Spatiotemporal profile of atrophy in the first year following moderate-severe traumatic brain injury.

Traumatic brain injury (TBI) triggers progressive neurodegeneration resulting in brain atrophy that continues months-to-years following injury. However, a comprehensive characterization of the spatial and temporal evolution of TBI-related brain atrophy remains incomplete. Utilizing a sensitive and unbiased morphometry analysis pipeline optimized for detecting longitudinal changes, we analyzed a sample consisting of 37 individuals with moderate-severe TBI who had primarily high-velocity and high-impact injury mechanisms. They were scanned up to three times during the first year after injury (3 months, 6 months, and 12 months post-injury) and compared with 33 demographically matched controls who were scanned once. Individuals with TBI already showed cortical thinning in frontal and temporal regions and reduced volume in the bilateral thalami at 3 months post-injury. Longitudinally, only a subset of cortical regions in the parietal and occipital lobes showed continued atrophy from 3 to 12 months post-injury. Additionally, cortical white matter volume and nearly all deep gray matter structures exhibited progressive atrophy over this period. Finally, we found that disproportionate atrophy of cortex along sulci relative to gyri, an emerging morphometric marker of chronic TBI, was present as early as 3 month post-injury. In parallel, neurocognitive functioning largely recovered during this period despite this pervasive atrophy. Our findings demonstrate msTBI results in characteristic progressive neurodegeneration patterns that are divergent across regions and scale with the severity of injury. Future clinical research using atrophy during the first year of TBI as a biomarker of neurodegeneration should consider the spatiotemporal profile of atrophy described in this study.

Association of brain microbleeds with risk factors, cognition, and MRI markers in MESA.

INTRODUCTION: Little is known about the epidemiology of brain microbleeds in racially/ethnically diverse populations. METHODS: In the Multi-Ethnic Study of Atherosclerosis, brain microbleeds were identified from 3T magnetic resonance imaging susceptibility-weighted imaging sequences using deep learning models followed by radiologist review. RESULTS: Among 1016 participants without prior stroke (25% Black, 15% Chinese, 19% Hispanic, 41% White, mean age 72), microbleed prevalence was 20% at age 60 to 64.9 and 45% at ≥85 years. Deep microbleeds were associated with older age, hypertension, higher body mass index, and atrial fibrillation, and lobar microbleeds with male sex and atrial fibrillation. Overall, microbleeds were associated with greater white matter hyperintensity volume and lower total white matter fractional anisotropy. DISCUSSION: Results suggest differing associations for lobar versus deep locations. Sensitive microbleed quantification will facilitate future longitudinal studies of their potential role as an early indicator of vascular pathology.

MR susceptibility imaging for detection of tumor-associated macrophages in glioblastoma.

PURPOSE: Tumor-associated macrophages (TAMs) are a key component of glioblastoma (GBM) microenvironment. Considering the differential role of different TAM phenotypes in iron metabolism with the M1 phenotype storing intracellular iron, and M2 phenotype releasing iron in the tumor microenvironment, we investigated MRI to quantify iron as an imaging biomarker for TAMs in GBM patients. METHODS: 21 adult patients with GBM underwent a 3D single echo gradient echo MRI sequence and quantitative susceptibility maps were generated. In 3 subjects, ex vivo imaging of surgical specimens was performed on a 9.4 Tesla MRI using 3D multi-echo GRE scans, and R2* (1/T2*) maps were generated. Each specimen was stained with hematoxylin and eosin, as well as CD68, CD86, CD206, and L-Ferritin. RESULTS: Significant positive correlation was observed between mean susceptibility for the tumor enhancing zone and the L-ferritin positivity percent (r = 0.56, p = 0.018) and the combination of tumor's enhancing zone and necrotic core and the L-Ferritin positivity percent (r = 0.72; p = 0.001). The mean susceptibility significantly correlated with positivity percent for CD68 (ρ = 0.52, p = 0.034) and CD86 (r = 0.7 p = 0.001), but not for CD206 (ρ = 0.09; p = 0.7). There was a positive correlation between mean R2* values and CD68 positive cell counts (r = 0.6, p = 0.016). Similarly, mean R2* values significantly correlated with CD86 (r = 0.54, p = 0.03) but not with CD206 (r = 0.15, p = 0.5). CONCLUSIONS: This study demonstrated the potential of MR quantitative susceptibility mapping as a non-invasive method for in vivo TAM quantification and phenotyping. Validation of these findings with large multicenter studies is needed.

Brain MRI Deep Learning and Bayesian Inference System Augments Radiology Resident Performance.

Automated quantitative and probabilistic medical image analysis has the potential to improve the accuracy and efficiency of the radiology workflow. We sought to determine whether AI systems for brain MRI diagnosis could be used as a clinical decision support tool to augment radiologist performance. We utilized previously developed AI systems that combine convolutional neural networks and expert-derived Bayesian networks to distinguish among 50 diagnostic entities on multimodal brain MRIs. We tested whether these systems could augment radiologist performance through an interactive clinical decision support tool known as Adaptive Radiology Interpretation and Education System (ARIES) in 194 test cases. Four radiology residents and three academic neuroradiologists viewed half of the cases unassisted and half with the results of the AI system displayed on ARIES. Diagnostic accuracy of radiologists for top diagnosis (TDx) and top three differential diagnosis (T3DDx) was compared with and without ARIES. Radiology resident performance was significantly better with ARIES for both TDx (55% vs 30%; P < .001) and T3DDx (79% vs 52%; P = 0.002), with the largest improvement for rare diseases (39% increase for T3DDx; P < 0.001). There was no significant difference between attending performance with and without ARIES for TDx (72% vs 69%; P = 0.48) or T3DDx (86% vs 89%; P = 0.39). These findings suggest that a hybrid deep learning and Bayesian inference clinical decision support system has the potential to augment diagnostic accuracy of non-specialists to approach the level of subspecialists for a large array of diseases on brain MRI.

Data-Driven Quantitative Susceptibility Mapping Using Loss Adaptive Dipole Inversion (LADI).

BACKGROUND: Quantitative susceptibility mapping (QSM) uses prior information to reconstruct maps, but prior information may not show pathology and introduce inconsistencies with susceptibility maps, degrade image quality and inadvertently smoothing image features. PURPOSE: To develop a local field data-driven QSM reconstruction that does not depend on spatial edge prior information. STUDY TYPE: Retrospective. SUBJECTS, ANIMAL MODELS: A dataset from 2016 ISMRM QSM Challenge, 11 patients with glioblastoma, a patient with microbleeds and porcine heart. SEQUENCE/FIELD STRENGTH: 3D gradient echo sequence on 3T and 7T scanners. ASSESSMENT: Accuracy was compared to Calculation of Susceptibility through Multiple Orientation Sampling (COSMOS), and several published techniques using region of interest (ROI) measurements, root-mean-squared error (RMSE), structural similarity index metric (SSIM), and high-frequency error norm (HFEN). Numerical ranking and semiquantitative image grading was performed by three expert observers to assess overall image quality (IQ) and image sharpness (IS). STATISTICAL TESTS: Bland-Altman, Friedman test, and Conover multiple comparisons. RESULTS: Loss adaptive dipole inversion (LADI) (ß = 0.82, R2 = 0.96), morphology-enabled dipole inversion (MEDI) (ß = 0.91, R2 = 0.97), and fast nonlinear susceptibility inversion (FANSI) (ß = 0.81, R2 = 0.98) had excellent correlation with COSMOS and no bias was detected (bias = 0.006 ± 0.014, P < 0.05). In glioblastoma patients, LADI showed consistently better performance (IQGrade = 2.6 ± 0.4, ISGrade = 2.6 ± 0.3, IQRank = 3.5 ± 0.4, ISRank = 3.9 ± 0.2) compared with MEDI (IQGrade = 2.1 ± 0.3, ISGrade = 2 ± 0.5, IQRank = 2.4 ± 0.5, ISRank = 2.8 ± 0.2) and FANSI (IQGrade = 2.2 ± 0.5, ISGrade = 2 ± 0.4, IQRank = 2.8 ± 0.3, ISRank = 2.1 ± 0.2). Dark artifact visible near the infarcted region in MEDI (InfMEDI = -0.27 ± 0.06 ppm) was better mitigated by FANSI (InfFANSI-TGV = -0.17 ± 0.05 ppm) and LADI (InfLADI = -0.18 ± 0.05 ppm). CONCLUSION: For neuroimaging applications, LADI preserved image sharpness and fine features in glioblastoma and microbleed patients. LADI performed better at mitigating artifacts in cardiac QSM. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY STAGE: 1 J. Magn. Reson. Imaging 2020;52:823-835.

Regional Cerebral Blood Flow in Children and Young Adults with Chronic Kidney Disease.

Purpose To investigate the pathophysiologic effects of chronic kidney disease (CKD) on brain function in children with CKD by correlating cerebral blood flow (CBF) with clinical and behavioral indexes. Materials and Methods In this prospective study, 73 pediatric patients with CKD (mean age, 15.80 years ± 3.63; range, 9-25 years) and 57 control subjects (mean age, 15.65 years ± 3.76; range, 9-25 years) were recruited. CBF measurements were acquired with an MRI arterial spin labeling scheme. Neurocognitive measurements were performed with traditional and computerized neurocognitive batteries. Clinical data were also collected. Group-level global and regional CBF differences between patients with CKD and control subjects were assessed. Regression analyses were conducted to evaluate the associations among regional CBF, clinical variables, and cognitive performance. Results Patients with CKD showed higher global CBF compared with control subjects that was attributable to reduced hematocrit level (mean, 60.2 mL/100 g/min ± 9.0 vs 56.5 mL/100 g/min ± 8.0, respectively). White matter CBF showed correlation with blood pressure (r = 0.244, P = .039), a finding suggestive of altered cerebrovascular autoregulation. Regional CBF differences between patients and control subjects included regions in the "default mode" network. In patients with CKD, positive extrema in the precuneus showed a strong correlation with executive function (ρ = 0.608, P = .001). Conclusion Systemic effects of estimated glomerular filtration rate, hematocrit level, and blood pressure on CBF and alterations in regional CBF may reflect impaired brain function underlying neurocognitive symptoms in CKD. These findings further characterize the nature of alterations in brain physiologic features in children, adolescents, and young adults with CKD.

Combat-related Mild Traumatic Brain Injury: Association between Baseline Diffusion-Tensor Imaging Findings and Long-term Outcomes.

Purpose To determine whether functional outcomes of veterans who sustained combat-related mild traumatic brain injury (TBI) are associated with scalar metrics derived from diffusion-tensor (DT) imaging at their initial postdeployment evaluation. Materials and Methods This HIPAA-compliant retrospective study was approved by the institutional review board, and the requirement to obtain informed consent was waived. From 2010 to 2013, initial postdeployment evaluation, including clinical assessment and brain magnetic resonance (MR) examination with DT imaging, was performed in combat veterans who sustained mild TBI while deployed. Outcomes from chart review encompassed initial postdeployment clinical assessment as well as later functional status, including evaluation of occupational status and health care utilization. Scalar diffusion metrics from the initial postdeployment evaluation were compared with outcomes by using multivariate analysis. Veterans who did and did not return to work were also compared for differences in clinical variables by using t and χ(2) tests. Results Postdeployment evaluation was performed a mean of 3.8 years after injury (range, 0.5-9 years; standard deviation, 2.5 years). After a mean follow-up of 1.4 years (range, 0.5-2.5 years; standard deviation, 0.8 year), 34 of 57 veterans (60%) had returned to work. Return to work was associated with diffusion metrics in multiple regions of white matter, particularly in the left internal capsule and the left frontal lobe (P = .02-.05). Overall, veterans had a mean of 46 health care visits per year during the follow-up period (range, 3-196 visits per year; standard deviation, 41 visits per year). Cumulative health care visits over time were inversely correlated with diffusion anisotropy of the splenium of the corpus callosum and adjacent parietal white matter (P < .05). Clinical measures obtained during initial postdeployment evaluation were not predictive of later functional status (P = .12-.8). Conclusion Differences in white matter microstructure may partially account for the variance in functional outcomes among veterans who sustained combat-related mild TBI. (©) RSNA, 2016.

68 Ga-DOTATATE PET to Characterize Lesions in the Neuroaxis.

AIM: The differentiation of paragangliomas, schwannomas, meningiomas, and other neuroaxis tumors in the head and neck remains difficult when conventional MRI is inconclusive. This study assesses the utility of 68 Ga-DOTATATE PET/CT as an adjunct to hone the diagnosis. PATIENTS AND METHODS: This retrospective study considered 70 neuroaxis lesions in 52 patients with 68 Ga-DOTATATE PET/CT examinations; 22 lesions (31%) had pathologic confirmation. Lesions were grouped based on pathological diagnosis and best radiologic diagnosis when pathology was not available. Wilcoxon rank sum tests were used to test for differences in SUV max among paragangliomas, schwannomas, and meningiomas. Receiver operator characteristic curves were constructed. RESULTS: Paragangliomas had a significantly greater 68 Ga-DOTATATE uptake (median SUV max , 62; interquartile range [IQR], 89) than nonparagangliomas. Schwannomas had near-zero 68 Ga-DOTATATE uptake (median SUV max , 2; IQR, 1). Intermediate 68 Ga-DOTATATE uptake was seen for meningiomas (median SUV max , 19; IQR, 6) and other neuroaxis lesions (median SUV max , 7; IQR, 9). Receiver operator characteristic analysis demonstrated an area under the curve of 0.87 for paragangliomas versus all other lesions and 0.97 for schwannomas versus all other lesions. CONCLUSIONS: Marked 68 Ga-DOTATATE uptake (>50 SUV max ) favors a diagnosis of paraganglioma, although paragangliomas exhibit a wide variability of uptake. Low to moderate level 68 Ga-DOTATATE uptake is nonspecific and may represent diverse pathophysiology including paraganglioma, meningioma, and other neuroaxis tumors but essentially excludes schwannomas, which exhibited virtually no uptake.

Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain Tumors.

BACKGROUND AND PURPOSE: Tumor segmentation is essential in surgical and treatment planning and response assessment and monitoring in pediatric brain tumors, the leading cause of cancer-related death among children. However, manual segmentation is time-consuming and has high interoperator variability, underscoring the need for more efficient methods. After training, we compared 2 deep-learning-based 3D segmentation models, DeepMedic and nnU-Net, with pediatric-specific multi-institutional brain tumor data based on multiparametric MR images. MATERIALS AND METHODS: Multiparametric preoperative MR imaging scans of 339 pediatric patients (n = 293 internal and n = 46 external cohorts) with a variety of tumor subtypes were preprocessed and manually segmented into 4 tumor subregions, ie, enhancing tumor, nonenhancing tumor, cystic components, and peritumoral edema. After training, performances of the 2 models on internal and external test sets were evaluated with reference to ground truth manual segmentations. Additionally, concordance was assessed by comparing the volume of the subregions as a percentage of the whole tumor between model predictions and ground truth segmentations using the Pearson or Spearman correlation coefficients and the Bland-Altman method. RESULTS: The mean Dice score for nnU-Net internal test set was 0.9 (SD, 0.07) (median, 0.94) for whole tumor; 0.77 (SD, 0.29) for enhancing tumor; 0.66 (SD, 0.32) for nonenhancing tumor; 0.71 (SD, 0.33) for cystic components, and 0.71 (SD, 0.40) for peritumoral edema, respectively. For DeepMedic, the mean Dice scores were 0.82 (SD, 0.16) for whole tumor; 0.66 (SD, 0.32) for enhancing tumor; 0.48 (SD, 0.27) for nonenhancing tumor; 0.48 (SD, 0.36) for cystic components, and 0.19 (SD, 0.33) for peritumoral edema, respectively. Dice scores were significantly higher for nnU-Net (P ≤ .01). Correlation coefficients for tumor subregion percentage volumes were higher (0.98 versus 0.91 for enhancing tumor, 0.97 versus 0.75 for nonenhancing tumor, 0.98 versus 0.80 for cystic components, 0.95 versus 0.33 for peritumoral edema in the internal test set). Bland-Altman plots were better for nnU-Net compared with DeepMedic. External validation of the trained nnU-Net model on the multi-institutional Brain Tumor Segmentation Challenge in Pediatrics (BraTS-PEDs) 2023 data set revealed high generalization capability in the segmentation of whole tumor, tumor core (a combination of enhancing tumor, nonenhancing tumor, and cystic components), and enhancing tumor with mean Dice scores of 0.87 (SD, 0.13) (median, 0.91), 0.83 (SD, 0.18) (median, 0.89), and 0.48 (SD, 0.38) (median, 0.58), respectively. CONCLUSIONS: The pediatric-specific data-trained nnU-Net model is superior to DeepMedic for whole tumor and subregion segmentation of pediatric brain tumors.

Towards Consistency in Pediatric Brain Tumor Measurements: Challenges, Solutions, and the Role of AI-Based Segmentation.

MR imaging is central to the assessment of tumor burden and changes over time in neuro-oncology. Several response assessment guidelines have been set forth by the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working groups in different tumor histologies; however, the visual delineation of tumor components using MRIs is not always straightforward, and complexities not currently addressed by these criteria can introduce inter- and intra-observer variability in manual assessments. Differentiation of non-enhancing tumor from peritumoral edema, mild enhancement from absence of enhancement, and various cystic components can be challenging; particularly given a lack of sufficient and uniform imaging protocols in clinical practice. Automated tumor segmentation with artificial intelligence (AI) may be able to provide more objective delineations, but rely on accurate and consistent training data created manually (ground truth). Herein, this paper reviews existing challenges and potential solutions to identifying and defining subregions of pediatric brain tumors (PBTs) that are not explicitly addressed by current guidelines. The goal is to assert the importance of defining and adopting criteria for addressing these challenges, as it will be critical to achieving standardized tumor measurements and reproducible response assessment in PBTs, ultimately leading to more precise outcome metrics and accurate comparisons among clinical studies.

Imaging Approach to Concussion.

The acute and long-term neurobiological sequelae of concussion (mild traumatic brain injury [mTBI]) and sub-concussive head trauma have become increasingly apparent in recent decades in part due to neuroimaging research. Although imaging has an established role in the clinical management of mTBI for the identification of intracranial lesions warranting urgent interventions, MR imaging is increasingly employed for the detection of post-traumatic sequelae which carry important prognostic significance. As neuroimaging research continues to elucidate the pathophysiology of TBI underlying prolonged recovery and the development of persistent post-concussive symptoms, there is a strong motivation to translate these techniques into clinical use for improved diagnosis and therapeutic monitoring.

Temporal dynamics of cerebral blood flow during the first year after moderate-severe traumatic brain injury: A longitudinal perfusion MRI study.

Traumatic brain injury (TBI) is associated with alterations in cerebral blood flow (CBF), which may underlie functional disability and precipitate TBI-induced neurodegeneration. Although it is known that chronic moderate-severe TBI (msTBI) causes decreases in CBF, the temporal dynamics during the early chronic phase of TBI remain unknown. Using arterial spin labeled (ASL) perfusion magnetic resonance imaging (MRI), we examined longitudinal CBF changes in 29 patients with msTBI at 3, 6, and 12 months post-injury in comparison to 35 demographically-matched healthy controls (HC). We investigated the difference between the two groups and the within-subject time effect in the TBI patients using whole-brain voxel-wise analysis. Mean CBF in gray matter (GM) was lower in the TBI group compared to HC at 6 and 12 months post-injury. Within the TBI group, we identified widespread regional decreases in CBF from 3 to 6 months post-injury. In contrast, there were no regions with decreasing CBF from 6 to 12 months post-injury, indicating stabilization of hypoperfusion. There was instead a small area of increase in CBF observed in the right precuneus. These CBF changes were not accompanied by cortical atrophy. The change in CBF was correlated with change in executive function from 3 to 6 months post-injury in TBI patients, suggesting functional relevance of CBF measures. Understanding the time course of TBI-induced hypoperfusion and its relationship with cognitive improvement could provide an optimal treatment window to benefit long-term outcome.

Deep Learning Based Detection of Enlarged Perivascular Spaces on Brain MRI.

Deep learning has been demonstrated effective in many neuroimaging applications. However, in many scenarios, the number of imaging sequences capturing information related to small vessel disease lesions is insufficient to support data-driven techniques. Additionally, cohort-based studies may not always have the optimal or essential imaging sequences for accurate lesion detection. Therefore, it is necessary to determine which imaging sequences are crucial for precise detection. This study introduces a deep learning framework to detect enlarged perivascular spaces (ePVS) and aims to find the optimal combination of MRI sequences for deep learning-based quantification. We implemented an effective lightweight U-Net adapted for ePVS detection and comprehensively investigated different combinations of information from SWI, FLAIR, T1-weighted (T1w), and T2-weighted (T2w) MRI sequences. The experimental results showed that T2w MRI is the most important for accurate ePVS detection, and the incorporation of SWI, FLAIR and T1w MRI in the deep neural network had minor improvements in accuracy and resulted in the highest sensitivity and precision (sensitivity =0.82, precision =0.83). The proposed method achieved comparable accuracy at a minimal time cost compared to manual reading. The proposed automated pipeline enables robust and time-efficient readings of ePVS from MR scans and demonstrates the importance of T2w MRI for ePVS detection and the potential benefits of using multimodal images. Furthermore, the model provides whole-brain maps of ePVS, enabling a better understanding of their clinical correlates compared to the clinical rating methods within only a couple of brain regions.

Automated Tumor Segmentation and Brain Tissue Extraction from Multiparametric MRI of Pediatric Brain Tumors: A Multi-Institutional Study.

Background: Brain tumors are the most common solid tumors and the leading cause of cancer-related death among all childhood cancers. Tumor segmentation is essential in surgical and treatment planning, and response assessment and monitoring. However, manual segmentation is time-consuming and has high interoperator variability. We present a multi-institutional deep learning-based method for automated brain extraction and segmentation of pediatric brain tumors based on multi-parametric MRI scans. Methods: Multi-parametric scans (T1w, T1w-CE, T2, and T2-FLAIR) of 244 pediatric patients (n=215 internal and n=29 external cohorts) with de novo brain tumors, including a variety of tumor subtypes, were preprocessed and manually segmented to identify the brain tissue and tumor subregions into four tumor subregions, i.e., enhancing tumor (ET), non-enhancing tumor (NET), cystic components (CC), and peritumoral edema (ED). The internal cohort was split into training (n=151), validation (n=43), and withheld internal test (n=21) subsets. DeepMedic, a three-dimensional convolutional neural network, was trained and the model parameters were tuned. Finally, the network was evaluated on the withheld internal and external test cohorts. Results: Dice similarity score (median±SD) was 0.91±0.10/0.88±0.16 for the whole tumor, 0.73±0.27/0.84±0.29 for ET, 0.79±19/0.74±0.27 for union of all non-enhancing components (i.e., NET, CC, ED), and 0.98±0.02 for brain tissue in both internal/external test sets. Conclusions: Our proposed automated brain extraction and tumor subregion segmentation models demonstrated accurate performance on segmentation of the brain tissue and whole tumor regions in pediatric brain tumors and can facilitate detection of abnormal regions for further clinical measurements. Key Points: We proposed automated tumor segmentation and brain extraction on pediatric MRI.The volumetric measurements using our models agree with ground truth segmentations. Importance of the Study: The current response assessment in pediatric brain tumors (PBTs) is currently based on bidirectional or 2D measurements, which underestimate the size of non-spherical and complex PBTs in children compared to volumetric or 3D methods. There is a need for development of automated methods to reduce manual burden and intra- and inter-rater variability to segment tumor subregions and assess volumetric changes. Most currently available automated segmentation tools are developed on adult brain tumors, and therefore, do not generalize well to PBTs that have different radiological appearances. To address this, we propose a deep learning (DL) auto-segmentation method that shows promising results in PBTs, collected from a publicly available large-scale imaging dataset (Children's Brain Tumor Network; CBTN) that comprises multi-parametric MRI scans of multiple PBT types acquired across multiple institutions on different scanners and protocols. As a complementary to tumor segmentation, we propose an automated DL model for brain tissue extraction.

Distinguishing Progression from Pseudoprogression in Glioblastoma Using 18F-Fluciclovine PET.

Accurate differentiation between tumor progression (TP) and pseudoprogression remains a critical unmet need in neurooncology. 18F-fluciclovine is a widely available synthetic amino acid PET radiotracer. In this study, we aimed to assess the value of 18F-fluciclovine PET for differentiating pseudoprogression from TP in a prospective cohort of patients with suspected radiographic recurrence of glioblastoma. Methods: We enrolled 30 glioblastoma patients with radiographic progression after first-line chemoradiotherapy for whom surgical resection was planned. The patients underwent preoperative 18F-fluciclovine PET and MRI. The relative percentages of viable tumor and therapy-related changes observed in histopathology were quantified and categorized as TP (≥50% viable tumor), mixed TP (<50% and >10% viable tumor), or pseudoprogression (≤10% viable tumor). Results: Eighteen patients had TP, 4 had mixed TP, and 8 had pseudoprogression. Patients with TP/mixed TP had a significantly higher 40- to 50-min SUVmax (6.64 + 1.88 vs. 4.11 ± 1.52, P = 0.009) than patients with pseudoprogression. A 40- to 50-min SUVmax cutoff of 4.66 provided 90% sensitivity and 83% specificity for differentiation of TP/mixed TP from pseudoprogression (area under the curve [AUC], 0.86). A maximum relative cerebral blood volume cutoff of 3.672 provided 90% sensitivity and 71% specificity for differentiation of TP/mixed TP from pseudoprogression (AUC, 0.779). Combining a 40- to 50-min SUVmax cutoff of 4.66 and a maximum relative cerebral blood volume of 3.67 on MRI provided 100% sensitivity and 80% specificity for differentiating TP/mixed TP from pseudoprogression (AUC, 0.95). Conclusion: 18F-fluciclovine PET uptake can accurately differentiate pseudoprogression from TP in glioblastoma, with even greater accuracy when combined with multiparametric MRI. Given the wide availability of 18F-fluciclovine, larger, multicenter studies are warranted to determine whether amino acid PET with 18F-fluciclovine should be used in the routine posttreatment assessment of glioblastoma.

Automated tumor segmentation and brain tissue extraction from multiparametric MRI of pediatric brain tumors: A multi-institutional study.

Background: Brain tumors are the most common solid tumors and the leading cause of cancer-related death among all childhood cancers. Tumor segmentation is essential in surgical and treatment planning, and response assessment and monitoring. However, manual segmentation is time-consuming and has high interoperator variability. We present a multi-institutional deep learning-based method for automated brain extraction and segmentation of pediatric brain tumors based on multi-parametric MRI scans. Methods: Multi-parametric scans (T1w, T1w-CE, T2, and T2-FLAIR) of 244 pediatric patients ( n = 215 internal and n = 29 external cohorts) with de novo brain tumors, including a variety of tumor subtypes, were preprocessed and manually segmented to identify the brain tissue and tumor subregions into four tumor subregions, i.e., enhancing tumor (ET), non-enhancing tumor (NET), cystic components (CC), and peritumoral edema (ED). The internal cohort was split into training ( n = 151), validation ( n = 43), and withheld internal test ( n = 21) subsets. DeepMedic, a three-dimensional convolutional neural network, was trained and the model parameters were tuned. Finally, the network was evaluated on the withheld internal and external test cohorts. Results: Dice similarity score (median ± SD) was 0.91 ± 0.10/0.88 ± 0.16 for the whole tumor, 0.73 ± 0.27/0.84 ± 0.29 for ET, 0.79 ± 19/0.74 ± 0.27 for union of all non-enhancing components (i.e., NET, CC, ED), and 0.98 ± 0.02 for brain tissue in both internal/external test sets. Conclusions: Our proposed automated brain extraction and tumor subregion segmentation models demonstrated accurate performance on segmentation of the brain tissue and whole tumor regions in pediatric brain tumors and can facilitate detection of abnormal regions for further clinical measurements.

Assessment of Risk Factors and Clinical Importance of Enlarged Perivascular Spaces by Whole-Brain Investigation in the Multi-Ethnic Study of Atherosclerosis.

Importance: Enlarged perivascular spaces (ePVSs) have been associated with cerebral small-vessel disease (cSVD). Although their etiology may differ based on brain location, study of ePVSs has been limited to specific brain regions; therefore, their risk factors and significance remain uncertain. Objective: Toperform a whole-brain investigation of ePVSs in a large community-based cohort. Design, Setting, and Participants: This cross-sectional study analyzed data from the Atrial Fibrillation substudy of the population-based Multi-Ethnic Study of Atherosclerosis. Demographic, vascular risk, and cardiovascular disease data were collected from September 2016 to May 2018. Brain magnetic resonance imaging was performed from March 2018 to July 2019. The reported analysis was conducted between August and October 2022. A total of 1026 participants with available brain magnetic resonance imaging data and complete information on demographic characteristics and vascular risk factors were included. Main Outcomes and Measures: Enlarged perivascular spaces were quantified using a fully automated deep learning algorithm. Quantified ePVS volumes were grouped into 6 anatomic locations: basal ganglia, thalamus, brainstem, frontoparietal, insular, and temporal regions, and were normalized for the respective regional volumes. The association of normalized regional ePVS volumes with demographic characteristics, vascular risk factors, neuroimaging indices, and prevalent cardiovascular disease was explored using generalized linear models. Results: In the 1026 participants, mean (SD) age was 72 (8) years; 541 (53%) of the participants were women. Basal ganglia ePVS volume was positively associated with age (ß = 3.59 × 10-3; 95% CI, 2.80 × 10-3 to 4.39 × 10-3), systolic blood pressure (ß = 8.35 × 10-4; 95% CI, 5.19 × 10-4 to 1.15 × 10-3), use of antihypertensives (ß = 3.29 × 10-2; 95% CI, 1.92 × 10-2 to 4.67 × 10-2), and negatively associated with Black race (ß = -3.34 × 10-2; 95% CI, -5.08 × 10-2 to -1.59 × 10-2). Thalamic ePVS volume was positively associated with age (ß = 5.57 × 10-4; 95% CI, 2.19 × 10-4 to 8.95 × 10-4) and use of antihypertensives (ß = 1.19 × 10-2; 95% CI, 6.02 × 10-3 to 1.77 × 10-2). Insular region ePVS volume was positively associated with age (ß = 1.18 × 10-3; 95% CI, 7.98 × 10-4 to 1.55 × 10-3). Brainstem ePVS volume was smaller in Black than in White participants (ß = -5.34 × 10-3; 95% CI, -8.26 × 10-3 to -2.41 × 10-3). Frontoparietal ePVS volume was positively associated with systolic blood pressure (ß = 1.14 × 10-4; 95% CI, 3.38 × 10-5 to 1.95 × 10-4) and negatively associated with age (ß = -3.38 × 10-4; 95% CI, -5.40 × 10-4 to -1.36 × 10-4). Temporal region ePVS volume was negatively associated with age (ß = -1.61 × 10-2; 95% CI, -2.14 × 10-2 to -1.09 × 10-2), as well as Chinese American (ß = -2.35 × 10-1; 95% CI, -3.83 × 10-1 to -8.74 × 10-2) and Hispanic ethnicities (ß = -1.73 × 10-1; 95% CI, -2.96 × 10-1 to -4.99 × 10-2). Conclusions and Relevance: In this cross-sectional study of ePVSs in the whole brain, increased ePVS burden in the basal ganglia and thalamus was a surrogate marker for underlying cSVD, highlighting the clinical importance of ePVSs in these locations.

A multi-institutional pediatric dataset of clinical radiology MRIs by the Children's Brain Tumor Network.

Pediatric brain and spinal cancers remain the leading cause of cancer-related death in children. Advancements in clinical decision-support in pediatric neuro-oncology utilizing the wealth of radiology imaging data collected through standard care, however, has significantly lagged other domains. Such data is ripe for use with predictive analytics such as artificial intelligence (AI) methods, which require large datasets. To address this unmet need, we provide a multi-institutional, large-scale pediatric dataset of 23,101 multi-parametric MRI exams acquired through routine care for 1,526 brain tumor patients, as part of the Children's Brain Tumor Network. This includes longitudinal MRIs across various cancer diagnoses, with associated patient-level clinical information, digital pathology slides, as well as tissue genotype and omics data. To facilitate downstream analysis, treatment-naïve images for 370 subjects were processed and released through the NCI Childhood Cancer Data Initiative via the Cancer Data Service. Through ongoing efforts to continuously build these imaging repositories, our aim is to accelerate discovery and translational AI models with real-world data, to ultimately empower precision medicine for children.