GD2-CAR T cell therapy for H3K27M-mutated diffuse midline gliomas.

Diffuse intrinsic pontine glioma (DIPG) and other H3K27M-mutated diffuse midline gliomas (DMGs) are universally lethal paediatric tumours of the central nervous system1. We have previously shown that the disialoganglioside GD2 is highly expressed on H3K27M-mutated glioma cells and have demonstrated promising preclinical efficacy of GD2-directed chimeric antigen receptor (CAR) T cells2, providing the rationale for a first-in-human phase I clinical trial (NCT04196413). Because CAR T cell-induced brainstem inflammation can result in obstructive hydrocephalus, increased intracranial pressure and dangerous tissue shifts, neurocritical care precautions were incorporated. Here we present the clinical experience from the first four patients with H3K27M-mutated DIPG or spinal cord DMG treated with GD2-CAR T cells at dose level 1 (1 × 106 GD2-CAR T cells per kg administered intravenously). Patients who exhibited clinical benefit were eligible for subsequent GD2-CAR T cell infusions administered intracerebroventricularly3. Toxicity was largely related to the location of the tumour and was reversible with intensive supportive care. On-target, off-tumour toxicity was not observed. Three of four patients exhibited clinical and radiographic improvement. Pro-inflammatory cytokine levels were increased in the plasma and cerebrospinal fluid. Transcriptomic analyses of 65,598 single cells from CAR T cell products and cerebrospinal fluid elucidate heterogeneity in response between participants and administration routes. These early results underscore the promise of this therapeutic approach for patients with H3K27M-mutated DIPG or spinal cord DMG.

Measuring Quantitative Cerebral Blood Flow in Healthy Children: A Systematic Review of Neuroimaging Techniques.

Cerebral blood flow (CBF) is an important hemodynamic parameter to evaluate brain health. It can be obtained quantitatively using medical imaging modalities such as magnetic resonance imaging and positron emission tomography (PET). Although CBF in adults has been widely studied and linked with cerebrovascular and neurodegenerative diseases, CBF data in healthy children are sparse due to the challenges in pediatric neuroimaging. An understanding of the factors affecting pediatric CBF and its normal range is crucial to determine the optimal CBF measuring techniques in pediatric neuroradiology. This review focuses on pediatric CBF studies using neuroimaging techniques in 32 articles including 2668 normal subjects ranging from birth to 18 years old. A systematic literature search was conducted in PubMed, Embase, and Scopus and reported following the preferred reporting items for systematic reviews and meta-analyses (PRISMA). We identified factors (such as age, gender, mood, sedation, and fitness) that have significant effects on pediatric CBF quantification. We also investigated factors influencing the CBF measurements in infants. Based on this review, we recommend best practices to improve CBF measurements in pediatric neuroimaging. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.

Short- and Long-Term MRI Assessed Hemodynamic Changes in Pediatric Moyamoya Patients After Revascularization.

BACKGROUND: Cerebrovascular reserve (CVR) reflects the capacity of cerebral blood flow (CBF) to change following a vasodilation challenge. Decreased CVR is associated with a higher stroke risk in patients with cerebrovascular diseases. While revascularization can improve CVR and reduce this risk in adult patients with vasculopathy such as those with Moyamoya disease, its impact on hemodynamics in pediatric patients remains to be elucidated. Arterial spin labeling (ASL) is a quantitative MRI technique that can measure CBF, CVR, and arterial transit time (ATT) non-invasively. PURPOSE: To investigate the short- and long-term changes in hemodynamics after bypass surgeries in patients with Moyamoya disease. STUDY TYPE: Longitudinal. POPULATION: Forty-six patients (11 months-18 years, 28 females) with Moyamoya disease. FIELD STRENGTH/SEQUENCE: 3-T, single- and multi-delay ASL, T1-weighted, T2-FLAIR, 3D MRA. ASSESSMENT: Imaging was performed 2 weeks before and 1 week and 6 months after surgical intervention. Acetazolamide was employed to induce vasodilation during the imaging procedure. CBF and ATT were measured by fitting the ASL data to the general kinetic model. CVR was computed as the percentage change in CBF. The mean CBF, ATT, and CVR values were measured in the regions affected by vasculopathy. STATISTICAL TESTS: Pre- and post-revascularization CVR, CBF, and ATT were compared for different regions of the brain. P-values <0.05 were considered statistically significant. RESULTS: ASL-derived CBF in flow territories affected by vasculopathy significantly increased after bypass by 41 ± 31% within a week. At 6 months, CBF significantly increased by 51 ± 34%, CVR increased by 68 ± 33%, and ATT was significantly reduced by 6.6 ± 2.9%. DATA CONCLUSION: There may be short- and long-term improvement in the hemodynamic parameters of pediatric Moyamoya patients after bypass surgery. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Increased confidence of radiomics facilitating pretherapeutic differentiation of BRAF-altered pediatric low-grade glioma.

OBJECTIVES: Currently, the BRAF status of pediatric low-grade glioma (pLGG) patients is determined through a biopsy. We established a nomogram to predict BRAF status non-invasively using clinical and radiomic factors. Additionally, we assessed an advanced thresholding method to provide only high-confidence predictions for the molecular subtype. Finally, we tested whether radiomic features provide additional predictive information for this classification task, beyond that which is embedded in the location of the tumor. METHODS: Random forest (RF) models were trained on radiomic and clinical features both separately and together, to evaluate the utility of each feature set. Instead of using the traditional single threshold technique to convert the model outputs to class predictions, we implemented a double threshold mechanism that accounted for uncertainty. Additionally, a linear model was trained and depicted graphically as a nomogram. RESULTS: The combined RF (AUC: 0.925) outperformed the RFs trained on radiomic (AUC: 0.863) or clinical (AUC: 0.889) features alone. The linear model had a comparable AUC (0.916), despite its lower complexity. Traditional thresholding produced an accuracy of 84.5%, while the double threshold approach yielded 92.2% accuracy on the 80.7% of patients with the highest confidence predictions. CONCLUSION: Models that included radiomic features outperformed, underscoring their importance for the prediction of BRAF status. A linear model performed similarly to RF but with the added benefit that it can be visualized as a nomogram, improving the explainability of the model. The double threshold technique was able to identify uncertain predictions, enhancing the clinical utility of the model. CLINICAL RELEVANCE STATEMENT: Radiomic features and tumor location are both predictive of BRAF status in pLGG patients. We show that they contain complementary information and depict the optimal model as a nomogram, which can be used as a non-invasive alternative to biopsy. KEY POINTS: • Radiomic features provide additional predictive information for the determination of the molecular subtype of pediatric low-grade gliomas patients, beyond what is embedded in the location of the tumor, which has an established relationship with genetic status. • An advanced thresholding method can help to distinguish cases where machine learning models have a high chance of being (in)correct, improving the utility of these models. • A simple linear model performs similarly to a more powerful random forest model at classifying the molecular subtype of pediatric low-grade gliomas but has the added benefit that it can be converted into a nomogram, which may facilitate clinical implementation by improving the explainability of the model.

DEEP MOVEMENT: Deep learning of movie files for management of endovascular thrombectomy.

OBJECTIVES: Treatment and outcomes of acute stroke have been revolutionised by mechanical thrombectomy. Deep learning has shown great promise in diagnostics but applications in video and interventional radiology lag behind. We aimed to develop a model that takes as input digital subtraction angiography (DSA) videos and classifies the video according to (1) the presence of large vessel occlusion (LVO), (2) the location of the occlusion, and (3) the efficacy of reperfusion. METHODS: All patients who underwent DSA for anterior circulation acute ischaemic stroke between 2012 and 2019 were included. Consecutive normal studies were included to balance classes. An external validation (EV) dataset was collected from another institution. The trained model was also used on DSA videos post mechanical thrombectomy to assess thrombectomy efficacy. RESULTS: In total, 1024 videos comprising 287 patients were included (44 for EV). Occlusion identification was achieved with 100% sensitivity and 91.67% specificity (EV 91.30% and 81.82%). Accuracy of location classification was 71% for ICA, 84% for M1, and 78% for M2 occlusions (EV 73, 25, and 50%). For post-thrombectomy DSA (n = 194), the model identified successful reperfusion with 100%, 88%, and 35% for ICA, M1, and M2 occlusion (EV 89, 88, and 60%). The model could also perform classification of post-intervention videos as mTICI < 3 with an AUC of 0.71. CONCLUSIONS: Our model can successfully identify normal DSA studies from those with LVO and classify thrombectomy outcome and solve a clinical radiology problem with two temporal elements (dynamic video and pre and post intervention). KEY POINTS: • DEEP MOVEMENT represents a novel application of a model applied to acute stroke imaging to handle two types of temporal complexity, dynamic video and pre and post intervention. • The model takes as an input digital subtraction angiograms of the anterior cerebral circulation and classifies according to (1) the presence or absence of large vessel occlusion, (2) the location of the occlusion, and (3) the efficacy of thrombectomy. • Potential clinical utility lies in providing decision support via rapid interpretation (pre thrombectomy) and automated objective gradation of thrombectomy outcomes (post thrombectomy).

Ferumoxytol-Enhanced MRI in Children and Young Adults: State of the Art.

Ferumoxytol is an ultrasmall iron oxide nanoparticle that was originally approved by the FDA in 2009 for IV treatment of iron deficiency in adults with chronic kidney disease. Subsequently, its off-label use as an MRI contrast agent increased in clinical practice, particularly in pediatric patients in North America. Unlike conventional MRI contrast agents that are based on the rare earth metal gadolinium (gadolinium-based contrast agents), ferumoxytol is biodegradable and carries no potential risk of nephrogenic systemic fibrosis. At FDA-approved doses, ferumoxytol shows no long-term tissue retention in patients with intact iron metabolism. Ferumoxytol provides unique MRI properties, including long-lasting vascular retention (facilitating high-quality vascular imaging) and retention in reticuloendothelial system tissues, thereby supporting a variety of applications beyond those possible with gadolinium-based contrast agents (GBCAs). This Clinical Perspective describes clinical and early translational applications of ferumoxytol-enhanced MRI in children and young adults through off-label use in a variety of settings, including vascular, cardiac, and cancer imaging, drawing on the institutional experience of the authors. In addition, we describe current advances in pre-clinical and clinical research using ferumoxytol in cellular and molecular imaging as well as the use of ferumoxytol as a novel potential cancer therapeutic agent.

Acetazolamide-Challenged Arterial Spin Labeling Detects Augmented Cerebrovascular Reserve After Surgery for Moyamoya.

BACKGROUND: Cerebrovascular reserve (CVR) inversely correlates with stroke risk in children with Moyamoya disease and may be improved by revascularization surgery. We hypothesized that acetazolamide-challenged arterial spin labeling MR perfusion quantifies augmentation of CVR achieved by revascularization and correlates with currently accepted angiographic scoring criteria. METHODS: We retrospectively identified pediatric patients with Moyamoya disease or syndrome who received cerebral revascularization at ≤18 years of age between 2012 and 2019 at our institution. Using acetazolamide-challenged arterial spin labeling, we compared postoperative CVR to corresponding preoperative values and to postoperative perfusion outcomes classified by Matsushima grading. RESULTS: In this cohort, 32 patients (17 males) with Moyamoya underwent 29 direct and 16 indirect extracranial-intracranial bypasses at a median 9.7 years of age (interquartile range, 7.6-15.7). Following revascularization, median CVR increased within the ipsilateral middle cerebral artery territory (6.9 mL/100 g per minute preoperatively versus 16.5 mL/100 g per minute postoperatively, P<0.01). No differences were observed in the ipsilateral anterior cerebral artery (P=0.13) and posterior cerebral artery (P=0.48) territories. Postoperative CVR was higher in the ipsilateral middle cerebral artery territories of patients who achieved Matsushima grade A perfusion, in comparison to those with grades B or C (25.8 versus 17.5 mL, P=0.02). The method of bypass (direct or indirect) did not alter relative increases in CVR (8 versus 3.8 mL/100 g per minute, P=0.7). CONCLUSIONS: Acetazolamide-challenged arterial spin labeling noninvasively quantifies augmentation of CVR following surgery for Moyamoya disease and syndrome.

MRI Radiogenomics of Pediatric Medulloblastoma: A Multicenter Study.

Background Radiogenomics of pediatric medulloblastoma (MB) offers an opportunity for MB risk stratification, which may aid therapeutic decision making, family counseling, and selection of patient groups suitable for targeted genetic analysis. Purpose To develop machine learning strategies that identify the four clinically significant MB molecular subgroups. Materials and Methods In this retrospective study, consecutive pediatric patients with newly diagnosed MB at MRI at 12 international pediatric sites between July 1997 and May 2020 were identified. There were 1800 features extracted from T2- and contrast-enhanced T1-weighted preoperative MRI scans. A two-stage sequential classifier was designed-one that first identifies non-wingless (WNT) and non-sonic hedgehog (SHH) MB and then differentiates therapeutically relevant WNT from SHH. Further, a classifier that distinguishes high-risk group 3 from group 4 MB was developed. An independent, binary subgroup analysis was conducted to uncover radiomics features unique to infantile versus childhood SHH subgroups. The best-performing models from six candidate classifiers were selected, and performance was measured on holdout test sets. CIs were obtained by bootstrapping the test sets for 2000 random samples. Model accuracy score was compared with the no-information rate using the Wald test. Results The study cohort comprised 263 patients (mean age ± SD at diagnosis, 87 months ± 60; 166 boys). A two-stage classifier outperformed a single-stage multiclass classifier. The combined, sequential classifier achieved a microaveraged F1 score of 88% and a binary F1 score of 95% specifically for WNT. A group 3 versus group 4 classifier achieved an area under the receiver operating characteristic curve of 98%. Of the Image Biomarker Standardization Initiative features, texture and first-order intensity features were most contributory across the molecular subgroups. Conclusion An MRI-based machine learning decision path allowed identification of the four clinically relevant molecular pediatric medulloblastoma subgroups. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Chaudhary and Bapuraj in this issue.

Radiology artificial intelligence: a systematic review and evaluation of methods (RAISE).

OBJECTIVE: There has been a large amount of research in the field of artificial intelligence (AI) as applied to clinical radiology. However, these studies vary in design and quality and systematic reviews of the entire field are lacking.This systematic review aimed to identify all papers that used deep learning in radiology to survey the literature and to evaluate their methods. We aimed to identify the key questions being addressed in the literature and to identify the most effective methods employed. METHODS: We followed the PRISMA guidelines and performed a systematic review of studies of AI in radiology published from 2015 to 2019. Our published protocol was prospectively registered. RESULTS: Our search yielded 11,083 results. Seven hundred sixty-seven full texts were reviewed, and 535 articles were included. Ninety-eight percent were retrospective cohort studies. The median number of patients included was 460. Most studies involved MRI (37%). Neuroradiology was the most common subspecialty. Eighty-eight percent used supervised learning. The majority of studies undertook a segmentation task (39%). Performance comparison was with a state-of-the-art model in 37%. The most used established architecture was UNet (14%). The median performance for the most utilised evaluation metrics was Dice of 0.89 (range .49-.99), AUC of 0.903 (range 1.00-0.61) and Accuracy of 89.4 (range 70.2-100). Of the 77 studies that externally validated their results and allowed for direct comparison, performance on average decreased by 6% at external validation (range increase of 4% to decrease 44%). CONCLUSION: This systematic review has surveyed the major advances in AI as applied to clinical radiology. KEY POINTS: • While there are many papers reporting expert-level results by using deep learning in radiology, most apply only a narrow range of techniques to a narrow selection of use cases. • The literature is dominated by retrospective cohort studies with limited external validation with high potential for bias. • The recent advent of AI extensions to systematic reporting guidelines and prospective trial registration along with a focus on external validation and explanations show potential for translation of the hype surrounding AI from code to clinic.

Decoding COVID-19 pneumonia: comparison of deep learning and radiomics CT image signatures.

PURPOSE: High-dimensional image features that underlie COVID-19 pneumonia remain opaque. We aim to compare feature engineering and deep learning methods to gain insights into the image features that drive CT-based for COVID-19 pneumonia prediction, and uncover CT image features significant for COVID-19 pneumonia from deep learning and radiomics framework. METHODS: A total of 266 patients with COVID-19 and other viral pneumonia with clinical symptoms and CT signs similar to that of COVID-19 during the outbreak were retrospectively collected from three hospitals in China and the USA. All the pneumonia lesions on CT images were manually delineated by four radiologists. One hundred eighty-four patients (n = 93 COVID-19 positive; n = 91 COVID-19 negative; 24,216 pneumonia lesions from 12,001 CT image slices) from two hospitals from China served as discovery cohort for model development. Thirty-two patients (17 COVID-19 positive, 15 COVID-19 negative; 7883 pneumonia lesions from 3799 CT image slices) from a US hospital served as external validation cohort. A bi-directional adversarial network-based framework and PyRadiomics package were used to extract deep learning and radiomics features, respectively. Linear and Lasso classifiers were used to develop models predictive of COVID-19 versus non-COVID-19 viral pneumonia. RESULTS: 120-dimensional deep learning image features and 120-dimensional radiomics features were extracted. Linear and Lasso classifiers identified 32 high-dimensional deep learning image features and 4 radiomics features associated with COVID-19 pneumonia diagnosis (P < 0.0001). Both models achieved sensitivity > 73% and specificity > 75% on external validation cohort with slight superior performance for radiomics Lasso classifier. Human expert diagnostic performance improved (increase by 16.5% and 11.6% in sensitivity and specificity, respectively) when using a combined deep learning-radiomics model. CONCLUSIONS: We uncover specific deep learning and radiomics features to add insight into interpretability of machine learning algorithms and compare deep learning and radiomics models for COVID-19 pneumonia that might serve to augment human diagnostic performance.

Simultaneous time of flight-MRA and T2* imaging for cerebrovascular MRI.

PURPOSE: 3D multi-echo gradient-recalled echo (ME-GRE) can simultaneously generate time-of-flight magnetic resonance angiography (pTOF) in addition to T2*-based susceptibility-weighted images (SWI). We assessed the clinical performance of pTOF generated from a 3D ME-GRE acquisition compared with conventional TOF-MRA (cTOF). METHODS: Eighty consecutive children were retrospectively identified who obtained 3D ME-GRE alongside cTOF. Two blinded readers independently assessed pTOF derived from 3D ME-GRE and compared them with cTOF. A 5-point Likert scale was used to rank lesion conspicuity and to assess for diagnostic confidence. RESULTS: Across 80 pediatric neurovascular pathologies, a similar number of lesions were reported on pTOF and cTOF (43-40%, respectively, p > 0.05). Rating of lesion conspicuity was higher with cTOF (4.5 ± 1.0) as compared with pTOF (4.0 ± 0.7), but this was not significantly different (p = 0.06). Diagnostic confidence was rated higher with cTOF (4.8 ± 0.5) than that of pTOF (3.7 ± 0.6; p < 0.001). Overall, the inter-rater agreement between two readers for lesion count on pTOF was classified as almost perfect (κ = 0.98, 96% CI 0.8-1.0). CONCLUSIONS: In this study, TOF-MRA simultaneously generated in addition to SWI from 3D MR-GRE can serve as a diagnostic adjunct, particularly for proximal vessel disease and when conventional TOF-MRA images are absent.

Response assessment in paediatric low-grade glioma: recommendations from the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working group.

Paediatric low-grade gliomas (also known as pLGG) are the most common type of CNS tumours in children. In general, paediatric low-grade gliomas show clinical and biological features that are distinct from adult low-grade gliomas, and the developing paediatric brain is more susceptible to toxic late effects of the tumour and its treatment. Therefore, response assessment in children requires additional considerations compared with the adult Response Assessment in Neuro-Oncology criteria. There are no standardised response criteria in paediatric clinical trials, which makes it more difficult to compare responses across studies. The Response Assessment in Pediatric Neuro-Oncology working group, consisting of an international panel of paediatric and adult neuro-oncologists, clinicians, radiologists, radiation oncologists, and neurosurgeons, was established to address issues and unique challenges in assessing response in children with CNS tumours. We established a subcommittee to develop consensus recommendations for response assessment in paediatric low-grade gliomas. Final recommendations were based on literature review, current practice, and expert opinion of working group members. Consensus recommendations include imaging response assessments, with additional guidelines for visual functional outcomes in patients with optic pathway tumours. As with previous consensus recommendations, these recommendations will need to be validated in prospective clinical trials.

Brain Iron Assessment after Ferumoxytol-enhanced MRI in Children and Young Adults with Arteriovenous Malformations: A Case-Control Study.

Background Iron oxide nanoparticles are an alternative contrast agent for MRI. Gadolinium deposition has raised safety concerns, but it is unknown whether ferumoxytol administration also deposits in the brain. Purpose To investigate whether there are signal intensity changes in the brain at multiecho gradient imaging following ferumoxytol exposure in children and young adults. Materials and Methods This retrospective case-control study included children and young adults, matched for age and sex, with brain arteriovenous malformations who received at least one dose of ferumoxytol from January 2014 to January 2018. In participants who underwent at least two brain MRI examinations (subgroup), the first and last available examinations were analyzed. Regions of interests were placed around deep gray structures on quantitative susceptibility mapping and R2* images. Mean susceptibility and R2* values of regions of interests were recorded. Measurements were assessed by linear regression analyses: a between-group comparison of ferumoxytol-exposed and unexposed participants and a within-group (subgroup) comparison before and after exposure. Results Seventeen participants (mean age ± standard deviation, 13 years ± 5; nine male) were in the ferumoxytol-exposed (case) group, 21 (mean age, 14 years ± 5; 11 male) were in the control group, and nine (mean age, 12 years ± 6; four male) were in the subgroup. The mean number of ferumoxytol administrations was 2 ± 1 (range, one to four). Mean susceptibility (in parts per million [ppm]) and R2* (in inverse seconds [sec-1]) values of the dentate (case participants: 0.06 ppm ± 0.04 and 23.87 sec-1 ± 4.13; control participants: 0.02 ppm ± 0.03 and 21.7 sec-1 ± 5.26), substantia nigrae (case participants: 0.08 ppm ± 0.06 and 27.46 sec-1 ± 5.58; control participants: 0.04 ppm ± 0.05 and 24.96 sec-1 ± 5.3), globus pallidi (case participants: 0.14 ppm ± 0.05 and 30.75 sec-1 ± 5.14; control participants: 0.08 ppm ± 0.07 and 28.82 sec-1 ± 6.62), putamina (case participants: 0.03 ppm ± 0.02 and 20.63 sec-1 ± 2.44; control participants: 0.02 ppm ± 0.02 and 19.65 sec-1 ± 3.6), caudate (case participants: -0.1 ppm ± 0.04 and 18.21 sec-1 ± 3.1; control participants: -0.06 ppm ± 0.05 and 18.83 sec-1 ± 3.32), and thalami (case participants: 0 ppm ± 0.03 and 16.49 sec-1 ± 3.6; control participants: 0.02 ppm ± 0.02 and 18.38 sec-1 ± 2.09) did not differ between groups (susceptibility, P = .21; R2*, P = .24). For the subgroup, the mean interval between the first and last ferumoxytol administration was 14 months ± 8 (range, 1-25 months). Mean susceptibility and R2* values of the dentate (first MRI: 0.06 ppm ± 0.05 and 25.78 sec-1 ± 5.9; last MRI: 0.06 ppm ± 0.02 and 25.55 sec-1 ± 4.71), substantia nigrae (first MRI: 0.06 ppm ± 0.06 and 28.26 sec-1 ± 9.56; last MRI: 0.07 ppm ± 0.06 and 25.65 sec-1 ± 6.37), globus pallidi (first MRI: 0.13 ppm ± 0.07 and 27.53 sec-1 ± 8.88; last MRI: 0.14 ppm ± 0.06 and 29.78 sec-1 ± 6.54), putamina (first MRI: 0.03 ppm ± 0.03 and 19.78 sec-1 ± 3.51; last MRI: 0.03 ppm ± 0.02 and 19.73 sec-1 ± 3.01), caudate (first MRI: -0.09 ppm ± 0.05 and 21.38 sec-1 ± 4.72; last MRI: -0.1 ppm ± 0.05 and 18.75 sec-1 ± 2.68), and thalami (first MRI: 0.01 ppm ± 0.02 and 17.65 sec-1 ± 5.16; last MRI: 0 ppm ± 0.02 and 15.32 sec-1 ± 2.49) did not differ between the first and last MRI examinations (susceptibility, P = .95; R2*, P = .54). Conclusion No overall significant differences were found in susceptibility and R2* values of deep gray structures to suggest retained iron in the brain between ferumoxytol-exposed and unexposed children and young adults with arteriovenous malformations and in those exposed to ferumoxytol over time. © RSNA, 2020.

End-to-end automatic differentiation of the coronavirus disease 2019 (COVID-19) from viral pneumonia based on chest CT.

PURPOSE: In the absence of a virus nucleic acid real-time reverse transcriptase-polymerase chain reaction (RT-PCR) test and experienced radiologists, clinical diagnosis is challenging for viral pneumonia with clinical symptoms and CT signs similar to that of coronavirus disease 2019 (COVID-19). We developed an end-to-end automatic differentiation method based on CT images to identify COVID-19 pneumonia patients in real time. METHODS: From January 18 to February 23, 2020, we conducted a retrospective study and enrolled 201 patients from two hospitals in China who underwent chest CT and RT-PCR tests, of which 98 patients tested positive for COVID-19 (118 males and 83 females, with an average age of 42 years). Patient CT images from one hospital were divided among training, validation and test datasets with an 80%:10%:10% ratio. An end-to-end representation learning method using a large-scale bi-directional generative adversarial network (BigBiGAN) architecture was designed to extract semantic features from the CT images. The semantic feature matrix was input for linear classifier construction. Patients from the other hospital were used for external validation. Differentiation accuracy was evaluated using a receiver operating characteristic curve. RESULTS: Based on the 120-dimensional semantic features extracted by BigBiGAN from each image, the linear classifier results indicated that the area under the curve (AUC) in the training, validation and test datasets were 0.979, 0.968 and 0.972, respectively, with an average sensitivity of 92% and specificity of 91%. The AUC for external validation was 0.850, with a sensitivity of 80% and specificity of 75%. Publicly available architecture and computing resources were used throughout the study to ensure reproducibility. CONCLUSION: This study provides an efficient recognition method for coronavirus disease 2019 pneumonia, using an end-to-end design to implement targeted and effective isolation for the containment of this communicable disease.

Neonatal genetic epilepsies display convergent white matter microstructural abnormalities.

White matter undergoes rapid development in the neonatal period. Its structure during and after development is influenced by neuronal activity. Pathological neuronal activity, as in seizures, might alter white matter, which in turn may contribute to network dysfunction. Neonatal epilepsy presents an opportunity to investigate seizures and early white matter development. Our objective was to determine whether neonatal seizures in the absence of brain injury or congenital anomalies are associated with altered white matter microstructure. In this retrospective case-control study of term neonates, cases had confirmed or suspected genetic epilepsy and normal brain magnetic resonance imaging (MRI) and no other conditions independently impacting white matter. Controls were healthy neonates with normal MRI results. White matter microstructure was assessed via quantitative mean diffusivity (MD). In 22 cases, MD was significantly lower in the genu of the corpus callosum, compared to 22 controls, controlling for gestational age and postmenstrual age at MRI. This finding suggests convergent abnormal corpus callosum microstructure in neonatal epilepsies with diverse suspected genetic causes. Further study is needed to determine the specific nature, causes, and functional impact of seizure-associated abnormal white matter in neonates, a potential pathogenic mechanism.

Deep Learning for Automated Classification of Inferior Vena Cava Filter Types on Radiographs.

PURPOSE: To demonstrate the feasibility and evaluate the performance of a deep-learning convolutional neural network (CNN) classification model for automated identification of different types of inferior vena cava (IVC) filters on radiographs. MATERIALS AND METHODS: In total, 1,375 cropped radiographic images of 14 types of IVC filters were collected from patients enrolled in a single-center IVC filter registry, with 139 images withheld as a test set and the remainder used to train and validate the classification model. Image brightness, contrast, intensity, and rotation were varied to augment the training set. A 50-layer ResNet architecture with fixed pre-trained weights was trained using a soft margin loss over 50 epochs. The final model was evaluated on the test set. RESULTS: The CNN classification model achieved a F1 score of 0.97 (0.92-0.99) for the test set overall and of 1.00 for 10 of 14 individual filter types. Of the 139 test set images, 4 (2.9%) were misidentified, all mistaken for other filter types that appear highly similar. Heat maps elucidated salient features for each filter type that the model used for class prediction. CONCLUSIONS: A CNN classification model was successfully developed to identify 14 types of IVC filters on radiographs and demonstrated high performance. Further refinement and testing of the model is necessary before potential real-world application.

Altered cerebral perfusion in children with Langerhans cell histiocytosis after chemotherapy.

BACKGROUND AND PURPOSE: Children with Langerhans cell histiocytosis (LCH) may develop a wide array of neurological symptoms, but associated cerebral physiologic changes are poorly understood. We examined cerebral hemodynamic properties of pediatric LCH using arterial spin-labeling (ASL) perfusion magnetic resonance imaging (MRI). MATERIALS AND METHODS: A retrospective study was performed in 23 children with biopsy-proven LCH. Analysis was performed on routine brain MRI obtained before or after therapy. Region of interest (ROI) methodology was used to determine ASL cerebral blood flow (CBF) (mL/100 g/min) in the following bilateral regions: angular gyrus, anterior prefrontal cortex, orbitofrontal cortex, dorsal anterior cingulate cortex, and hippocampus. Quantile (median) regression was performed for each ROI location. CBF patterns were compared between pre- and posttreatment LCH patients as well as with age-matched healthy controls. RESULTS: Significantly reduced CBF was seen in posttreatment children with LCH compared to age-matched controls in angular gyrus (P = .046), anterior prefrontal cortex (P = .039), and dorsal anterior cingulate cortex (P = .023). Further analysis revealed dominant perfusion abnormalities in the right hemisphere. No significant perfusion differences were observed in the hippocampus or orbitofrontal cortex. CONCLUSION: Perfusion in specific cerebral regions may be consistently reduced in children with LCH, and may represent effects of underlying disease physiology and/or sequelae of chemotherapy. Studies that combine a formal cognitive assessment and hemodynamic data may further provide insight into perfusion deficits associated with the disease and the potential neurotoxic effects in children treated by chemotherapy.

Children with epilepsy demonstrate macro- and microstructural changes in the thalamus, putamen, and amygdala.

PURPOSE: Despite evidence for macrostructural alteration in epilepsy patients later in life, little is known about the underlying pathological or compensatory mechanisms at younger ages causing these alterations. The aim of this work was to investigate the impact of pediatric epilepsy on the central nervous system, including gray matter volume, cerebral blood flow, and water diffusion, compared with neurologically normal children. METHODS: Inter-ictal magnetic resonance imaging data was obtained from 30 children with epilepsy ages 1-16 (73% F, 27% M). An atlas-based approach was used to determine values for volume, cerebral blood flow, and apparent diffusion coefficient in the cerebral cortex, hippocampus, thalamus, caudate, putamen, globus pallidus, amygdala, and nucleus accumbens. These values were then compared with previously published values from 100 neurologically normal children using a MANCOVA analysis. RESULTS: Most brain volumes of children with epilepsy followed a pattern similar to typically developing children, except for significantly larger putamen and amygdala. Cerebral blood flow was also comparable between the groups, except for the putamen, which demonstrated decreased blood flow in children with epilepsy. Diffusion (apparent diffusion coefficient) showed a trend towards higher values in children with epilepsy, with significantly elevated diffusion within the thalamus in children with epilepsy compared with neurologically normal children. CONCLUSION: Children with epilepsy show statistically significant differences in volume, diffusion, and cerebral blood flow within their thalamus, putamen, and amygdala, suggesting that epilepsy is associated with structural changes of the central nervous system influencing brain development and potentially leading to poorer neurocognitive outcomes.

Utilization of Novel High-Resolution, MRI-Based Vascular Imaging Modality for Preoperative Stereoelectroencephalography Planning in Children: A Technical Note.

INTRODUCTION: Stereoelectroencephalography (SEEG) is a powerful intracranial diagnostic tool that requires accurate imaging for proper electrode trajectory planning to ensure efficacy and maximize patient safety. Computed tomography (CT) angiography and digital subtraction angiography are commonly used, but recent developments in magnetic resonance angiography allow for high-resolution vascular visualization without added risks of radiation. We report on the accuracy of electrode placement under robotic assistance planning utilizing a novel high-resolution magnetic resonance imaging (MRI)-based imaging modality. METHODS: Sixteen pediatric patients between February 2014 and October 2017 underwent SEEG exploration for epileptogenic zone localization. A gadolinium-enhanced 3D T1-weighted spoiled gradient recalled echo sequence with minimum echo time and repetition time was applied for background parenchymal suppression and vascular enhancement. Electrode placement accuracy was determined by analyzing postoperative CT scans laid over preoperative virtual electrode trajectory paths. Entry point, target point, and closest vessel intersection were measured. RESULTS: For any intersection along the trajectory path, 57 intersected vessels were measured. The mean diameter of an intersected vessel was 1.0343 ± 0.1721 mm, and 21.05% of intersections involved superficial vessels. There were 157 overall intersection + near-miss events. The mean diameter for an involved vessel was 1.0236 ± 0.0928 mm, and superficial vessels were involved in 20.13%. Looking only at final electrode target, 3 intersection events were observed. The mean diameter of an intersected vessel was 1.0125 ± 0.2227 mm. For intersection + near-miss events, 24 were measured. An involved vessel's mean diameter was 1.1028 ± 0.2634 mm. For non-entry point intersections, 45 intersected vessels were measured. The mean diameter for intersected vessels was 0.9526 ± 0.0689 mm. For non-entry point intersections + near misses, 126 events were observed. The mean diameter for involved vessels was 0.9826 ± 0.1008 mm. CONCLUSION: We believe this novel sequence allows better identification of superficial and deeper subcortical vessels compared to conventional T1-weighted gadolinium-enhanced MRI.

Learning-based single-step quantitative susceptibility mapping reconstruction without brain extraction.

Quantitative susceptibility mapping (QSM) estimates the underlying tissue magnetic susceptibility from MRI gradient-echo phase signal and typically requires several processing steps. These steps involve phase unwrapping, brain volume extraction, background phase removal and solving an ill-posed inverse problem relating the tissue phase to the underlying susceptibility distribution. The resulting susceptibility map is known to suffer from inaccuracy near the edges of the brain tissues, in part due to imperfect brain extraction, edge erosion of the brain tissue and the lack of phase measurement outside the brain. This inaccuracy has thus hindered the application of QSM for measuring susceptibility of tissues near the brain edges, e.g., quantifying cortical layers and generating superficial venography. To address these challenges, we propose a learning-based QSM reconstruction method that directly estimates the magnetic susceptibility from total phase images without the need for brain extraction and background phase removal, referred to as autoQSM. The neural network has a modified U-net structure and is trained using QSM maps computed by a two-step QSM method. 209 healthy subjects with ages ranging from 11 to 82 years were employed for patch-wise network training. The network was validated on data dissimilar to the training data, e.g., in vivo mouse brain data and brains with lesions, which suggests that the network generalized and learned the underlying mathematical relationship between magnetic field perturbation and magnetic susceptibility. Quantitative and qualitative comparisons were performed between autoQSM and other two-step QSM methods. AutoQSM was able to recover magnetic susceptibility of anatomical structures near the edges of the brain including the veins covering the cortical surface, spinal cord and nerve tracts near the mouse brain boundaries. The advantages of high-quality maps, no need for brain volume extraction, and high reconstruction speed demonstrate autoQSM's potential for future applications.