The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20\%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a landmark community benchmark event with a successful history of 12 years of resource creation for the segmentation and analysis of adult glioma. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which represents the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on benchmarking the development of volumentric segmentation algorithms for pediatric brain glioma through standardized quantitative performance evaluation metrics utilized across the BraTS 2023 cluster of challenges. Models gaining knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training data will be evaluated on separate validation and unseen test mpMRI dataof high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors.

The University of California San Francisco Brain Metastases Stereotactic Radiosurgery (UCSF-BMSR) MRI Dataset

Supplemental material is available for this article.

Automated neonatal nnU-Net brain MRI extractor trained on a large multi-institutional dataset

Brain extraction, or skull-stripping, is an essential data preprocessing step for machine learning approaches to brain MRI analysis. Currently, there are limited extraction algorithms for the neonatal brain. We aim to adapt an established deep learning algorithm for the automatic segmentation of neonatal brains from MRI, trained on a large multi-institutional dataset for improved generalizability across image acquisition parameters. Our model, ANUBEX (automated neonatal nnU-Net brain MRI extractor), was designed using nnU-Net and was trained on a subset of participants (N = 433) enrolled in the High-dose Erythropoietin for Asphyxia and Encephalopathy (HEAL) study. We compared the performance of our model to five publicly available models (BET, BSE, CABINET, iBEATv2, ROBEX) across conventional and machine learning methods, tested on two public datasets (NIH and dHCP). We found that our model had a significantly higher Dice score on the aggregate of both data sets and comparable or significantly higher Dice scores on the NIH (low-resolution) and dHCP (high-resolution) datasets independently. ANUBEX performs similarly when trained on sequence-agnostic or motion-degraded MRI, but slightly worse on preterm brains. In conclusion, we created an automatic deep learning-based neonatal brain extraction algorithm that demonstrates accurate performance with both high- and low-resolution MRIs with fast computation time.

Automated Segmentation of Ablated Lesions Using Deep Convolutional Neural Networks: A Basis for Response Assessment Following Laser Interstitial Thermal Therapy

BACKGROUND

Laser interstitial thermal therapy (LITT) of intracranial tumors or radiation necrosis enables tissue diagnosis, cytoreduction, and rapid return to systemic therapies. Ablated tissue remains in situ, resulting in characteristic post-LITT edema associated with transient clinical worsening and complicating post-LITT response assessment.

METHODS

All patients receiving LITT at a single center for tumors or radiation necrosis from 2015 - 2023 with ≥ 9 months of MRI follow-up were included. An nnU-Net segmentation model was trained to automatically segment Contrast-enhancing Lesion Volume (CeLV) of LITT-treated lesions on T1-weighted images. Response assessment was performed using volumetric measurements.

RESULTS

384 unique MRI exams of 61 LITT-treated lesions and 6 control cases of medically-managed radiation necrosis were analyzed. Automated segmentation was accurate in 367/384 (95.6%) images. CeLV increased to a median of 68.3% (IQR 35.1 - 109.2%) from baseline at 1 - 3 months from LITT (P = 0.0012) and returned to baseline thereafter. Overall survival (OS) for LITT-treated patients was 39.1 (9.2 - 93.4) months. Lesion expansion above 40% from volumetric nadir or baseline was considered volumetric progression. Twenty-one of 56 (37.5%) patients experienced progression for a volumetric progression-free survival of 21.4 (6.0 - 93.4) months. Patients with volumetric progression had worse OS (17.3 vs 62.1 months, P = 0.0015).

CONCLUSIONS

Post-LITT CeLV expansion is quantifiable and resolves within 6 months of LITT. Development of response assessment criteria for LITT-treated lesions is feasible and should be considered for clinical trials. Automated lesion segmentation could speed adoption of volumetric response criteria in clinical practice.

Reducing Gadolinium Contrast With Artificial Intelligence

Gadolinium contrast is an important agent in magnetic resonance imaging (MRI), particularly in neuroimaging where it can help identify blood-brain barrier breakdown from an inflammatory, infectious, or neoplastic process. However, gadolinium contrast has several drawbacks, including nephrogenic systemic fibrosis, gadolinium deposition in the brain and bones, and allergic-like reactions. As computer hardware and technology continues to evolve, machine learning has become a possible solution for eliminating or reducing the dose of gadolinium contrast. This review summarizes the clinical uses of gadolinium contrast, the risks of gadolinium contrast, and state-of-the-art machine learning methods that have been applied to reduce or eliminate gadolinium contrast administration, as well as their current limitations, with a focus on neuroimaging applications. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 1.

Association of partial T2-FLAIR mismatch sign and isocitrate dehydrogenase mutation in WHO grade 4 gliomas: results from the ReSPOND consortium

PURPOSE

While the T2-FLAIR mismatch sign is highly specific for isocitrate dehydrogenase (IDH)-mutant, 1p/19q-noncodeleted astrocytomas among lower-grade gliomas, its utility in WHO grade 4 gliomas is not well-studied. We derived the partial T2-FLAIR mismatch sign as an imaging biomarker for IDH mutation in WHO grade 4 gliomas.

METHODS

Preoperative MRI scans of adult WHO grade 4 glioma patients (n = 2165) from the multi-institutional ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium were analyzed. Diagnostic performance of the partial T2-FLAIR mismatch sign was evaluated. Subset analyses were performed to assess associations of imaging markers with overall survival (OS).

RESULTS

One hundred twenty-one (5.6%) of 2165 grade 4 gliomas were IDH-mutant. Partial T2-FLAIR mismatch was present in 40 (1.8%) cases, 32 of which were IDH-mutant, yielding 26.4% sensitivity, 99.6% specificity, 80.0% positive predictive value, and 95.8% negative predictive value. Multivariate logistic regression demonstrated IDH mutation was significantly associated with partial T2-FLAIR mismatch (odds ratio [OR] 5.715, 95% CI [1.896, 17.221], p = 0.002), younger age (OR 0.911 [0.895, 0.927], p < 0.001), tumor centered in frontal lobe (OR 3.842, [2.361, 6.251], p < 0.001), absence of multicentricity (OR 0.173, [0.049, 0.612], p = 0.007), and presence of cystic (OR 6.596, [3.023, 14.391], p < 0.001) or non-enhancing solid components (OR 6.069, [3.371, 10.928], p < 0.001). Multivariate Cox analysis demonstrated cystic components (p = 0.024) and non-enhancing solid components (p = 0.003) were associated with longer OS, while older age (p < 0.001), frontal lobe center (p = 0.008), multifocality (p < 0.001), and multicentricity (p < 0.001) were associated with shorter OS.

CONCLUSION

Partial T2-FLAIR mismatch sign is highly specific for IDH mutation in WHO grade 4 gliomas.

Correlating Quantitative MRI-based Apparent Diffusion Coefficient Metrics with 24-month Neurodevelopmental Outcomes in Neonates from the HEAL Trial

Background Multiple qualitative scoring systems have been created to capture the imaging severity of hypoxic ischemic brain injury. Purpose To evaluate quantitative volumes of acute brain injury at MRI in neonates with hypoxic ischemic brain injury and correlate these findings with 24-month neurodevelopmental outcomes and qualitative brain injury scoring by radiologists. Materials and Methods In this secondary analysis, brain diffusion-weighted MRI data from neonates in the High-dose Erythropoietin for Asphyxia and Encephalopathy trial, which recruited participants between January 2017 and October 2019, were analyzed. Volume of acute brain injury, defined as brain with apparent diffusion coefficient (ADC) less than 800 × 10-6 mm2/sec, was automatically computed across the whole brain and within the thalami and white matter. Outcomes of death and neurodevelopmental impairment (NDI) were recorded at 24-month follow-up. Associations between the presence and volume (in milliliters) of acute brain injury with 24-month outcomes were evaluated using multiple logistic regression. The correlation between quantitative acute brain injury volume and qualitative MRI scores was assessed using the Kendall tau-b test. Results A total of 416 neonates had available MRI data (mean gestational age, 39.1 weeks ± 1.4 [SD]; 235 male) and 113 (27%) showed evidence of acute brain injury at MRI. Of the 387 participants with 24-month follow-up data, 185 (48%) died or had any NDI. Volume of acute injury greater than 1 mL (odds ratio [OR], 13.9 [95% CI: 5.93, 32.45]; P < .001) and presence of any acute injury in the brain (OR, 4.5 [95% CI: 2.6, 7.8]; P < .001) were associated with increased odds of death or any NDI. Quantitative whole-brain acute injury volume was strongly associated with radiologists' qualitative scoring of diffusion-weighted images (Kendall tau-b = 0.56; P < .001). Conclusion Automated quantitative volume of brain injury is associated with death, moderate to severe NDI, and cerebral palsy in neonates with hypoxic ischemic encephalopathy and correlated well with qualitative MRI scoring of acute brain injury. Clinical trial registration no. NCT02811263 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Huisman in this issue.

The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)

Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions.

The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI

Clinical monitoring of metastatic disease to the brain can be a laborious and timeconsuming process, especially in cases involving multiple metastases when the assessment is performed manually. The Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) guideline, which utilizes the unidimensional longest diameter, is commonly used in clinical and research settings to evaluate response to therapy in patients with brain metastases. However, accurate volumetric assessment of the lesion and surrounding peri-lesional edema holds significant importance in clinical decision-making and can greatly enhance outcome prediction. The unique challenge in performing segmentations of brain metastases lies in their common occurrence as small lesions. Detection and segmentation of lesions that are smaller than 10 mm in size has not demonstrated high accuracy in prior publications. The brain metastases challenge sets itself apart from previously conducted MICCAI challenges on glioma segmentation due to the significant variability in lesion size. Unlike gliomas, which tend to be larger on presentation scans, brain metastases exhibit a wide range of sizes and tend to include small lesions. We hope that the BraTS-METS dataset and challenge will advance the field of automated brain metastasis detection and segmentation.

A Deep Learning Approach for Automated Bone Removal from Computed Tomography Angiography of the Brain

Advanced visualization techniques such as maximum intensity projection (MIP) and volume rendering (VR) are useful for evaluating neurovascular anatomy on CT angiography (CTA) of the brain; however, interference from surrounding osseous anatomy is common. Existing methods for removing bone from CTA images are limited in scope and/or accuracy, particularly at the skull base. We present a new brain CTA bone removal tool, which addresses many of these limitations. A deep convolutional neural network was designed and trained for bone removal using 72 brain CTAs. The model was tested on 15 CTAs from the same data source and 17 CTAs from an independent external dataset. Bone removal accuracy was assessed quantitatively, by comparing automated segmentation results to manual segmentations, and qualitatively by evaluating VR visualization of the carotid siphons compared to an existing method for automated bone removal. Average Dice overlap between automated and manual segmentations from the internal and external test datasets were 0.986 and 0.979 respectively. This was superior compared to a publicly available noncontrast head CT bone removal algorithm which had a Dice overlap of 0.947 (internal dataset) and 0.938 (external dataset). Our algorithm yielded better VR visualization of the carotid siphons than the publicly available bone removal tool in 14 out of 15 CTAs (93%, chi-square statistic of 22.5, p-value of < 0.00001) from the internal test dataset and 15 out of 17 CTAs (88%, chi-square statistic of 23.1, p-value of < 0.00001) from the external test dataset. Bone removal allowed subjectively superior MIP and VR visualization of vascular anatomy/pathology. The proposed brain CTA bone removal algorithm is rapid, accurate, and allows superior visualization of vascular anatomy and pathology compared to other available techniques and was validated on an independent external dataset.

The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa)

Gliomas are the most common type of primary brain tumors. Although gliomas are relatively rare, they are among the deadliest types of cancer, with a survival rate of less than 2 years after diagnosis. Gliomas are challenging to diagnose, hard to treat and inherently resistant to conventional therapy. Years of extensive research to improve diagnosis and treatment of gliomas have decreased mortality rates across the Global North, while chances of survival among individuals in low- and middle-income countries (LMICs) remain unchanged and are significantly worse in Sub-Saharan Africa (SSA) populations. Long-term survival with glioma is associated with the identification of appropriate pathological features on brain MRI and confirmation by histopathology. Since 2012, the Brain Tumor Segmentation (BraTS) Challenge have evaluated state-of-the-art machine learning methods to detect, characterize, and classify gliomas. However, it is unclear if the state-of-the-art methods can be widely implemented in SSA given the extensive use of lower-quality MRI technology, which produces poor image contrast and resolution and more importantly, the propensity for late presentation of disease at advanced stages as well as the unique characteristics of gliomas in SSA (i.e., suspected higher rates of gliomatosis cerebri). Thus, the BraTS-Africa Challenge provides a unique opportunity to include brain MRI glioma cases from SSA in global efforts through the BraTS Challenge to develop and evaluate computer-aided-diagnostic (CAD) methods for the detection and characterization of glioma in resource-limited settings, where the potential for CAD tools to transform healthcare are more likely.

The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma

Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on multiparametric MRI (mpMRI) for diagnosis, treatment planning, and longitudinal treatment monitoring; yet automated, objective, and quantitative tools for non-invasive assessment of meningiomas on mpMRI are lacking. The BraTS meningioma 2023 challenge will provide a community standard and benchmark for state-of-the-art automated intracranial meningioma segmentation models based on the largest expert annotated multilabel meningioma mpMRI dataset to date. Challenge competitors will develop automated segmentation models to predict three distinct meningioma sub-regions on MRI including enhancing tumor, non-enhancing tumor core, and surrounding nonenhancing T2/FLAIR hyperintensity. Models will be evaluated on separate validation and held-out test datasets using standardized metrics utilized across the BraTS 2023 series of challenges including the Dice similarity coefficient and Hausdorff distance. The models developed during the course of this challenge will aid in incorporation of automated meningioma MRI segmentation into clinical practice, which will ultimately improve care of patients with meningioma.

Parieto-Occipital Injury on Diffusion MRI Correlates with Poor Neurologic Outcome following Cardiac Arrest

BACKGROUND AND PURPOSE

MR imaging of the brain provides unbiased neuroanatomic evaluation of brain injury and is useful for neurologic prognostication following cardiac arrest. Regional analysis of diffusion imaging may provide additional prognostic value and help reveal the neuroanatomic underpinnings of coma recovery. The purpose of this study was to evaluate global, regional, and voxelwise differences in diffusion-weighted MR imaging signal in patients in a coma after cardiac arrest.

MATERIALS AND METHODS

We retrospectively analyzed diffusion MR imaging data from 81 subjects who were comatose for >48 hours following cardiac arrest. Poor outcome was defined as the inability to follow simple commands at any point during hospitalization. ADC differences between groups were evaluated across the whole brain, locally by using voxelwise analysis and regionally by using ROI-based principal component analysis.

RESULTS

Subjects with poor outcome had more severe brain injury as measured by lower average whole-brain ADC (740 [SD, 102] × 10-6 mm2/s versus 833 [SD, 23] × 10-6 mm2/s, P < .001) and larger average volumes of tissue with ADC below 650 × 10-6 mms/s (464 [SD, 469] mL versus 62 [SD, 51] mL, P < .001). Voxelwise analysis showed lower ADC in the bilateral parieto-occipital areas and perirolandic cortices for the poor outcome group. ROI-based principal component analysis showed an association between lower ADC in parieto-occipital regions and poor outcome.

CONCLUSIONS

Brain injury affecting the parieto-occipital region measured with quantitative ADC analysis was associated with poor outcomes after cardiac arrest. These results suggest that injury to specific brain regions may influence coma recovery.

Deep Learning to Simulate Contrast-enhanced Breast MRI of Invasive Breast Cancer

Background There is increasing interest in noncontrast breast MRI alternatives for tumor visualization to increase the accessibility of breast MRI. Purpose To evaluate the feasibility and accuracy of generating simulated contrast-enhanced T1-weighted breast MRI scans from precontrast MRI sequences in biopsy-proven invasive breast cancer with use of deep learning. Materials and Methods Women with invasive breast cancer and a contrast-enhanced breast MRI examination that was performed for initial evaluation of the extent of disease between January 2015 and December 2019 at a single academic institution were retrospectively identified. A three-dimensional, fully convolutional deep neural network simulated contrast-enhanced T1-weighted breast MRI scans from five precontrast sequences (T1-weighted non-fat-suppressed [FS], T1-weighted FS, T2-weighted FS, apparent diffusion coefficient, and diffusion-weighted imaging). For qualitative assessment, four breast radiologists (with 3-15 years of experience) blinded to whether the method of contrast was real or simulated assessed image quality (excellent, acceptable, good, poor, or unacceptable), presence of tumor enhancement, and maximum index mass size by using 22 pairs of real and simulated contrast-enhanced MRI scans. Quantitative comparison was performed using whole-breast similarity and error metrics and Dice coefficient analysis of enhancing tumor overlap. Results Ninety-six MRI examinations in 96 women (mean age, 52 years ± 12 [SD]) were evaluated. The readers assessed all simulated MRI scans as having the appearance of a real MRI scan with tumor enhancement. Index mass sizes on real and simulated MRI scans demonstrated good to excellent agreement (intraclass correlation coefficient, 0.73-0.86; P < .001) without significant differences (mean differences, -0.8 to 0.8 mm; P = .36-.80). Almost all simulated MRI scans (84 of 88 [95%]) were considered of diagnostic quality (ratings of excellent, acceptable, or good). Quantitative analysis demonstrated strong similarity (structural similarity index, 0.88 ± 0.05), low voxel-wise error (symmetric mean absolute percent error, 3.26%), and Dice coefficient of enhancing tumor overlap of 0.75 ± 0.25. Conclusion It is feasible to generate simulated contrast-enhanced breast MRI scans with use of deep learning. Simulated and real contrast-enhanced MRI scans demonstrated comparable tumor sizes, areas of tumor enhancement, and image quality without significant qualitative or quantitative differences. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Slanetz in this issue. An earlier incorrect version appeared online. This article was corrected on January 17, 2023.

Federated learning enables big data for rare cancer boundary detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.

NIMG-67. MULTI-PARAMETRIC MRI-BASED MACHINE LEARNING ANALYSIS FOR PREDICTION OF NEOPLASTIC INFILTRATION AND RECURRENCE IN PATIENTS WITH GLIOBLASTOMA: UPDATES FROM THE MULTI-INSTITUTIONAL RESPOND CONSORTIUM

PURPOSE

Glioblastoma is extremely infiltrative with malignant cells extending beyond the enhancing rim where recurrence inevitably occurs, despite aggressive multimodal therapy. We hypothesize that important characteristics of peritumoral tissue heterogeneity captured and analyzed by multi-parametric MRI and artificial intelligence (AI) methods are generalizable in the updated multi-institutional ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium and predictive of neoplastic infiltration and future recurrence.

METHODS

We used the most recent update of the ReSPOND consortium to evaluate and further refine generalizability of our methods with different scanners and acquisition settings. 179 de novo glioblastoma patients with available T1, T1Gd, T2, T2-FLAIR, and ADC sequences at pre-resection baseline and after complete resection with subsequent pathology-confirmed recurrence were included. To establish generalizability of the predictive models, training and testing of the refined AI model was performed through Leave-One-Institution-Out-Cross-Validation schema. The multi-institutional cohort consisted of the Hospital of the University of Pennsylvania (UPenn, 124), Case Western Reserve University/University Hospitals (CWRU/UH, 27), New York University (NYU, 13), Ohio State University (OSU, 13), and University Hospital Río Hortega (RH, 2). Features extracted from pre-resection MRI were used to build the model predicting the spatial pattern of subsequent tumor recurrence. These predictions were evaluated against regions of pathology-confirmed post-resection recurrence.

RESULTS

Our model predicted the locations that later harbored tumor recurrence with overall odds ratio (99% CI)/AUC (99% CI), 12.0(11.8-12.2)/0.80(0.76-0.85), and per institute, CWRU/UH, 11.0(10.7-11.3)/0.80 (0.64-0.97); NYU, 7.0(6.7-7.3)/0.78(0.56-1.00); OSU, 18.3(17.5-19.1)/0.83(0.54-1.00); RH, 40.0(35.3-45.5)/0.93(0.00-1.00); UPenn, 8.00(7.7-8.3)/0.80(0.75-0.84).

CONCLUSION

This study provides extensive multi-institutional validated evidence that machine learning tools can identify peritumoral neoplastic infiltration and predict location of future recurrence, by decrypting the MRI signal heterogeneity in peritumoral tissue. Our analyses leveraged the unique dataset of the ReSPOND consortium, which aims to develop and validate AI-based biomarkers for individualized prediction and prognostication and establish generalizability in a multi-institutional setting.

NIMG-15. INTEGRATED RADIOGRAPHIC AND PATHOLOGICAL ANALYSIS OF MALIGNANT PERIPHERAL NERVE SHEATH TUMORS (MPNSTS) REVEALS IMAGING CORRELATES OF MOLECULAR ALTERATIONS AND CLINICAL OUTCOME

Malignant peripheral nerve sheath tumors (MPNSTs) are the most common cause of death in patients with neurofibromatosis type 1 (NF-1) yet non-invasive diagnosis and risk stratification of NF-1-associated peripheral nerve tumors remains challenging. Moreover, the relationship between radiographic features and pathologic measures such as mitotic index and necrosis, or molecular markers of malignant transformation such as H3K27 trimethylation and Schwann cell marker (S100B, SOX10) expression remain unknown. Here, we integrate positron emission tomography (PET) (n=32 studies from 18 patients) and magnetic resonance imaging (MRI) (n=20 studies from 20 patients) with immunohistochemical analysis and clinical follow-up in a total of 20 patients with pathologically confirmed MPNST diagnosis. The median age was 33 years (range 5-74 years), n = 11 were male (55%) and n=13 (65%) harbored a clinical diagnosis of NF-1. Anatomically, n=6 (30%) tumors arose near the spine, n=5 (25%) arose in the extremities, and n=4 (20%) arose in the sacrum. By PET, the average preoperative SUVmax was 8.53 with a trend toward increased SUVmax in Ki-67 high tumors (SUVmax 10.58 vs. 6.47, p=0.08). No significant differences in SUVmax based on H3K27 trimethylation, SOX10, or S100B expression was noted. The appearance of cystic necrosis by MRI was significantly associated with H3K27 trimethylation loss (Chi-square p=0.04) with a trend toward increased KI-67 labeling (43% vs. 24%, p = 0.07), suggesting cystic necrosis identifies aggressive lesions. No other features were correlated with H3K27 trimethylation status or Ki-67 labeling index. With regard to clinical outcome, dichotomized SUVmax identified patient subgroups with significant differences in overall survival (OS), with high SUVmax tumors demonstrating improved survival (median OS: 250 months vs. 60 months, log-rank test p=0.02). Our data support a relationship between radiographic features, histopathologic characteristics and clinical outcomes. Future work will include longitudinal analysis, validation in larger, multi-institutional cohorts and incorporation of radiomic approaches.

NIMG-33. PROGNOSTIC STRATIFICATION OF DE NOVO GLIOBLASTOMA PATIENTS ACROSS 22 GEOGRAPHICALLY DISTINCT INSTITUTIONS: UPDATES FROM THE RESPOND CONSORTIUM

PURPOSE

Glioblastoma, IDH-wildtype, is the most common primary malignant adult brain tumor with median overall survival (OS) of ~14 months, with little improvement over the last 20 years. We hypothesize that AI-based integration of quantitative tumor characteristics, independent of acquisition protocol and equipment, can reveal accurate generalizable prognostic stratification. We seek an AI-based OS predictor using routine clinically acquired MRI sequences, quantitatively evaluated across institutions of the ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium.

METHODS

We identified a retrospective cohort of 2,293 diffuse glioma (IDH-wildtype/-NOS/-NEC) patients from 22 geographically distinct institutions across 3 continents, with preoperative structural MRI scans. The entire tumor burden was automatically segmented into 3 sub-compartments, i.e., enhancing, necrotic, peritumoral T2-FLAIR abnormality. We developed our AI predictor by multivariate integration of i)patient age, ii)tumor sub-compartment volume normalized to brain volume, iii)spatial distribution characteristics (tumor location, distance to the ventricles, and laterality), and iv)morphologic descriptors (major axes’ length, axes’ ratio, extent, and number of tumors). The AI predictor returns a continuous value between 0-1, defining short-, intermediate-, and long-survivors based on thresholds on the 25th and 75th percentiles. Leave-One-Site-Out-Cross-Validation was used to assess the generalizability of our stratification. Kaplan-Meier survival curves were computed for OS analysis and evaluated by a Cox proportional hazards model for statistical significance and hazard ratios.

RESULTS

Survival analysis yielded a hazard ratio of 2.07 (95%CI, 2.06-2.08, p-value= 4.8e-102) for patient stratification into short-, intermediate-, and long-survivors. Pearson correlation between the predicted and actual OS yielded an R= 0.49.

CONCLUSION

Multivariate integration of visually quantified tumor characteristics, agnostic to acquisition protocol/equipment, yields an accurate OS surrogate index. Validation of our AI model in the largest centralized glioblastoma imaging dataset, from the ReSPOND consortium, supports its generalizability across diverse patient populations and acquisition settings, potentially contributing to equitable improvements of personalized patient care.

The University of California San Francisco Preoperative Diffuse Glioma MRI Dataset

Supplemental material is available for this article. Keywords: Informatics, MR Diffusion Tensor Imaging, MR Perfusion, MR Imaging, Neuro-Oncology, CNS, Brain/Brain Stem, Oncology, Radiogenomics, Radiology-Pathology Integration © RSNA, 2022.

Longitudinal Assessment of Posttreatment Diffuse Glioma Tissue Volumes with Three-dimensional Convolutional Neural Networks

Neural networks were trained for segmentation and longitudinal assessment of posttreatment diffuse glioma. A retrospective cohort (from January 2018 to December 2019) of 298 patients with diffuse glioma (mean age, 52 years ± 14 [SD]; 177 men; 152 patients with glioblastoma, 72 patients with astrocytoma, and 74 patients with oligodendroglioma) who underwent two consecutive multimodal MRI examinations were randomly selected into training (n = 198) and testing (n = 100) samples. A posttreatment tumor segmentation three-dimensional nnU-Net convolutional neural network with multichannel inputs (T1, T2, and T1 postcontrast and fluid-attenuated inversion recovery [FLAIR]) was trained to segment three multiclass tissue types (peritumoral edematous, infiltrated, or treatment-changed tissue [ED]; active tumor or enhancing tissue [AT]; and necrotic core). Separate longitudinal change nnU-Nets were trained on registered and subtracted FLAIR and T1 postlongitudinal images to localize and better quantify and classify changes in ED and AT. Segmentation Dice scores, volume similarities, and 95th percentile Hausdorff distances ranged from 0.72 to 0.89, 0.90 to 0.96, and 2.5 to 3.6 mm, respectively. Accuracy rates of the posttreatment tumor segmentation and longitudinal change networks being able to classify longitudinal changes in ED and AT as increased, decreased, or unchanged were 76%-79% and 90%-91%, respectively. The accuracy levels of the longitudinal change networks were not significantly different from those of three neuroradiologists (accuracy, 90%-92%; κ, 0.58-0.63; P > .05). The results of this study support the potential clinical value of artificial intelligence-based automated longitudinal assessment of posttreatment diffuse glioma. Keywords: MR Imaging, Neuro-Oncology, Neural Networks, CNS, Brain/Brain Stem, Segmentation, Quantification, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2022.

AB0836 Micronutrients deficiencies in Enteropathic Spondyloarthritis: the interplay with metabolism and HLA-B27 in disease phenotype

Background

Micronutrients play immunomodulatory roles through interactions with innate and adaptive immunity influencing the pro/anti-inflammatory balance. Serum levels depend on multiple factors such as gender, nutrition, and gut microbiome. Micronutrient deficiencies (MNDs) are associated with a pro-inflammatory status and co-morbidities in patients with chronic inflammatory diseases. No studies focused on potential interplay between MNDs and disease phenotype in Enteropathic Spondyloarthritis (ESpA) in which the combination of SpA with inflammatory bowel diseases (IBD) might dramatically affect micronutrients status.

Objectives

We analysed the occurrence of anemia (Hb ≤12 g/dl) and deficiencies of ferritin (Fe, ≤15 ng/dL), vitamin D [25(OH)D, ≤20 ng/ml], vitamin B12 (VB12, ≤200 pg/ml), and folic acid (FA, ≤ 4ng/mL) in ESpA patients. The interplay of MNDs with gender, metabolic parameters, HLA-B27 susceptibility, type of SpA and IBD, disease activity, and treatments was also explored.

Methods

Selection criteria of this cross-sectional descriptive study consisted of having a diagnosis of ESpA, regardless of its type, in an age of ESpA onset ≥18 and ≤80 years, among patients who were admitted to a combined Gastro-Intestinal and RHEumatologic “GI–Rhe” clinic (Tor Vergata University Hospital, Rome, Italy). Exclusion criteria were represented by active IBD, pregnancy or lactation, kidney and/or liver failure, alcohol abuse, neoplasia, ongoing supplementations. SpA disease activity was assessed by ASDAS-CRP and functional status by HAQ-S. All the enrolled patients underwent blood chemistry analysis to determine parameters including CRP, uric acid (SUA), Fe, 25(OH)D, VB12, FA, and HLA-B27 typing.

Results

We included 164 patients comprising 109 females and 55 age-matched males. A diagnosis of Crohn’s Disease (CD) occurred in 70% of patients while Ulcerative Colitis (UC) represented a third of the cohort. Peripheral (per) and axial (ax) SpA were equally distributed (50%vs50%). Patients with ax-SpA displayed a greater prevalence of CD than UC (p=0.02) while UC was prevalent in per-SpA (p=0.02). B27+ was revealed in 19% of our cohort: B27+ patients had a higher prevalence of ax-SpA (p=0.016) and a more severe disease activity (p=0.02) than B27-. Moreover, B27 positivity and uveitis were prevalent in ax-SpA compared to per-SpA (p=0.009 and p=0.01, respectively). According to univariate analysis, males showed higher SUA (P=0.004) and BMI (p=0.03) than females. Conversely, females showed a higher prevalence of anemia than males (p=0.002). A third of ESpA cohort showed FA (31.6%) and 25(OH)D (27.8%) deficiency while VB12 defect was less frequent (18.2%) and was registered almost entirely in B27- ESpA (p=0.02). CD-ESpA showed a lower mean VB12 (p=0.04) and a higher prevalence of ocular/skin co-morbidities (p=0.02) and ax-SpA (p=0.04) than UC-ESpA. Accordingly, CD-ESpA were on bDMARDs more than UC-ESpA (p=0.04).

Conclusion

Our findings document that FA and 25(OH)D deficiencies represent the main MNDs among ESpA patients while VB12 seems to be deficient mostly in patients with CD and almost exclusively in B27- patients. Otherwise, B27+ in ESpA results to be associated mainly with disease phenotype and treatments. In ESpA, the gender of patients appears to impact principally on dysmetabolism highlighting the role for nutritional interventions particularly in males. The interplay of MNDs with B27 and dysmetabolism in ESpA deserves further investigations also taking into account CD/UC localization and behavior.

References

[1]Park YE, et al. Incidence and risk factors of micronutrient deficiency in patients with IBD and intestinal Behçet’s disease: folate, vitamin B12, 25-OH-vitamin D, and ferritin. BMC Gastroenterol. 2021;21(1):32.doi: 10.1186/s12876-021-01609-8.

[2]Conigliaro P, et al. Impact of a multidisciplinary approach in enteropathic spondyloarthritis patients. Autoimmun Rev. 2016;15(2):184-90.doi: 10.1016/j.autrev.2015.11.002.

Disclosure of Interests

None declared

SARS-CoV-2 Igg seroprevalence in IBD patients treated with biologics: first vs. second pandemic wave in a prospective study

OBJECTIVE

In a prospective study, SARS-CoV-2 IgG seroprevalence was assessed during the second pandemic wave (W2) in a cohort of Inflammatory Bowel Disease (IBD) patients using biologics. The secondary aim was to compare, in the same cohort, the frequency of seropositivity and of COVID-19 during the second vs. the first (W1) wave.

PATIENTS AND METHODS

From November 2020 to March 2021, SARS-CoV-2 IgG seropositivity and the prevalence of COVID-19 were assessed in a cohort of IBD patients using biologics already studied at W1.

INCLUSION CRITERIA

age ≥ 18 years; diagnosis of IBD; follow-up; written consent.

EXCLUSION CRITERIA

SARS-CoV-2 vaccination. Risk factors for infection, compatible symptoms, history of infection or COVID-19, nasopharyngeal swab test were recorded. Data were expressed as median [range]. The χ2 test, Student's t-test, logistic regression analysis was used.

RESULTS

IBD cohort at W1 and W2 included 85 patients: 45 CD (52.9%), 40 UC (47.1%). When comparing the same 85 patients at W2 vs. W1, a higher SARS-CoV-2 seroprevalence at W2 was at the limit of the statistical significance (9.4% vs. 2.3%; p=0.05). The prevalence of COVID-19 at W2 vs. W1 was 3.5% (3/85) vs. 0% (0/85) (p=0.08). Contacts with COVID-19 patients and symptoms compatible with COVID-19 were more frequent at W2 vs. W1 (18.8 % vs. 0%; p=0.0001; 34.1% vs. 15.3%; p=0.004). At W2, history of contacts and new onset diarrhea were more frequent in seropositive patients [4/8 (50%) vs. 12/77 (15.6%); p=0.01 and 4/8 (50%) vs. 2/77 (2.6%); p=0.0001]. At W2, the risk factors for seropositivity included cough, fever, new onset diarrhea, rhinitis, arthromyalgia, dysgeusia/anosmia at univariate (p<0.05), but not at multivariate analysis. History of contacts was the only risk factor for seropositivity at univariate (p=0.03), but not at multivariate analysis (p=0.1).

CONCLUSIONS

During W2, characterized by a high viral spread, IBD and biologics appeared not to increase the prevalence of SARS-CoV-2 infection or COVID-19 disease. New onset diarrhea mimicking IBD relapse may be observed in patients with SARS-CoV-2 infection.

Combining radiomics and deep convolutional neural network features from preoperative MRI for predicting clinically relevant genetic biomarkers in glioblastoma

Background

Glioblastoma is the most common primary brain malignancy, yet treatment options are limited, and prognosis remains guarded. Individualized tumor genetic assessment has become important for accurate prognosis and for guiding emerging targeted therapies. However, challenges remain for widespread tumor genetic testing due to costs and the need for tissue sampling. The aim of this study is to evaluate a novel artificial intelligence method for predicting clinically relevant genetic biomarkers from preoperative brain MRI in patients with glioblastoma.

Methods

We retrospectively analyzed preoperative MRI data from 400 patients with glioblastoma and grade 4 astrocytoma, IDH mutant who underwent resection and genetic testing. Nine genetic biomarkers were assessed: hotspot mutations of IDH1 or TERT promoter, pathogenic mutations of TP53, PTEN, ATRX, or CDKN2A/B, MGMT promoter methylation, EGFR amplification, and combined aneuploidy of chromosomes 7 & 10. Models were developed to predict biomarker status from MRI data using radiomics features, convolutional neural network (CNN) features, and a combination of both.

Results

Combined model performance was good for IDH1 and TERT promoter hotspot mutations, pathogenic mutations of ATRX and CDKN2A/B, and combined aneuploidy of chromosomes 7 & 10, with receiver operating characteristic area under the curve (ROC AUC) &gt; 0.85 and was fair for all other tested biomarkers with ROC AUC &gt; 0.7. Combined model performance was statistically superior to individual radiomics and CNN feature models for prediction chromosome 7 & 10 aneuploidy, MGMT promoter methylation, and PTEN loss.

Conclusions

Combining radiomics and CNN features from preoperative MRI yields improved non-invasive genetic biomarker prediction performance in patients with grade 4 diffuse gliomas.

Structural Connectivity of Human Inferior Colliculus Subdivisions Using in vivo and post mortem Diffusion MRI Tractography

Inferior colliculus (IC) is an obligatory station along the ascending auditory pathway that also has a high degree of top-down convergence via efferent pathways, making it a major computational hub. Animal models have attributed critical roles for the IC in in mediating auditory plasticity, egocentric selection, and noise exclusion. IC contains multiple functionally distinct subdivisions. These include a central nucleus that predominantly receives ascending inputs and external and dorsal nuclei that receive more heterogeneous inputs, including descending and multisensory connections. Subdivisions of human IC have been challenging to identify and quantify using standard brain imaging techniques such as MRI, and the connectivity of each of these subnuclei has not been identified in the human brain. In this study, we estimated the connectivity of human IC subdivisions with diffusion MRI (dMRI) tractography, using both anatomical-based seed analysis as well as unsupervised k-means clustering. We demonstrate sensitivity of tractography to overall IC connections in both high resolution post mortem and in vivo datasets. k-Means clustering of the IC streamlines in both the post mortem and in vivo datasets generally segregated streamlines based on their terminus beyond IC, such as brainstem, thalamus, or contralateral IC. Using fine-grained anatomical segmentations of the major IC subdivisions, the post mortem dataset exhibited unique connectivity patterns from each subdivision, including commissural connections through dorsal IC and lateral lemniscal connections to central and external IC. The subdivisions were less distinct in the context of in vivo connectivity, although lateral lemniscal connections were again highest to central and external IC. Overall, the unsupervised and anatomically driven methods provide converging evidence for distinct connectivity profiles for each of the IC subdivisions in both post mortem and in vivo datasets, suggesting that dMRI tractography with high quality data is sensitive to neural pathways involved in auditory processing as well as top-down control of incoming auditory information.

Interinstitutional Portability of a Deep Learning Brain MRI Lesion Segmentation Algorithm

PURPOSE

To assess how well a brain MRI lesion segmentation algorithm trained at one institution performed at another institution, and to assess the effect of multi-institutional training datasets for mitigating performance loss.

MATERIALS AND METHODS

In this retrospective study, a three-dimensional U-Net for brain MRI abnormality segmentation was trained on data from 293 patients from one institution (IN1) (median age, 54 years; 165 women; patients treated between 2008 and 2018) and tested on data from 51 patients from a second institution (IN2) (median age, 46 years; 27 women; patients treated between 2003 and 2019). The model was then trained on additional data from various sources: (a) 285 multi-institution brain tumor segmentations, (b) 198 IN2 brain tumor segmentations, and (c) 34 IN2 lesion segmentations from various brain pathologic conditions. All trained models were tested on IN1 and external IN2 test datasets, assessing segmentation performance using Dice coefficients.

RESULTS

The U-Net accurately segmented brain MRI lesions across various pathologic conditions. Performance was lower when tested at an external institution (median Dice score, 0.70 [IN2] vs 0.76 [IN1]). Addition of 483 training cases of a single pathologic condition, including from IN2, did not raise performance (median Dice score, 0.72; P = .10). Addition of IN2 training data with heterogeneous pathologic features, representing only 10% (34 of 329) of total training data, increased performance to baseline (Dice score, 0.77; P < .001). This final model produced total lesion volumes with a high correlation to the reference standard (Spearman r = 0.98).

CONCLUSION

For brain MRI lesion segmentation, adding a modest amount of relevant training data from an external institution to a previously trained model supported successful application of the model to this external institution.Keywords: Neural Networks, Brain/Brain Stem, Segmentation Supplemental material is available for this article. © RSNA, 2021.

A high-resolution interactive atlas of the human brainstem using magnetic resonance imaging

Conventional atlases of the human brainstem are limited by the inflexible, sparsely-sampled, two-dimensional nature of histology, or the low spatial resolution of conventional magnetic resonance imaging (MRI). Postmortem high-resolution MRI circumvents the challenges associated with both modalities. A single human brainstem specimen extending from the rostral diencephalon through the caudal medulla was prepared for imaging after the brain was removed from a 65-year-old male within 24 h of death. The specimen was formalin-fixed for two weeks, then rehydrated and placed in a custom-made MRI compatible tube and immersed in liquid fluorocarbon. MRI was performed in a 7-Tesla scanner with 120 unique diffusion directions. Acquisition time for anatomic and diffusion images were 14 h and 208 h, respectively. Segmentation was performed manually. Deterministic fiber tractography was done using strategically chosen regions of interest and avoidance, with manual editing using expert knowledge of human neuroanatomy. Anatomic and diffusion images were rendered with isotropic resolutions of 50 μm and 200 μm, respectively. Ninety different structures were segmented and labeled, and 11 different fiber bundles were rendered with tractography. The complete atlas is available online for interactive use at https://www.civmvoxport.vm.duke.edu/voxbase/login.php?return_url=%2Fvoxbase%2F. This atlas presents multiple contrasting datasets and selected tract reconstruction with unprecedented resolution for MR imaging of the human brainstem. There are immediate applications in neuroanatomical education, with the potential to serve future applications for neuroanatomical research and enhanced neurosurgical planning through "safe" zones of entry into the human brainstem.

Feasibility of Simulated Postcontrast MRI of Glioblastomas and Lower-Grade Gliomas by Using Three-dimensional Fully Convolutional Neural Networks

PURPOSE

To evaluate the feasibility and accuracy of simulated postcontrast T1-weighted brain MR images generated by using precontrast MR images in patients with brain glioma.

MATERIALS AND METHODS

In this retrospective study, a three-dimensional deep convolutional neural network was developed to simulate T1-weighted postcontrast images from eight precontrast sequences in 400 patients (mean age, 57 years; 239 men; from 2015 to 2020), including 332 with glioblastoma and 68 with lower-grade gliomas. Performance was evaluated by using quantitative image similarity and error metrics and enhancing tumor overlap analysis. Performance was also assessed on a multicenter external dataset (n = 286 from the 2019 Multimodal Brain Tumor Segmentation Challenge; mean age, 60 years; ratio of men to women unknown) by using transfer learning. A subset of cases was reviewed by neuroradiologist readers to assess whether simulated images affected the ability to determine the tumor grade.

RESULTS

Simulated whole-brain postcontrast images were both qualitatively and quantitatively similar to the real postcontrast images in terms of quantitative image similarity (structural similarity index of 0.84 ± 0.05), pixelwise error (symmetric mean absolute percent error of 3.65%), and enhancing tumor compartment overlap (Dice coefficient, 0.65 ± 0.25). Similar results were achieved with the external dataset (Dice coefficient, 0.62 ± 0.27). There was no difference in the ability of the neuroradiologist readers to determine the tumor grade in real versus simulated images (accuracy, 87.7% vs 90.6%; P = .87).

CONCLUSION

The developed model was capable of producing simulated postcontrast T1-weighted MR images that were similar to real acquired images as determined by both quantitative analysis and radiologist assessment.Keywords: MR-Contrast Agent, MR-Imaging, CNS, Brain/Brain Stem, Contrast Agents-Intravenous, Neoplasms-Primary, Experimental Investigations, Technology Assessment, Supervised Learning, Transfer Learning, Convolutional Neural Network, Deep Learning Algorithms, Machine Learning Algorithms Supplemental material is available for this article. © RSNA, 2021.

Low prevalence of SARS-CoV-2 infection in inflammatory bowel disease

OBJECTIVE

Treatments used in Inflammatory Bowel Disease (IBD) have been associated with enhanced risk of viral infections and viral reactivation, however, it remains unclear whether IBD patients have increased risk of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. The aim of the study was to examine the prevalence of SARS-CoV-2 IgG positivity in IBD patients followed at our referral center. The role of treatments for IBD and risk factors for infection were also evaluated.

PATIENTS AND METHODS

In a prospective study, all IBD patients followed at our referral centre between May 27th and July 21st, 2020 and fulfilling the inclusion criteria were tested for SARS-CoV-2 IgG. Specific IgG antibodies were evaluated by a commercial ELISA kit and SARS-CoV-2 nasopharyngeal swab was performed in seropositive patients.

RESULTS

Two-hundred and eighteen patients, 128 Crohn's disease (CD) and 90 Ulcerative colitis (UC) [age 44, (19-77) years; ongoing biologics in 115 (52.7%)] were enrolled. No patient had major SARS-CoV-2-related symptoms. SARS-CoV-2 IgG were detected in 3 out of 218 (1.37%) patients with IBD (2 CD and 1 UC), all on biologics (2.6%). In all of the 3 seropositive patients, the nasopharyngeal swab was negative. There was no relationship between SARS-CoV-2 seroprevalence and the demographic/clinical characteristics of IBD patients. In contrast, history of recent travel was more frequent in the SARS-CoV-2 seropositive patients (2/3; 66.6%) than in SARS-CoV-2 seronegative patients [7/215 (3.25%); p<0.0001].

CONCLUSIONS

The prevalence of SARS-CoV-2 IgG seropositivity in IBD patients appears to be comparable to the non-IBD population and not influenced by ongoing treatments. Risk factors for infection common to the general non-IBD population should be considered when managing patients with IBD.

Hepatic follicular lymphoma in an old patient with Crohn's disease: a rare case and review of the literature

OBJECTIVE

Crohn's Disease (CD) has been associated with non-Hodgkin lymphoma. Follicular Lymphoma (FL) limited to the liver is extremely rare, accounting for 1% to 4.4% of all Primary Hepatic Lymphoma (PHL).

CASE PRESENTATION

In 2018, an 85-years old male patient with post-operative recurrence of ileal CD referred rare episodes of fever and mild diffuse abdominal pain. Since cholecystectomy in 2001, clinical history was characterized by recurrent episodes of cholangitis and common bile duct stones. In 2018, ultrasonography and MRI showed a solid focal hepatic lesion (FHL)(4.5 cm x 2.5 cm) in the IV hepatic segment. The radiographic aspect of the lesion was unusual. Initially, focal nodular hyperplasia was suspected. Clinical history of cholangitis and radiological findings subsequently suggested a diagnosis of Hepatic Abscess (HA). A progressive enlargement of the FHL (7.3 cm x 5.8 cm) despite antibiotic treatments, led to perform a liver biopsy. Histological and immunophenotypical analysis of the FHL (7.5 cm x 5.4 cm) enabled a final diagnosis of FL. The "in situ" hybridization for Epstein-Barr virus (EBER) was negative. No additional lesions related to FL were initially detected, thus suggesting a very rare case of PHL in an old patient with CD never treated with thiopurines.

CONCLUSIONS

This case report highlights the need to consider a rare diagnosis of FL of the liver in patients showing a challenging focal hepatic lesion of unknown origin.

3D Exploration of the Brainstem in 50-Micron Resolution MRI

The brainstem, a structure of vital importance in mammals, is currently becoming a principal focus in cognitive, affective, and clinical neuroscience. Midbrain, pontine and medullary structures serve as the conduit for signals between the forebrain and spinal cord, are the epicenter of cranial nerve-circuits and systems, and subserve such integrative functions as consciousness, emotional processing, pain, and motivation. In this study, we parcellated the nuclear masses and the principal fiber pathways that were visible in a high-resolution T2-weighted MRI dataset of 50-micron isotropic voxels of a postmortem human brainstem. Based on this analysis, we generated a detailed map of the human brainstem. To assess the validity of our maps, we compared our observations with histological maps of traditional human brainstem atlases. Given the unique capability of MRI-based morphometric analysis in generating and preserving the morphology of 3D objects from individual 2D sections, we reconstructed the motor, sensory and integrative neural systems of the brainstem and rendered them in 3D representations. We anticipate the utilization of these maps by the neuroimaging community for applications in basic neuroscience as well as in neurology, psychiatry, and neurosurgery, due to their versatile computational nature in 2D and 3D representations in a publicly available capacity.

A fully automated artificial intelligence method for non-invasive, imaging-based identification of genetic alterations in glioblastomas

Glioblastoma is the most common malignant brain parenchymal tumor yet remains challenging to treat. The current standard of care-resection and chemoradiation-is limited in part due to the genetic heterogeneity of glioblastoma. Previous studies have identified several tumor genetic biomarkers that are frequently present in glioblastoma and can alter clinical management. Currently, genetic biomarker status is confirmed with tissue sampling, which is costly and only available after tumor resection or biopsy. The purpose of this study was to evaluate a fully automated artificial intelligence approach for predicting the status of several common glioblastoma genetic biomarkers on preoperative MRI. We retrospectively analyzed multisequence preoperative brain MRI from 199 adult patients with glioblastoma who subsequently underwent tumor resection and genetic testing. Radiomics features extracted from fully automated deep learning-based tumor segmentations were used to predict nine common glioblastoma genetic biomarkers with random forest regression. The proposed fully automated method was useful for predicting IDH mutations (sensitivity = 0.93, specificity = 0.88), ATRX mutations (sensitivity = 0.94, specificity = 0.92), chromosome 7/10 aneuploidies (sensitivity = 0.90, specificity = 0.88), and CDKN2 family mutations (sensitivity = 0.76, specificity = 0.86).

The regional pattern of abnormal cerebrovascular reactivity in HIV-infected, virally suppressed women

The purpose of this study was to assess whole brain and regional patterns of cerebrovascular reactivity (CVR) abnormalities in HIV-infected women using quantitative whole brain arterial spin labeling (ASL). We hypothesized that HIV-infected women would demonstrate decreased regional brain CVR despite viral suppression. This cross-sectional study recruited subjects from the Bay Area Women's Interagency Health Study (WIHS)-a cohort study designed to investigate the progression of HIV disease in women. In addition to conventional noncontrast cerebral MRI sequences, perfusion imaging was performed before and after the administration of intravenous acetazolamide. CVR was measured by comparing quantitative ASL brain perfusion before and after administration of intravenous acetazolamide. In order to validate and corroborate ASL-based whole brain and regional perfusion, phase-contrast (PC) imaging was also performed through the major neck vessels. FLAIR and susceptibility weighted sequences were performed to assess for white matter injury and microbleeds, respectively. Ten HIV-infected women and seven uninfected, age-matched controls were evaluated. Significant group differences were present in whole brain and regional CVR between HIV-infected and uninfected women. These regional differences were significant in the frontal lobe and basal ganglia. CVR measurements were not significantly impacted by the degree of white matter signal abnormality or presence of microbleeds. Despite complete viral suppression, dysfunction of the neurovascular unit persists in the HIV population. Given the lack of association between CVR and traditional imaging markers of small vessel disease, CVR quantification may provide an early biomarker of pre-morbid vascular disease.

Low-Volume and High-Volume Readers of Neurological and Musculoskeletal MRI: Achieving Subspecialization in Radiology

OBJECTIVE

Differentiate high- versus low-volume radiologists who interpret neurological (Neuro) MRI or musculoskeletal (MSK) MRI and measure the proportion of Neuro and MSK MRIs read by low-volume radiologists.

METHODS

We queried the 2015 Medicare Physician and Other Supplier Public Use File for radiologists who submitted claims for Neuro or MSK MRIs. Radiologists were classified as high-volume versus low-volume based on their work relative value units (wRVUs) focus or volume of studies interpreted using three different methodologies: Method 1, percentage of wRVUs in Neuro or MSK MRI; Method 2, absolute number of Neuro or MSK MRIs interpreted; and Method 3, both percentage and absolute number. Multiple thresholds with each methodology were tested, and the percentage of Neuro or MSK MRIs interpreted by low-volume radiologists was calculated for each threshold.

RESULTS

With Method 1, 33% of Neuro MRI and 50% of MSK MRI studies were interpreted by a radiologist whose wRVUs in Neuro or MSK MRI were less than 20% (Method 1). With Method 2, 22% of Neuro MRIs and 37% of MSK MRIs were interpreted by radiologists who read fewer than the mean number of Neuro or MSK MRIs interpreted by an "average full-time radiologist" whose wRVUs in Neuro or MSK MRI were approximately 20%. With Method 3, 38% of Neuro MRIs and 57% of MSK MRIs were interpreted by "low-volume" radiologists. If instead a 50% wRVU threshold is used for Methods One, Two, and Three, then 70%, 58%, and 77% of Neuro MRIs and 86%, 80%, and 90% of MSK MRIs are read by low-volume radiologists.

DISCUSSION

A large number of radiologists read a low volume of Neuro or MSK MRIs; these low-volume Neuro or MSK MRI radiologists read a substantial portion of Neuro or MSK MRIs. It is unknown which of the methods for distinguishing low-volume radiologists, combined with which threshold, may best correlate with high-performing or low-performing radiologists.

Traces of Imidacloprid Induce Hormesis as a Stimulatory Conditioned Response of Sweetpotato Whitefly (Hemiptera: Aleyrodidae)

Our purpose is to determine whether extremely low concentrations of imidacloprid (2-8 ppb) typically found in field soil 1-3 yr after a crop is grown using seed with a standard imidacloprid seed-coating could impact the fitness of whiteflies, Bemisia tabaci Gennadius (Hemiptera: Aleyrodidae). Results of our experiments indicate that imidacloprid-resistant whitefly larvae feeding on cotton seedlings growing in soil with 8.0 ppb imidacloprid are conditioned so that when the same individuals feed on plants treated with imidacloprid as adults their fitness, measured as fecundity, increases 30-70% compared with individuals that were not primed as larvae. This conditioning hormesis stimulates resistant whiteflies more than susceptible whiteflies, which may contribute to the selection of resistant populations.

Mapping the human subcortical auditory system using histology, postmortem MRI and in vivo MRI at 7T

Studying the human subcortical auditory system non-invasively is challenging due to its small, densely packed structures deep within the brain. Additionally, the elaborate three-dimensional (3-D) structure of the system can be difficult to understand based on currently available 2-D schematics and animal models. Wfe addressed these issues using a combination of histological data, post mortem magnetic resonance imaging (MRI), and in vivo MRI at 7 Tesla. We created anatomical atlases based on state-of-the-art human histology (BigBrain) and postmortem MRI (50 µm). We measured functional MRI (fMRI) responses to natural sounds and demonstrate that the functional localization of subcortical structures is reliable within individual participants who were scanned in two different experiments. Further, a group functional atlas derived from the functional data locates these structures with a median distance below 2 mm. Using diffusion MRI tractography, we revealed structural connectivity maps of the human subcortical auditory pathway both in vivo (1050 µm isotropic resolution) and post mortem (200 µm isotropic resolution). This work captures current MRI capabilities for investigating the human subcortical auditory system, describes challenges that remain, and contributes novel, openly available data, atlases, and tools for researching the human auditory system.

Structural mapping with fiber tractography of the human cuneate fasciculus at microscopic resolution in cervical region

Human spinal white matter tract anatomy has been mapped using post mortem histological information with the help of molecular tracing studies in animal models. This study used 7 Tesla diffusion MR tractography on a human cadaver that was harvested 24 hours post mortem to evaluate cuneate fasciculus anatomy in cervical spinal cord. Based on this method, for the first time much more nuanced tractographic anatomy was used to investigate possible new routes for cuneate fasciculus in the posterior and lateral funiculus. Additionally, current molecular tracing studies were reviewed, and confirmatory data was presented along with our radiological results. Both studies confirm that upon entry to the spinal cord, upper cervical level tracts (C1-2-3) travel inside lateral funiculus and lower level tracts travel medially inside the posterior funiculus after entry at posterolateral sulcus which is different than traditional knowledge of having cuneate fasciculus tracts concentrated in the lateral part of posterior funiculus.

Postmortem diffusion MRI of the entire human spinal cord at microscopic resolution

The human spinal cord is a central nervous system structure that plays an important role in normal motor and sensory function, and can be affected by many debilitating neurologic diseases. Due to its clinical importance, the spinal cord is frequently the subject of imaging research. Common methods for visualizing spinal cord anatomy and pathology include histology and magnetic resonance imaging (MRI), both of which have unique benefits and drawbacks. Postmortem microscopic resolution MRI of fixed specimens, sometimes referred to as magnetic resonance microscopy (MRM), combines many of the benefits inherent to both techniques. However, the elongated shape of the human spinal cord, along with hardware and scan time limitations, have restricted previous microscopic resolution MRI studies (both in vivo and ex vivo) to small sections of the cord. Here we present the first MRM dataset of the entire postmortem human spinal cord. These data include 50 μm isotropic resolution anatomic image data and 100 μm isotropic resolution diffusion data, made possible by a 280 h long multi-segment acquisition and automated image segment composition. We demonstrate the use of these data for spinal cord lesion detection, automated volumetric gray matter segmentation, and quantitative spinal cord morphometry including estimates of cross sectional dimensions and gray matter fraction throughout the length of the cord.

Repeated mild blast exposure in young adult rats results in dynamic and persistent microstructural changes in the brain

A history of mild traumatic brain injury (mTBI), particularly repeated mTBI (rmTBI), has been identified as a risk factor for late-onset neurodegenerative conditions. The mild and transient nature of early symptoms often impedes diagnosis in young adults who are disproportionately affected by mTBIs. A proportion of the affected population will incur long-term behavioral and cognitive consequences but the underlying pathomechanism is currently unknown. Diffusion tensor imaging (DTI) provides sensitive and quantitative assessment of TBI-induced structural changes, including white matter injury, and may be used to predict long-term outcome. We used DTI in an animal model of blast rmTBI (rmbTBI) to quantify blast-induced structural changes at 7 and 90 days post-injury, and their evolution between the two time points. Young adult male rats (~P65 at injury) were exposed to repeated mild blast overpressure, or anesthetized as shams, and their fixed brains were imaged using high-field (7 T) MRI. We found that whole brain volumes similarly increased in injured and sham rats from 7 to 90 days. However, we detected localized volume increases in blast-exposed animals 7 days post-injury, mainly ipsilateral to incident blast waves. Affected regions included gray matter of the frontal association, cingulate, and motor cortex, thalamus, substantia nigra, and raphe nuclei (median and dorsal), as well as white matter of the internal capsule and cerebral peduncle. Conversely, we measured volume reductions in these and other regions, including the hippocampus and cerebellum, at 90 days post-injury. DTI also detected both transient and persistent microstructural changes following injury, with some changes showing distinct ipsilateral versus contralateral side differences relative to blast impact. Early changes in fractional anisotropy (FA) were subtle, becoming more prominent at 90 days in the cerebral and inferior cerebellar peduncles, and cerebellar white matter. Widespread increases in radial diffusivity (RD) and axial diffusivity (primary eigenvalue or E1) at 7 days post-injury largely subsided by 90 days, although RD was more sensitive than E1 at detecting white matter changes. E1 effects in gray and white matter, which paralleled increases in apparent diffusion, were likely more indicative of dysregulated water homeostasis than pathologic structural changes. Importantly, we found evidence for a different developmental trajectory following rmbTBI, as indicated by significant injury x age interactions on volume. Our findings demonstrate that rmbTBI initiates dynamic pathobiological processes that may negatively alter the course of late-stage neurodevelopment and adversely affect long-term cognitive and behavioral outcomes.

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Young adult rats exposed to mild blast show lasting microstructural brain changes.

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The evolution of mTBI pathology was reflected by temporal changes in DTI measures.

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Regional volume changes captured significant injury × age interactions.

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DTI measures differentially captured injury effects in white and gray matter.

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Significant interaction effects suggest an altered developmental trajectory.

Successful treatment of pneumatosis intestinalis with associated pneumoperitoneum and ileus with hyperbaric oxygen therapy

Pneumatosis intestinalis (PI), or the presence of air in the bowel wall, is a rare disorder that is associated with a variety of underlying diseases, including connective tissue disorders. PI presents on a spectrum from asymptomatic to bowel obstruction and acute abdomen. In general, treatment of PI consists of treating the underlying disease. Both normobaric and hyperbaric oxygen have been used to treat PI directly. Here we report a symptomatic scleroderma-related case of PI that responded clinically to hyperbaric oxygen therapy. This report adds to a growing body of literature supporting a role for hyperbaric oxygen therapy in symptomatic PI.

Quantifying the brain's sheet structure with normalized convolution

The hypothesis that brain pathways form 2D sheet-like structures layered in 3D as "pages of a book" has been a topic of debate in the recent literature. This hypothesis was mainly supported by a qualitative evaluation of "path neighborhoods" reconstructed with diffusion MRI (dMRI) tractography. Notwithstanding the potentially important implications of the sheet structure hypothesis for our understanding of brain structure and development, it is still considered controversial by many for lack of quantitative analysis. A means to quantify sheet structure is therefore necessary to reliably investigate its occurrence in the brain. Previous work has proposed the Lie bracket as a quantitative indicator of sheet structure, which could be computed by reconstructing path neighborhoods from the peak orientations of dMRI orientation density functions. Robust estimation of the Lie bracket, however, is challenging due to high noise levels and missing peak orientations. We propose a novel method to estimate the Lie bracket that does not involve the reconstruction of path neighborhoods with tractography. This method requires the computation of derivatives of the fiber peak orientations, for which we adopt an approach called normalized convolution. With simulations and experimental data we show that the new approach is more robust with respect to missing peaks and noise. We also demonstrate that the method is able to quantify to what extent sheet structure is supported for dMRI data of different species, acquired with different scanners, diffusion weightings, dMRI sampling schemes, and spatial resolutions. The proposed method can also be used with directional data derived from other techniques than dMRI, which will facilitate further validation of the existence of sheet structure.

Diffusion Tractography in Deep Brain Stimulation Surgery: A Review

Deep brain stimulation (DBS) is believed to exert its therapeutic effects through modulation of brain circuitry, yet conventional preoperative planning does not allow direct targeting or visualization of white matter pathways. Diffusion MRI tractography (DT) is virtually the only non-invasive method of visualizing structural connectivity in the brain, leading many to suggest its use to guide DBS targeting. DT-guided DBS not only has the potential to allow direct white matter targeting for established applications [e.g., Parkinson’s disease (PD), essential tremor (ET), dystonia], but may also aid in the discovery of new therapeutic targets for a variety of other neurologic and psychiatric diseases. Despite these exciting opportunities, DT lacks standardization and rigorous anatomic validation, raising significant concern for the use of such data in stereotactic brain surgery. This review covers the technical details, proposed methods, and initial clinical data for the use of DT in DBS surgery. Rather than focusing on specific disease applications, this review focuses on methods that can be applied to virtually any DBS target.

Automated segmentation of the canine corpus callosum for the measurement of diffusion tensor imaging

The goal of this study was to apply image registration-based automated segmentation methods to measure diffusion tensor imaging (DTI) metrics within the canine brain. Specifically, we hypothesized that this method could measure DTI metrics within the canine brain with greater reproducibility than with hand-drawn region of interest (ROI) methods. We performed high-resolution post-mortem DTI imaging on two canine brains on a 7 T MR scanner. We designated the two brains as brain 1 and brain 2. We measured DTI metrics within the corpus callosum of brain 1 using a hand-drawn ROI method and an automated segmentation method in which ROIs from brain 2 were transformed into the space of brain 1. We repeated both methods in order to measure their reliability. Mean differences between the two sets of hand-drawn ROIs ranged from 4% to 10%. Mean differences between the hand-drawn ROIs and the automated ROIs were less than 3%. The mean differences between the first and second automated ROIs were all less than 0.25%. Our findings indicate that the image registration-based automated segmentation method was clearly the more reproducible method. These results provide the groundwork for using image registration-based automated segmentation methods to measure DTI metrics within the canine brain. Such methods will facilitate the study of white matter pathology in canine models of neurologic disease.

Correlation of diffusion tensor imaging parameters in the canine brain

The goal of this study was to determine the degree to which ex vivo diffusion tensor imaging (DTI) parameters correlate to one another in white matter regions on very high resolution MR scans. Specifically, we hypothesized that radial diffusivity (RD) and apparent diffusion coefficient (ADC) would correlate more closely than either would correlate with fractional anisotropy (FA). We performed post mortem DTI imaging on three canine brains on a 7 T MR scanner (TR = 100 ms, NEX = 1, gradient amplitude = 600 mT/m, b = 1492-1,565 s/mm²) and generated maps of FA, RD, and ADC. We measured RD, FA and ADC within 14 regions of interest representative of various portions of white matter. We compared the three combinations of values, i.e., FA vs ADC, FA vs RD and ADC vs RD, using linear regression models. Linear regression demonstrated that RD was significantly correlated with FA (p << 0.01; R² = 0.3053) and also with ADC (p << 0.01; R² = 0.6755), but to a much greater degree. However, ADC was not significantly correlated with FA (p = 0.526; R² = 0.101). Our findings suggest that both RD and ADC reflect similar cytoarchitectural features, most likely that of myelination, whereas FA values likely reflect both myelination and additional microstructural features that constrain the diffusion of water in white matter.

Predicting degree of myelination based on diffusion tensor imagining of canines with mucopolysaccharidosis type I

OBJECTIVE

This study examined the effect of mucopolysaccharidosis (MPS) type 1 on diffusion tensor imaging (DTI) metrics in the canine brain. We hypothesized 1) white matter regions in the MPS brain would show decreased fractional anisotropy (FA) and increased radial diffusivity (RD) compared to the same regions in normal brain, 2) compared to FA, RD would more closely correlate with myelin density and fiber coherence, and 3) DTI and histological data from the normal brain could be used to accurately predict degree of myelination in the MPS brain using DTI metrics.

METHODS

We performed DTI imaging on one normal canine brain and two MPS brains on a 7T MR scanner and generated FA and RD maps. Brains were sectioned and stained with a gold chloride stain for myelin to obtain myelin optical density and fiber coherence values. The three brains were compared using the DTI and histology metrics.

RESULTS

Most measured regions in one MPS brain and all measured regions in the other MPS brain exhibited decreased FA, increased RD, and decreased myelin density in white matter. FA and RD significantly correlated with myelin density in the normal brain but failed to reach significance in either MPS brain. A predictive model using FA but not RD was able to accurately predict degree of myelination in one MPS brain.

CONCLUSION

Dysmyelination in the MPS brain results in decreased FA and increased RD. However, in the small sample, FA and RD were values not significantly correlated with myelination in either MPS brain.