) patients and significantly more frequently when initial CRP levels were elevated [213 (64.5%) vs. 122 (42.1%), p < 0.0001, and 79 (25.2%) vs. 34 (9.4%), p < 0.0001, respectively]. CRP levels were highly predictive for impaired outcomes, especially in patients with atrial fibrillation, in both univariate and multivariate models. Interestingly, patients with initially elevated CRP levels also showed more pronounced increases in CRP post-MT. Conclusion: Poor outcome and death occur significantly more often in stroke patients with elevated CRP levels before MT. Our findings suggest that stroke patients with atrial fibrillation and elevated inflammatory markers are of particular risk for poor outcomes.

Brain white-matter changes associated with symptomatic acute COVID-19 infection in the neonatal period.

We report an important case of periventricular white matter damage in a 1-month-old infant, demonstrated on high quality structural (T2) and diffusion weighted magnetic resonance imaging. The infant was born at term following an uneventful pregnancy and discharged home shortly after, but was brought to the paediatric emergency department five days after birth with seizures and respiratory distress, testing positive for COVID-19 infection on PCR. These images highlight the need to consider brain MRI in all infants with symptomatic SARS-Cov-2 infection, and show how this infection can lead to extensive white matter damage in the context of multisystem inflammation.

Longitudinal Assessment of Multiple Sclerosis Lesion Load With Synthetic Magnetic Resonance Imaging-A Multicenter Validation Study.

INTRODUCTION: Double inversion recovery (DIR) has been validated as a sensitive magnetic resonance imaging (MRI) contrast in multiple sclerosis (MS). Deep learning techniques can use basic input data to generate synthetic DIR (synthDIR) images that are on par with their acquired counterparts. As assessment of longitudinal MRI data is paramount in MS diagnostics, our study's purpose is to evaluate the utility of synthDIR longitudinal subtraction imaging for detection of disease progression in a multicenter data set of MS patients. METHODS: We implemented a previously established generative adversarial network to synthesize DIR from input T1-weighted and fluid-attenuated inversion recovery (FLAIR) sequences for 214 MRI data sets from 74 patients and 5 different centers. One hundred and forty longitudinal subtraction maps of consecutive scans (follow-up scan-preceding scan) were generated for both acquired FLAIR and synthDIR. Two readers, blinded to the image origin, independently quantified newly formed lesions on the FLAIR and synthDIR subtraction maps, grouped into specific locations as outlined in the McDonald criteria. RESULTS: Both readers detected significantly more newly formed MS-specific lesions in the longitudinal subtractions of synthDIR compared with acquired FLAIR (R1: 3.27 ± 0.60 vs 2.50 ± 0.69 [ P = 0.0016]; R2: 3.31 ± 0.81 vs 2.53 ± 0.72 [ P < 0.0001]). Relative gains in detectability were most pronounced in juxtacortical lesions (36% relative gain in lesion counts-pooled for both readers). In 5% of the scans, synthDIR subtraction maps helped to identify a disease progression missed on FLAIR subtraction maps. CONCLUSIONS: Generative adversarial networks can generate high-contrast DIR images that may improve the longitudinal follow-up assessment in MS patients compared with standard sequences. By detecting more newly formed MS lesions and increasing the rates of detected disease activity, our methodology promises to improve clinical decision-making.

Uncertainty-Aware and Lesion-Specific Image Synthesis in Multiple Sclerosis Magnetic Resonance Imaging: A Multicentric Validation Study.

Generative adversarial networks (GANs) can synthesize high-contrast MRI from lower-contrast input. Targeted translation of parenchymal lesions in multiple sclerosis (MS), as well as visualization of model confidence further augment their utility, provided that the GAN generalizes reliably across different scanners. We here investigate the generalizability of a refined GAN for synthesizing high-contrast double inversion recovery (DIR) images and propose the use of uncertainty maps to further enhance its clinical utility and trustworthiness. A GAN was trained to synthesize DIR from input fluid-attenuated inversion recovery (FLAIR) and T1w of 50 MS patients (training data). In another 50 patients (test data), two blinded readers (R1 and R2) independently quantified lesions in synthetic DIR (synthDIR), acquired DIR (trueDIR) and FLAIR. Of the 50 test patients, 20 were acquired on the same scanner as training data (internal data), while 30 were scanned at different scanners with heterogeneous field strengths and protocols (external data). Lesion-to-Background ratios (LBR) for MS-lesions vs. normal appearing white matter, as well as image quality parameters were calculated. Uncertainty maps were generated to visualize model confidence. Significantly more MS-specific lesions were found in synthDIR compared to FLAIR (R1: 26.7 ± 2.6 vs. 22.5 ± 2.2 p < 0.0001; R2: 22.8 ± 2.2 vs. 19.9 ± 2.0, p = 0.0005). While trueDIR remained superior to synthDIR in R1 [28.6 ± 2.9 vs. 26.7 ± 2.6 (p = 0.0021)], both sequences showed comparable lesion conspicuity in R2 [23.3 ± 2.4 vs. 22.8 ± 2.2 (p = 0.98)]. Importantly, improvements in lesion counts were similar in internal and external data. Measurements of LBR confirmed that lesion-focused GAN training significantly improved lesion conspicuity. The use of uncertainty maps furthermore helped discriminate between MS lesions and artifacts. In conclusion, this multicentric study confirms the external validity of a lesion-focused Deep-Learning tool aimed at MS imaging. When implemented, uncertainty maps are promising to increase the trustworthiness of synthetic MRI.

Faster and Better: How Anomaly Detection Can Accelerate and Improve Reporting of Head Computed Tomography.

BACKGROUND: Most artificial intelligence (AI) systems are restricted to solving a pre-defined task, thus limiting their generalizability to unselected datasets. Anomaly detection relieves this shortfall by flagging all pathologies as deviations from a learned norm. Here, we investigate whether diagnostic accuracy and reporting times can be improved by an anomaly detection tool for head computed tomography (CT), tailored to provide patient-level triage and voxel-based highlighting of pathologies. METHODS: Four neuroradiologists with 1-10 years of experience each investigated a set of 80 routinely acquired head CTs containing 40 normal scans and 40 scans with common pathologies. In a random order, scans were investigated with and without AI-predictions. A 4-week wash-out period between runs was included to prevent a reminiscence effect. Performance metrics for identifying pathologies, reporting times, and subjectively assessed diagnostic confidence were determined for both runs. RESULTS: AI-support significantly increased the share of correctly classified scans (normal/pathological) from 309/320 scans to 317/320 scans (p = 0.0045), with a corresponding sensitivity, specificity, negative- and positive- predictive value of 100%, 98.1%, 98.2% and 100%, respectively. Further, reporting was significantly accelerated with AI-support, as evidenced by the 15.7% reduction in reporting times (65.1 ± 8.9 s vs. 54.9 ± 7.1 s; p < 0.0001). Diagnostic confidence was similar in both runs. CONCLUSION: Our study shows that AI-based triage of CTs can improve the diagnostic accuracy and accelerate reporting for experienced and inexperienced radiologists alike. Through ad hoc identification of normal CTs, anomaly detection promises to guide clinicians towards scans requiring urgent attention.

Improving Automated Glioma Segmentation in Routine Clinical Use Through Artificial Intelligence-Based Replacement of Missing Sequences With Synthetic Magnetic Resonance Imaging Scans.

OBJECTIVES: Although automated glioma segmentation holds promise for objective assessment of tumor biology and response, its routine clinical use is impaired by missing sequences, for example, due to motion artifacts. The aim of our study was to develop and validate a generative adversarial network for synthesizing missing sequences to allow for a robust automated segmentation. MATERIALS AND METHODS: Our model was trained on data from The Cancer Imaging Archive (n = 238 WHO II-IV gliomas) to synthesize either missing FLAIR, T2-weighted, T1-weighted (T1w), or contrast-enhanced T1w images from available sequences, using a novel tumor-targeting loss to improve synthesis of tumor areas. We validated performance in a test set from both the REMBRANDT repository and our local institution (n = 68 WHO II-IV gliomas), using qualitative image appearance metrics, but also segmentation performance with state-of-the-art segmentation models. Segmentation of synthetic images was compared with 2 commonly used strategies for handling missing input data, entering a blank mask or copying an existing sequence. RESULTS: Across tumor areas and missing sequences, synthetic images generally outperformed both conventional approaches, in particular when FLAIR was missing. Here, for edema and whole tumor segmentation, we improved the Dice score, a common metric for evaluation of segmentation performance, by 12% and 11%, respectively, over the best conventional method. No method was able to reliably replace missing contrast-enhanced T1w images. DISCUSSION: Replacing missing nonenhanced magnetic resonance sequences via synthetic images significantly improves segmentation quality over most conventional approaches. This model is freely available and facilitates more widespread use of automated segmentation in routine clinical use, where missing sequences are common.

Automated Detection of Ischemic Stroke and Subsequent Patient Triage in Routinely Acquired Head CT.

PURPOSE: Advanced machine-learning (ML) techniques can potentially detect the entire spectrum of pathology through deviations from a learned norm. We investigated the utility of a weakly supervised ML tool to detect characteristic findings related to ischemic stroke in head CT and provide subsequent patient triage. METHODS: Patients having undergone non-enhanced head CT at a tertiary care hospital in April 2020 with either no anomalies, subacute or chronic ischemia, lacunar infarcts of the deep white matter or hyperdense vessel signs were retrospectively analyzed. Anomaly detection was performed using a weakly supervised ML classifier. Findings were displayed on a voxel-level (heatmap) and pooled to an anomaly score. Thresholds for this score classified patients into i) normal, ii) inconclusive, iii) pathological. Expert-validated radiological reports were considered as ground truth. Test assessment was performed with ROC analysis; inconclusive results were pooled to pathological predictions for accuracy measurements. RESULTS: During the investigation period 208 patients were referred for head CT of which 111 could be included. Definite ratings into normal/pathological were feasible in 77 (69.4%) patients. Based on anomaly scores, the AUC to differentiate normal from pathological scans was 0.98 (95% CI 0.97-1.00). The sensitivity, specificity, positive and negative predictive values were 100%, 40.6%, 80.6% and 100%, respectively. CONCLUSION: Our study demonstrates the potential of a weakly supervised anomaly-detection tool to detect stroke findings in head CT. Definite classification into normal/pathological was made with high accuracy in > 2/3 of patients. Anomaly heatmaps further provide guidance towards pathologies, also in cases with inconclusive ratings.

Radiation Dose and Image Quality of a High-Pitch Prospective Spiral First Approach in Coronary Computed Tomography Angiography (CCTA).

Objective: To investigate a high-pitch spiral first (HPSF) approach for coronary computed tomography angiography (CCTA) in an unselected patient cohort and compare diagnostic yield and radiation exposure to CCTAs acquired via conventional, non-high-pitch spiral first (NHPSF) scan regimes. Materials and Methods: All consecutive patients from 1 January 2015 to 31 December 2017 were included. Two investigation protocols (HPSF/NHPSF) were used with the aim to achieve diagnostic image quality of all coronary segments. Low-pitch secondary scans followed the initial examination if image quality was unsatisfactory. Dosage and image quality were compared between both regimes. Results: 1410 patients were subject to a HPSF and 236 patients to a NHPSF approach. While the HPSF approach led to a higher fraction of re-scans (35% vs. 11%, p < 0.001), the fraction of aggregate scans that remained non-diagnostic after considering the initial and secondary scan was comparably low for the HPSF and NHPSF approach (0.78 vs. 0%, p = 0.18). Aggregate radiation exposure in the HPSF protocol was significantly lower (1.12 mSv (IQR: 0.73, 2.10) vs. 3.96 mSv (IQR: 2.23, 8.33) p < 0.001). Conclusions: In spite of a higher number of re-scans, a HPSF approach leads to a reduction in overall radiation exposure with diagnostic yields similar to a NHPSF approach.

Automated Pathology Detection and Patient Triage in Routinely Acquired Head Computed Tomography Scans.

OBJECTIVES: Anomaly detection systems can potentially uncover the entire spectrum of pathologies through deviations from a learned norm, meaningfully supporting the radiologist's workflow. We aim to report on the utility of a weakly supervised machine learning (ML) tool to detect pathologies in head computed tomography (CT) and adequately triage patients in an unselected patient cohort. MATERIALS AND METHODS: All patients having undergone a head CT at a tertiary care hospital in March 2020 were eligible for retrospective analysis. Only the first scan of each patient was included. Anomaly detection was performed using a weakly supervised ML technique. Anomalous findings were displayed on voxel-level and pooled to an anomaly score ranging from 0 to 1. Thresholds for this score classified patients into the 3 classes: "normal," "pathological," or "inconclusive." Expert-validated radiological reports with multiclass pathology labels were considered as ground truth. Test assessment was performed with receiver operator characteristics analysis; inconclusive results were pooled to "pathological" predictions for accuracy measurements. External validity was tested in a publicly available external data set (CQ500). RESULTS: During the investigation period, 297 patients were referred for head CT of which 248 could be included. Definite ratings into normal/pathological were feasible in 167 patients (67.3%); 81 scans (32.7%) remained inconclusive. The area under the curve to differentiate normal from pathological scans was 0.95 (95% confidence interval, 0.92-0.98) for the study data set and 0.87 (95% confidence interval, 0.81-0.94) in external validation. The negative predictive value to exclude pathology if a scan was classified as "normal" was 100% (25/25), and the positive predictive value was 97.6% (137/141). Sensitivity and specificity were 100% and 86%, respectively. In patients with inconclusive ratings, pathologies were found in 26 (63%) of 41 cases. CONCLUSIONS: Our study provides the first clinical evaluation of a weakly supervised anomaly detection system for brain imaging. In an unselected, consecutive patient cohort, definite classification into normal/diseased was feasible in approximately two thirds of scans, going along with an excellent diagnostic accuracy and perfect negative predictive value for excluding pathology. Moreover, anomaly heat maps provide important guidance toward pathology interpretation, also in cases with inconclusive ratings.

MR imaging by 3D T1-weighted black blood sequences may improve delineation of therapy-naive high-grade gliomas.

OBJECTIVES: To investigate the value of contrast-enhanced (CE) turbo spin echo black blood (BB) sequences for imaging of therapy-naive high-grade gliomas (HGGs). METHODS: Consecutive patients with histopathologically confirmed World Health Organization (WHO) grade III or IV gliomas and no oncological treatment prior to index imaging (March 2019 to January 2020) were retrospectively included. Magnetic resonance imaging (MRI) at 3 Tesla comprised CE BB and CE turbo field echo (TFE) sequences. The lack/presence of tumor-related contrast enhancement and satellite lesions were evaluated by two readers. Sharper delineation of tumor boundaries (1, bad; 2, intermediate; 3, good delineation) and vaster expansion of HGGs into the adjacent brain parenchyma on CE BB imaging were the endpoints. Furthermore, contrast-to-noise ratios (CNRs) were calculated and compared between sequences. RESULTS: Fifty-four patients were included (mean age: 61.2 ± 15.9 years, 64% male). The vast majority of HGGs (51/54) showed contrast enhancement in both sequences, while two HGGs as well as one of six detected satellite lesions were depicted in CE BB imaging only. Tumor boundaries were significantly sharper (R1: 2.43 ± 0.71 vs. 2.73 ± 0.62, p < 0.001; R2: 2.44 ± 0.74 vs. 2.77 ± 0.60, p = 0.001), while the spread of HGGs into the adjacent parenchyma was larger when considering CE BB sequences according to both readers (larger spread in CE BB sequences: R1: 23 patients; R2: 20 patients). The CNR for CE BB sequences significantly exceeded that of CE TFE sequences (43.4 ± 27.1 vs. 32.5 ± 25.0, p = 0.0028). CONCLUSIONS: Our findings suggest that BB imaging may considerably improve delineation of therapy-naive HGGs when compared with established TFE imaging. Thus, CE BB sequences might supplement MRI protocols for brain tumors. KEY POINTS: • This study investigated contrast-enhanced (CE) T1-weighted black blood (BB) sequences for improved MRI in patients with therapy-naive high-grade gliomas (HGGs). • Compared with conventionally used turbo field echo (TFE) sequences, CE BB sequences depicted tumor boundaries and spread of HGGs into adjacent parenchyma considerably better, which also showed higher CNRs. • Two enhancing tumor masses and one satellite lesion were exclusively identified in CE BB sequences, but remained undetected in conventionally used CE TFE sequences.

Bornavirus Encephalitis Shows a Characteristic Magnetic Resonance Phenotype in Humans.

OBJECTIVE: The number of diagnosed fatal encephalitis cases in humans caused by the classical Borna disease virus (BoDV-1) has been increasing, ever since it was proved that BoDV-1 can cause human infections. However, awareness of this entity is low, and a specific imaging pattern has not yet been identified. We therefore provide the first comprehensive description of the morphology of human BoDV-1 encephalitis, with histopathological verification of imaging abnormalities. METHODS: In an institutional review board-approved multicenter study, we carried out a retrospective analysis of 55 magnetic resonance imaging (MRI) examinations of 19 patients with confirmed BoDV-1 encephalitis. Fifty brain regions were analyzed systematically (T1w, T2w, T2*w, T1w + Gd, and DWI), in order to discern a specific pattern of inflammation. Histopathological analysis of 25 locations in one patient served as correlation for MRI abnormalities. RESULTS: Baseline imaging, acquired at a mean of 11 ± 10 days after symptom onset, in addition to follow-up scans of 16 patients, revealed characteristic T2 hyperintensities with a predilection for the head of the caudate nucleus, insula, and cortical spread to the limbic system, whereas the occipital lobes and cerebellar hemispheres were unaffected. This gradient was confirmed by histology. Nine patients (47.4%) developed T1 hyperintensities of the basal ganglia, corresponding to accumulated lipid phagocytes on histology and typical for late-stage necrosis. INTERPRETATION: BoDV-1 encephalitis shows a distinct pattern of inflammation in both the early and late stages of the disease. Its appearance can mimic sporadic Creutzfeldt-Jakob disease on MRI and should be considered a differential diagnosis in the case of atypical clinical presentation. ANN NEUROL 2020;88:723-735.

Assessment of the Extent of Resection in Surgery of High-Grade Glioma-Evaluation of Black Blood Sequences for Intraoperative Magnetic Resonance Imaging at 3 Tesla.

Achieving an optimal extent of resection (EOR) whilst keeping lasting neurological decline to a minimum is paramount for modern neurosurgery in patients with high-grade glioma (HGG). To improve EOR assessment, this study introduces Black Blood (BB) imaging, which uses a selective saturation pulse to suppress the blood signal, to 3-Tesla intraoperative magnetic resonance imaging (iMRI). Seventy-three patients (56.4 ± 13.9 years, 64.4% male) with contrast-enhancing HGGs underwent iMRI, including contrast-enhanced (CE) and non-CE 3D turbo field-echo imaging (TFE; acquisition time: 4:20 min per sequence) and CE and non-CE 3D BB imaging (acquisition time: 1:36 min per sequence). Two readers (R1 and R2) retrospectively evaluated the EOR and diagnostic confidence (1-very inconfident to 5-very confident) as well as the delineation of tumor boarders and spread of contrast-enhancing tumor components (in case of contrast-enhancing tumor residuals). Furthermore, the contrast-to-noise ratio (CNR) was measured for contrast-enhancing tumor residuals. Both BB and conventional TFE imaging allowed for the correct detection of all contrast-enhancing tumor residuals intraoperatively (considering postsurgical MRI and histopathological evaluation as the ground truth for determination of the lack/presence of contrast-enhancing tumor residuals), but BB imaging showed significantly higher diagnostic confidence (R1: 4.65 ± 0.53 vs. 3.88 ± 1.02, p < 0.0001; R2: 4.75 ± 0.50 vs. 4.25 ± 0.81, p < 0.0001). Delineation of contrast-enhancing tumor residuals and detection of their spread into adjacent brain parenchyma was better for BB imaging. Accordingly, significantly higher CNRs were noted for BB imaging (48.1 ± 32.1 vs. 24.4 ± 15.3, p < 0.0001). In conclusion, BB imaging is not inferior to conventional TFE imaging for EOR assessment, but may significantly reduce scanning time for iMRI whilst increasing diagnostic confidence. Furthermore, given the better depiction of contrast-enhancing tumor residual spread and borders, BB imaging could support achieving complete macroscopic resection in patients suffering from HGG, which is clinically relevant as an optimal EOR is correlated to prolonged survival.

Normative brain volume reports may improve differential diagnosis of dementing neurodegenerative diseases in clinical practice.

OBJECTIVES: Normative brain volume reports (NBVRs) are becoming more and more available for the workup of dementia patients in clinical routine. However, it is yet unknown how this information can be used in the radiological decision-making process. The present study investigates the diagnostic value of NBVRs for detection and differential diagnosis of distinct regional brain atrophy in several dementing neurodegenerative disorders. METHODS: NBVRs were obtained for 81 consecutive patients with distinct dementing neurodegenerative diseases and 13 healthy controls (HC). Forty Alzheimer's disease (AD; 18 with dementia, 22 with mild cognitive impairment (MCI), 11 posterior cortical atrophy (PCA)), 20 frontotemporal dementia (FTD), and ten semantic dementia (SD) cases were analyzed, and reports were tested qualitatively for the representation of atrophy patterns. Gold standard diagnoses were based on the patients' clinical course, FDG-PET imaging, and/or cerebrospinal fluid (CSF) biomarkers following established diagnostic criteria. Diagnostic accuracy of pattern representations was calculated. RESULTS: NBVRs improved the correct identification of patients vs. healthy controls based on structural MRI for rater 1 (p < 0.001) whereas the amount of correct classifications was rather unchanged for rater 2. Correct differential diagnosis of dementing neurodegenerative disorders was significantly improved for both rater 1 (p = 0.001) and rater 2 (p = 0.022). Furthermore, interrater reliability was improved from moderate to excellent for both detection and differential diagnosis of neurodegenerative diseases (κ = 0.556/0.894 and κ = 0.403/0.850, respectively). CONCLUSION: NBVRs deliver valuable and observer-independent information, which can improve differential diagnosis of neurodegenerative diseases. KEY POINTS: • Normative brain volume reports increase detection of neurodegenerative atrophy patterns compared to visual reading alone. • Differential diagnosis of regionally distinct atrophy patterns is improved. • Agreement between radiologists is significantly improved from moderate to excellent when using normative brain volume reports.

Deep-Learning Generated Synthetic Double Inversion Recovery Images Improve Multiple Sclerosis Lesion Detection.

OBJECTIVES: The aim of the study was to implement a deep-learning tool to produce synthetic double inversion recovery (synthDIR) images and compare their diagnostic performance to conventional sequences in patients with multiple sclerosis (MS). MATERIALS AND METHODS: For this retrospective analysis, 100 MS patients (65 female, 37 [22-68] years) were randomly selected from a prospective observational cohort between 2014 and 2016. In a subset of 50 patients, an artificial neural network (DiamondGAN) was trained to generate a synthetic DIR (synthDIR) from standard acquisitions (T1, T2, and fluid-attenuated inversion recovery [FLAIR]). With the resulting network, synthDIR was generated for the remaining 50 subjects. These images as well as conventionally acquired DIR (trueDIR) and FLAIR images were assessed for MS lesions by 2 independent readers, blinded to the source of the DIR image. Lesion counts in the different modalities were compared using a Wilcoxon signed-rank test, and interrater analysis was performed. Contrast-to-noise ratios were compared for objective image quality. RESULTS: Utilization of synthDIR allowed to detect significantly more lesions compared with the use of FLAIR images (31.4 ± 20.7 vs 22.8 ± 12.7, P < 0.001). This improvement was mainly attributable to an improved depiction of juxtacortical lesions (12.3 ± 10.8 vs 7.2 ± 5.6, P < 0.001). Interrater reliability was excellent in FLAIR 0.92 (95% confidence interval [CI], 0.85-0.95), synthDIR 0.93 (95% CI, 0.87-0.96), and trueDIR 0.95 (95% CI, 0.85-0.98).Contrast-to-noise ratio in synthDIR exceeded that of FLAIR (22.0 ± 6.4 vs 16.7 ± 3.6, P = 0.009); no significant difference was seen in comparison to trueDIR (22.0 ± 6.4 vs 22.4 ± 7.9, P = 0.87). CONCLUSIONS: Computationally generated DIR images improve lesion depiction compared with the use of standard modalities. This method demonstrates how artificial intelligence can help improving imaging in specific pathologies.

Long-term prognostic value of morphological plaque features on coronary computed tomography angiography.

AIMS: To investigate the incremental prognostic value of morphological plaque features beyond clinical risk and coronary stenosis levels. Although associated with the degree of coronary stenosis, most cardiac events occur on the basis of ruptured non-obstructive plaques and consecutive vessel thrombosis. As such, identification of vulnerable plaques is paramount for cardiovascular risk prediction and treatment decisions. METHODS AND RESULTS: A total of 1615 patients with suspected but not previously diagnosed coronary artery disease (CAD) were examined by coronary computed tomography angiography and morphological plaque features were assessed. Mean follow-up was 10.5 (interquartile range 9.2-11.4) years. Cox proportional hazards analysis was used for the composite endpoint of cardiac death and non-fatal myocardial infarction. The study endpoint was reached in 51 patients (36 cardiac deaths, 15 non-fatal myocardial infarctions). In addition to quantitative parameters (presence of any calcified/non-calcified plaque or elevated plaque load), morphologic plaque features such as a spotty or gross calcification pattern and napkin-ring sign (NRS) were predictive for events. However, only spotty calcified plaques and NRS could confer additive prognostic value beyond clinical risk and coronary stenosis level. In a stepwise approach, endpoint prediction beyond clinical risk (Morise score) could be improved by inclusion of CAD severity (χ2 of 27.5, P < 0.001) and further discrimination for spotty calcified plaques (χ2 of 3.89, P = 0.049). CONCLUSION: Improved cardiovascular risk prediction beyond clinical risk and coronary stenosis levels can be made by discriminating for the presence of spotty calcified plaques. Thus, an intensified prophylactic anti-atherosclerotic treatment appears to be warranted in patients with coronary plaques that show spotty calcifications.

Coronary computed tomography angiography as a tool for long-term cardiovascular risk stratification in diabetic patients.

Objectives of the study were to examine the long-term prognostic power of coronary computed tomography angiography (CCTA) to predict death or myocardial infarction in patients with diabetes mellitus (DM). The prognostic value of CCTA in diabetic patients has been confirmed for short- and intermediate follow-up durations. The slowly progressing nature of coronary artery disease (CAD), however, underlines the necessity to validate CCTA for longer observation periods in this high-risk population. A total of 132 patients with DM and 1781 without DM were examined by CCTA and followed for a median duration of 9.7 (IQR 6.9, 11.2) and 9.9 (IQR 6.9, 11.1) years, respectively. Cox proportional hazards analysis was used for the composite endpoint of death and myocardial infarction. Warranty period was defined as the number of years that an individual stays in a low-risk group with a cumulative probability for the endpoint below 1% and calculated for patients with/without DM and rising degrees of CAD. The study endpoint was reached in 12 (9.1%) patients with and 87 (4.9%) patients without DM (p = 0.024). Quantification of coronary stenosis by CADRADS or CAD severity (normal/non-obstructive/obstructive) was incremental for endpoint prediction with a multivariate (+Morise) χ2 of 3.90 and 3.85, respectively. The lowest annual event rate of 0.19% was noted in non-diabetic patients with no CAD, translating to a warranty period of 5.26 years. The highest annual event rate of 1.73% was found in diabetic patients with obstructive CAD, corresponding to a warranty period of 0.58 years. Compared to patients with no DM and no CAD, the risk of death or myocardial infarction in diabetic patients increased with rising levels of coronary obstruction at multivariate hazard ratios (HR) of 3.28 [95% CI 2.32, 4.64 (p < 0.001)], 3.02 [95% CI 2.19, 4.17 (p < 0.001)] and 9.40 [95% CI 4.90, 18.03 (p < 0.001)] for normal coronary arteries, non-obstructive CAD and obstructive CAD. This study validates the long-term prognostic utility of CCTA-assessed CAD for predicting death or myocardial infarction in a population of patients with DM. The rates of death or myocardial infarction rise with CAD severity in diabetic and non-diabetic patients, identifying the highest risk group of patients with DM and obstructive CAD.

10-Year Follow-Up After Coronary Computed Tomography Angiography in Patients With Suspected Coronary Artery Disease.

OBJECTIVES: The aim of this study was to determine the long-term prognostic power of coronary computed tomography angiography (CTA) to predict cardiac death and nonfatal myocardial infarction. BACKGROUND: Prognostic usefulness of coronary CTA has been confirmed for short- and intermediate-term follow-up. However, long-term data for prognostic usefulness is still lacking, but is paramount because of the slowly progressing nature of coronary artery disease (CAD). METHODS: A total of 2,011 patients with suspected but not previously diagnosed CAD were examined by coronary CTA. Mean follow-up was 10.0 years (interquartile range [IQR]: 8.1 to 11.2 years). Cox proportional hazards analysis was used for the composite endpoint of cardiac death and nonfatal myocardial infarction. Event-free survival, which was defined as the years it took to reach a cumulative 1% risk for the composite endpoint and reclassification from clinical risk, was calculated. RESULTS: The study endpoint was reached in 58 patients (42 cardiac deaths, 16 nonfatal myocardial infarctions). Coronary CTA-assessed CAD severity (normal, nonobstructive, or obstructive) showed the best correlation with the endpoint, with an adjusted c-index of 0.704, compared with a univariate c-index of 0.622 for the clinical risk model (Morise score) alone. The annual event rate for patients with normal coronary arteries on baseline coronary CTA was 0.04%, which translated to an event-free survival period of 10 years. The highest annual event rate of 1.33% was found in patients with 3-vessel obstructive CAD. Reclassification from clinical risk (Morise score) was possible in approximately two-thirds of all patients (68%; p < 0.0001), which led to a substantial reduction of the intermediate-risk group (reduction from 74% to 15%) in favor of the low-risk group (increase from 20% to 83%). CONCLUSIONS: Patients with normal coronary CTA results benefitted from an event-free survival period of 10 years against cardiac death and nonfatal myocardial infarction. Risk stratification according to coronary CTA results allowed for the delineation of clearly diverging prognostic groups and reclassified approximately two-thirds of all patients from clinical risk groups.