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  • 1
    Online Resource
    Online Resource
    Brain MRI Deep Learning and Bayesian Inference System Augments Radiology Resident Performance
    Springer Science and Business Media LLC ; 2021
    In:  Journal of Digital Imaging Vol. 34, No. 4 ( 2021-08), p. 1049-1058
    Details
    In: Journal of Digital Imaging, Springer Science and Business Media LLC, Vol. 34, No. 4 ( 2021-08), p. 1049-1058
    Type of Medium: Online Resource
    ISSN: 0897-1889 , 1618-727X
    URL: Article
    DOI: 10.1007/s10278-021-00470-1
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2080328-X
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  • 2
    Online Resource
    Online Resource
    Spatiotemporal profile of atrophy in the first year following moderate‐severe traumatic brain injury
    Wiley ; 2023
    In:  Human Brain Mapping Vol. 44, No. 13 ( 2023-09), p. 4692-4709
    Details
    In: Human Brain Mapping, Wiley, Vol. 44, No. 13 ( 2023-09), p. 4692-4709
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 1065-9471 , 1097-0193
    URL: Issue
    URL: Article
    DOI: 10.1002/hbm.v44.13
    DOI: 10.1002/hbm.26410
    Language: English
    Publisher: Wiley
    Publication Date: 2023
    detail.hit.zdb_id: 1492703-2
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  • 3
    Online Resource
    Online Resource
    Vigorous Exercise in Patients With Hypertrophic Cardiomyopathy
    American Medical Association (AMA) ; 2023
    In:  JAMA Cardiology Vol. 8, No. 6 ( 2023-06-01), p. 595-
    Details
    In: JAMA Cardiology, American Medical Association (AMA), Vol. 8, No. 6 ( 2023-06-01), p. 595-
    Abstract: Whether vigorous intensity exercise is associated with an increase in risk of ventricular arrhythmias in individuals with hypertrophic cardiomyopathy (HCM) is unknown. Objective To determine whether engagement in vigorous exercise is associated with increased risk for ventricular arrhythmias and/or mortality in individuals with HCM. The a priori hypothesis was that participants engaging in vigorous activity were not more likely to have an arrhythmic event or die than those who reported nonvigorous activity. Design, Setting, and Participants This was an investigator-initiated, prospective cohort study. Participants were enrolled from May 18, 2015, to April 25, 2019, with completion in February 28, 2022. Participants were categorized according to self-reported levels of physical activity: sedentary, moderate, or vigorous-intensity exercise. This was a multicenter, observational registry with recruitment at 42 high-volume HCM centers in the US and internationally; patients could also self-enroll through the central site. Individuals aged 8 to 60 years diagnosed with HCM or genotype positive without left ventricular hypertrophy (phenotype negative) without conditions precluding exercise were enrolled. Exposures Amount and intensity of physical activity. Main Outcomes and Measures The primary prespecified composite end point included death, resuscitated sudden cardiac arrest, arrhythmic syncope, and appropriate shock from an implantable cardioverter defibrillator. All outcome events were adjudicated by an events committee blinded to the patient’s exercise category. Results Among the 1660 total participants (mean [SD] age, 39 [15] years; 996 male [60%]), 252 (15%) were classified as sedentary, and 709 (43%) participated in moderate exercise. Among the 699 individuals (42%) who participated in vigorous-intensity exercise, 259 (37%) participated competitively. A total of 77 individuals (4.6%) reached the composite end point. These individuals included 44 (4.6%) of those classified as nonvigorous and 33 (4.7%) of those classified as vigorous, with corresponding rates of 15.3 and 15.9 per 1000 person-years, respectively. In multivariate Cox regressi on analysis of the primary composite end point, individuals engaging in vigorous exercise did not experience a higher rate of events compared with the nonvigorous group with an adjusted hazard ratio of 1.01. The upper 95% 1-sided confidence level was 1.48, which was below the prespecified boundary of 1.5 for noninferiority. Conclusions and Relevance Results of this cohort study suggest that among individuals with HCM or those who are genotype positive/phenotype negative and are treated in experienced centers, those exercising vigorously did not experience a higher rate of death or life-threatening arrhythmias than those exercising moderately or those who were sedentary. These data may inform discussion between the patient and their expert clinician around exercise participation.
    Type of Medium: Online Resource
    ISSN: 2380-6583
    URL: Article
    DOI: 10.1001/jamacardio.2023.1042
    Language: English
    Publisher: American Medical Association (AMA)
    Publication Date: 2023
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  • 4
    Online Resource
    Online Resource
    Relationship of Cerebral Blood Flow to Cognitive Function and Recovery in Early Chronic Traumatic Brain Injury
    Mary Ann Liebert Inc ; 2020
    In:  Journal of Neurotrauma Vol. 37, No. 20 ( 2020-10-15), p. 2180-2187
    Details
    In: Journal of Neurotrauma, Mary Ann Liebert Inc, Vol. 37, No. 20 ( 2020-10-15), p. 2180-2187
    Type of Medium: Online Resource
    ISSN: 0897-7151 , 1557-9042
    URL: Article
    DOI: 10.1089/neu.2020.7031
    Language: English
    Publisher: Mary Ann Liebert Inc
    Publication Date: 2020
    detail.hit.zdb_id: 2030888-7
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  • 5
    Online Resource
    Online Resource
    IMG-06. RADIOMIC-BASED RISK STRATIFICATION OF PEDIATRIC LOW-GRADE GLIOMA REVEALS DIFFERENTIALLY EXPRESSED MOLECULAR PROCESSES CONTRIBUTING TO VARIABLE PATIENT PROGNOSIS
    Oxford University Press (OUP) ; 2023
    In:  Neuro-Oncology Vol. 25, No. Supplement_1 ( 2023-06-12), p. i47-i48
    Details
    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 25, No. Supplement_1 ( 2023-06-12), p. i47-i48
    Abstract: In pediatric low-grade gliomas (pLGG), prognosis and responses to treatments are heterogeneous. This heterogeneity may be explained by the differences in molecular composition of the tumors of the same histology and the likely upstream alterations following administered radiation or systemic therapy. Integration of radiomics and clinical variables could generate a non-invasive biomarker that provides upfront prediction about patient’s risk of progression. We show that our proposed radiomic-based risk-stratification signature for pLGGs is associated with alterations in transcriptomic pathways. Standard multiparametric MRI sequences of 134 pLGG patients from Children’s Brain Tumor Network (CBTN) were retrospectively collected and 881 quantitative radiomic features were extracted. A multivariate Cox proportional hazard’s (Cox-PH) regression model was fitted on clinical (age, sex, tumor location, and extent of tumor resection) along with radiomic variables using 5-fold cross-validation, to predict patient’s risk of progression. The Cox-PH model showed excellent performance in prediction of PFS and patient’s risk scores, supported by the concordance index of 0.78. Radiogenomic analysis was performed to determine the transcriptomic pathways (1594 pathways, c2 MsigDb Reactome v2022.1) that contribute to the pLGG risk, predicted by the radiomic signature. ElasticNet regression was applied on the scores obtained by gene set enrichment analysis (GSEA) (in 70/134 subjects) to predict radiomic-based risk scores. Increased risk, corresponded to upregulation of DNA repair pathways, dysregulation of lipophagy, fatty acid beta oxidation, and vitamin D pathways, which are tumor-promoting. BRAFfusion signaling inversely correlated with risk, consistent with known favorable prognosis of KIAA1549-BRAF fused pLGGs. Upregulation in immune related pathways and Toll-Like Receptor (TLR) signaling was associated with lower risk. This study elucidates the synergistic dynamics between the biological processes that promote the risk of progression and radiomic-based risk-stratification signature. The proposed biomarker may be used to encourage targeted therapies in patients with increased predicted risk.
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    URL: Article
    DOI: 10.1093/neuonc/noad073.183
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 2094060-9
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  • 6
    Online Resource
    Online Resource
    IMG-05. A MULTI-INSTITUTIONAL AND MULTI-HISTOLOGY PEDIATRIC-SPECIFIC BRAIN TUMOR SUBREGION SEGMENTATION TOOL: FACILITATING RAPNO-BASED ASSESSMENT OF TREATMENT RESPONSE
    Oxford University Press (OUP) ; 2023
    In:  Neuro-Oncology Vol. 25, No. Supplement_1 ( 2023-06-12), p. i47-i47
    Details
    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 25, No. Supplement_1 ( 2023-06-12), p. i47-i47
    Abstract: Current response assessment in pediatric brain tumors (PBTs), as recommended by the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working group, relies on 2D measurements of changes in tumor size. However, there is growing evidence of underestimation of tumor size in PBTs using 2D compared to volumetric (3D) measurement approach. Accordingly, automated methods that reduce manual burden and intra- and inter-rater variability in segmenting tumor subregions and volumetric evaluations are warranted to facilitate tumor response assessment of PBTs. We have developed a fully automatic deep learning (DL) model using the nnUNet architecture on a large cohort of multi-institutional and multi-histology PBTs. The model was trained on widely available standard multiparametric MRI sequences (T1-pre, T1-post, T2, T2-FLAIR) for segmentation of the whole tumor and RAPNO-recommended subregions, including enhancing tumor (ET), non-enhancing tumor (NET), cystic components (CC), and peritumoral edema (ED). As a prerequisite step for accurate tumor segmentation, we also generated another DL model based on DeepMedic for brain extraction from mpMRIs. The models were trained on an institutional cohort of 151 subjects and independently tested on 64 subjects from the internal and 29 patients from external institutions. The trained models showed excellent performance with median Dice scores of 0.98±0.02/0.97±0.02 for brain tissue segmentation, 0.92±0.08/0.90±0.17 for whole tumor segmentation, 0.76±0.31/0.87±0.29 for ET subregion, and 0.82±0.15/0.80±0.28 for segmentation of non-enhancing components (combination of NET, CC, and ED) in internal/external test sets, respectively. The automated segmentation demonstrated strong agreement with expert segmentations in volumetric measurement of tumor components, with Pearson’s correlation coefficients of 0.97, 0.97, 0.99, and 0.79 (p & lt;0.0001) for ET, NET, CC, and ED regions, respectively. Our proposed multi-institutional and multi-histology automated segmentation method has the potential to aid clinical neuro-oncology practice by providing reliable and reproducible volumetric measurements for treatment response assessment.
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    URL: Article
    DOI: 10.1093/neuonc/noad073.182
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 2094060-9
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  • 7
    Online Resource
    Online Resource
    NIMG-87. CHARACTERIZING IMMUNE PROFILES OF PEDIATRIC MEDULLOBLASTOMA AND THEIR RADIOLOGICAL CORRELATES
    Oxford University Press (OUP) ; 2022
    In:  Neuro-Oncology Vol. 24, No. Supplement_7 ( 2022-11-14), p. vii185-vii185
    Details
    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 24, No. Supplement_7 ( 2022-11-14), p. vii185-vii185
    Abstract: Recent studies have shown preliminary evidence for differentiation of the tumor microenvironment (TME) and immune landscape between molecularly-defined medulloblastoma (MB) subtypes. Identifying radiological correlates of these TME patterns could establish a non-invasive method of immune profile characterization for guiding patient-centered therapies. Here, we examine immune profiles between MB subtypes using data from Open Pediatric Brain Tumor Atlas (OpenPBTA), and their relationship to tumor measurements from pre-operative MRIs. We identified a retrospective cohort of 94 pediatric MB patients with available molecular subtyping and immune profiles (36 cell types) from bulk gene expression data. A random forest analysis was used to classify the four MB subtypes based on immune profiles. Four cell types had high impact on classification performance: plasmacytoid dendritic cells (PDC; 25.8% accuracy decrease when randomized), hematopoietic stem cells (HSC; 21.9%), plasma B cells (20.3%), and cancer associated fibroblasts (18.8%). Pairwise comparisons revealed SHH and WNT tumors had significantly higher numbers of fibroblasts and HSCs compared to Group3/Group4. We also found novel evidence for significantly lower amounts of plasma B cells in the SHH group, and high PDC levels in Group4, followed by Group3, and low PDC in SHH/WNT. Multi-parametric MRI scans for 39 patients were used to segment tumor volumes. Overall tumor volume was significantly correlated with composite stroma scores (R = 0.34, p = 0.036). Additionally, patients with higher volumes of gadolinium contrast-enhancing compared to non-enhancing components had higher immune (R = 0.42, p = 0.009) and microenvironment (summed immune and stromal cell types; R = 0.44, p = 0.006) scores, regardless of their molecular subtype. Together, our results demonstrate: (1) the use of rich immune profiles for differentiating molecular subtypes of MB and their unique TME characterization; and (2) initial evidence for radiological correlates of these profiles based on pre-operative imaging collected through standard practices.
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    URL: Article
    DOI: 10.1093/neuonc/noac209.705
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 2094060-9
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  • 8
    Online Resource
    Online Resource
    NIMG-74. RADIOIMMUNOMIC SIGNATURES IN PEDIATRIC LOW-GRADE GLIOMA BASED ON MULTIPARAMETRIC MRI SCANS
    Oxford University Press (OUP) ; 2022
    In:  Neuro-Oncology Vol. 24, No. Supplement_7 ( 2022-11-14), p. vii181-vii182
    Details
    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 24, No. Supplement_7 ( 2022-11-14), p. vii181-vii182
    Abstract: Understanding the immune microenvironment in pediatric low-grade glioma (pLGG) patients may help in identification of the patients who benefit from anti-tumor immunotherapies. However, surgical resection is not feasible for many pLGG tumors in certain anatomical locations. Therefore, developing non-invasive tools that characterize the tumor microenvironment prior to therapeutic interventions could contribute to stratification and enrollment of the patients into relevant clinical trials. In this work, we derived radiomic signatures of immune profiles (radioimmunomics) based on machine learning (ML) analysis of readily available conventional MRI scans. Transcriptomic data for a cohort of 197 subjects was retrospectively collected from Open Pediatric Brain Tumor Atlas (OpenPBTA). The patients were categorized into three groups (Group1-3) based on their immunological profiles using consensus clustering algorithm. This analysis revealed greater immune cell infiltration in non-BRAF mutated pLGGs. Group1 showed more enrichment in M1 macrophages, and microenvironment and immune scores compared to Group2 and Group3. Elevated tumor inflammation score (TIS), as a predictor of clinical response to anti-PD-1 blockade, was observed in Group1 compared to Group2 (p= 1.4e-7) and Group3 (p= 0.0054). Radiomic features, including volumetric, morphologic, histogram, and texture descriptors, were extracted from the segmented tumor regions on multiparametric MRI (mpMRI) scans of 71 (of 197) patients. Multivariate ML models were trained to predict the three immunological groups based on radiomic features using cross-validated random forest classifier along with recursive feature elimination, which yielded AUC of 0.72 for this multi-class classification problem. Our findings indicate the presence of distinct immunological groups in pLGG tumors, with possibly more favorable response to immunotherapies in Group1 tumors. Furthermore, we developed radioimmunomic signatures based on pre-operative conventional mpMRI that can potentially stratify the patients based on their immune tumor microenvironment. Based on these initial promising results, we are exploring additional features to increase the accuracy of radioimmunomics model.
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    URL: Article
    DOI: 10.1093/neuonc/noac209.692
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 2094060-9
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  • 9
    Online Resource
    Online Resource
    IMG-07. MOLECULAR PROCESSES GIVE RISE TO EMERGENCE OF RADIO-PHENOTYPES ON MRI SCANS OF PEDIATRIC LOW-GRADE GLIOMA
    Oxford University Press (OUP) ; 2023
    In:  Neuro-Oncology Vol. 25, No. Supplement_1 ( 2023-06-12), p. i48-i48
    Details
    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 25, No. Supplement_1 ( 2023-06-12), p. i48-i48
    Abstract: There is mounting evidence that tumor microenvironmental pressure selects for somatic genetic alterations that contribute to the formation of distinct morphological characteristics, captured on radiology scans as radio-phenotypes. Such phenotypic variations are a source of heterogeneity in clinical manifestation of tumors of the same histology across the patients, and in part, their heterogeneous responses to therapies. Deeper understanding of the associations between genotype and radio-phenotype in pediatric low-grade glioma (pLGG), the most histologically diverse childhood brain tumor, may facilitate precision diagnostic and therapeutic approaches. Here, we categorize pLGGs into distinct and relatively homogeneous imaging subtypes based on radiomic features and further explore the associations of these imaging subtypes with genotype. From multiparametric MRI scans of 167 pLGGs from the Children’s Brain Tumor Network (CBTN), 881 radiomic features and clinical variables (tumor location, age, and gender) were extracted. After dimensionality reduction using principal component analysis (PCA), K-Means clustering was applied on 19 principal components to group the patients into three imaging subtypes. Using whole transcriptomic data from OpenTargets, differential expression and co-expression of network- and pathway-level and immune-related signaling were compared among these three imaging subtypes. Gene Set Enrichment Analysis (GSEA) revealed differentially higher expression of cell cycle regulatory, extracellular matrix (ECM) remodeling, and cell migratory pathways in imaging subtype1 than subtypes 2 and 3, and upregulation of ECM and immune-related pathways in subtypes 1 and 2 compared to subtype3. Based on Gene Sets Net Correlations Analysis (GSNCA), subtype1 exhibited differential co-regulation of TNF/TNFR1 signaling compared to subtype2, and differential co-regulation of RHOG GTPase and TGFB1pathways when compared against subtype3. Subtype2 showed differential co-regulation in NOTCH1 signaling and transcriptional regulatory pathways. Our proposed multi-disciplinary radiomic-genomic analysis approach elucidates the molecular and biological processes in the genotype of the tumors that are associated with emergence of distinct imaging subtypes in pLGG.
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    URL: Article
    DOI: 10.1093/neuonc/noad073.184
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 2094060-9
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  • 10
    Online Resource
    Online Resource
    NIMG-102. RAPNO-DEFINED SEGMENTATION AND VOLUMETRIC ASSESSMENT OF PEDIATRIC BRAIN TUMORS ON MULTI-PARAMETRIC MRI SCANS USING DEEP LEARNING; A ROBUST TOOL WITH POTENTIAL APPLICATION IN TUMOR RESPONSE ASSESSMENT
    Oxford University Press (OUP) ; 2022
    In:  Neuro-Oncology Vol. 24, No. Supplement_7 ( 2022-11-14), p. vii188-vii189
    Details
    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 24, No. Supplement_7 ( 2022-11-14), p. vii188-vii189
    Abstract: Volumetric measurements of whole tumor and its components on MRI scans, facilitated by automatic segmentation tools, are essential to reduce inter-observer variability in monitoring tumor progression and response assessment for pediatric brain tumors. Here, we present a fully automatic segmentation model based on deep learning that reliably delineates the tumor components recommended by the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working group for evaluation of treatment response. Multi-parametric MRI (mpMRI) scans (T1-pre, T1-post, T2, and T2-FLAIR), acquired on multiple MRI scanners with different field strengths and vendors, for a cohort of 218 pediatric patients with a variety of histologically confirmed brain tumor subtypes were collected. The mpMRI scans were co-registered and manually segmented by experienced neuroradiologists in consensus to identify the tumor subregions including the enhancing tumor (ET), non-enhancing tumor (NET), cystic components (CC), and peritumoral edema (ED) regions. A convolutional neural network model based on DeepMedic architecture was trained using mpMRI scans as the inputs for segmentation of the whole tumor and subregions. The trained model showed excellent performance in segmentation of the whole tumor, as suggested by median dice of 0.90/0.85 for validation (n = 44)/independent test (n = 22) sets. ET and non-enhancing components (union of NET, CC, and ED) were segmented with median dice scores of 0.78/0.84 and 0.76/0.74 for validation/test sets, respectively. The automated and manual segmentations demonstrated strong agreement in estimating VASARI (Visually AcceSAble Rembrandt Images) MRI features with Pearson’s correlation coefficient R & gt; 0.75 (p & lt; 0.0001) for ET, NET, CC, and ED components. Our proposed automated segmentation method developed based on MRI scans acquired with different protocols, equipment, and from a variety of brain tumor subtypes, shows potential application for reliable and generalizable volumetric measurements which can be used for treatment response assessment in clinical trials.
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    URL: Article
    DOI: 10.1093/neuonc/noac209.720
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 2094060-9
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