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

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

OpenPBTA: The Open Pediatric Brain Tumor Atlas.

Pediatric brain and spinal cancers are collectively the leading disease-related cause of death in children; thus, we urgently need curative therapeutic strategies for these tumors. To accelerate such discoveries, the Children's Brain Tumor Network (CBTN) and Pacific Pediatric Neuro-Oncology Consortium (PNOC) created a systematic process for tumor biobanking, model generation, and sequencing with immediate access to harmonized data. We leverage these data to establish OpenPBTA, an open collaborative project with over 40 scalable analysis modules that genomically characterize 1,074 pediatric brain tumors. Transcriptomic classification reveals universal TP53 dysregulation in mismatch repair-deficient hypermutant high-grade gliomas and TP53 loss as a significant marker for poor overall survival in ependymomas and H3 K28-mutant diffuse midline gliomas. Already being actively applied to other pediatric cancers and PNOC molecular tumor board decision-making, OpenPBTA is an invaluable resource to the pediatric oncology community.

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

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

Unsupervised machine learning using K-means identifies radiomic subgroups of pediatric low-grade gliomas that correlate with key molecular markers.

INTRODUCTION: Despite advancements in molecular and histopathologic characterization of pediatric low-grade gliomas (pLGGs), there remains significant phenotypic heterogeneity among tumors with similar categorizations. We hypothesized that an unsupervised machine learning approach based on radiomic features may reveal distinct pLGG imaging subtypes. METHODS: Multi-parametric MR images (T1 pre- and post-contrast, T2, and T2 FLAIR) from 157 patients with pLGGs were collected and 881 quantitative radiomic features were extracted from tumorous region. Clustering was performed using K-means after applying principal component analysis (PCA) for feature dimensionality reduction. Molecular and demographic data was obtained from the PedCBioportal and compared between imaging subtypes. RESULTS: K-means identified three distinct imaging-based subtypes. Subtypes differed in mutational frequencies of BRAF (p < 0.05) as well as the gene expression of BRAF (p<0.05). It was also found that age (p < 0.05), tumor location (p < 0.01), and tumor histology (p < 0.0001) differed significantly between the imaging subtypes. CONCLUSION: In this exploratory work, it was found that clustering of pLGGs based on radiomic features identifies distinct, imaging-based subtypes that correlate with important molecular markers and demographic details. This finding supports the notion that incorporation of radiomic data could augment our ability to better characterize pLGGs.

The children's brain tumor network (CBTN) - Accelerating research in pediatric central nervous system tumors through collaboration and open science.

Pediatric brain tumors are the leading cause of cancer-related death in children in the United States and contribute a disproportionate number of potential years of life lost compared to adult cancers. Moreover, survivors frequently suffer long-term side effects, including secondary cancers. The Children's Brain Tumor Network (CBTN) is a multi-institutional international clinical research consortium created to advance therapeutic development through the collection and rapid distribution of biospecimens and data via open-science research platforms for real-time access and use by the global research community. The CBTN's 32 member institutions utilize a shared regulatory governance architecture at the Children's Hospital of Philadelphia to accelerate and maximize the use of biospecimens and data. As of August 2022, CBTN has enrolled over 4700 subjects, over 1500 parents, and collected over 65,000 biospecimen aliquots for research. Additionally, over 80 preclinical models have been developed from collected tumors. Multi-omic data for over 1000 tumors and germline material are currently available with data generation for > 5000 samples underway. To our knowledge, CBTN provides the largest open-access pediatric brain tumor multi-omic dataset annotated with longitudinal clinical and outcome data, imaging, associated biospecimens, child-parent genomic pedigrees, and in vivo and in vitro preclinical models. Empowered by NIH-supported platforms such as the Kids First Data Resource and the Childhood Cancer Data Initiative, the CBTN continues to expand the resources needed for scientists to accelerate translational impact for improved outcomes and quality of life for children with brain and spinal cord tumors.

Personas for the translational workforce.

Twelve evidence-based profiles of roles across the translational workforce and two patients were made available through clinical and translational science (CTS) Personas, a project of the Clinical and Translational Science Awards (CTSA) Program National Center for Data to Health (CD2H). The persona profiles were designed and researched to demonstrate the key responsibilities, motivators, goals, software use, pain points, and professional development needs of those working across the spectrum of translation, from basic science to clinical research to public health. The project's goal was to provide reliable documents that could be used to inform CTSA software development projects, educational resources, and communication initiatives. This paper presents the initiative to create personas for the translational workforce, including the methodology, engagement strategy, and lessons learned. Challenges faced and successes achieved by the project may serve as a roadmap for others searching for best practices in the creation of Persona profiles.

Mapping scientific landscapes in UMLS research: a scientometric review.

OBJECTIVE: The Unified Medical Language System (UMLS) is 1 of the most successful, collaborative efforts of terminology resource development in biomedicine. The present study aims to 1) survey historical footprints, emerging technologies, and the existing challenges in the use of UMLS resources and tools, and 2) present potential future directions. MATERIALS AND METHODS: We collected 10 469 bibliographic records published between 1986 and 2019, using a Web of Science database. graph analysis, data visualization, and text mining to analyze domain-level citations, subject categories, keyword co-occurrence and bursts, document co-citation networks, and landmark papers. RESULTS: The findings show that the development of UMLS resources and tools have been led by interdisciplinary collaboration among medicine, biology, and computer science. Efforts encompassing multiple disciplines, such as medical informatics, biochemical sciences, and genetics, were the driving forces behind the domain's growth. The following topics were found to be the dominant research themes from the early phases to mid-phases: 1) development and extension of ontologies and 2) enhancing the integrity and accessibility of these resources. Knowledge discovery using machine learning and natural language processing and applications in broader contexts such as drug safety surveillance have recently been receiving increasing attention. DISCUSSION: Our analysis confirms that while reaching its scientific maturity, UMLS research aims to boundary-span to more variety in the biomedical context. We also made some recommendations for editorship and authorship in the domain. CONCLUSION: The present study provides a systematic approach to map the intellectual growth of science, as well as a self-explanatory bibliometric profile of the published UMLS literature. It also suggests potential future directions. Using the findings of this study, the scientific community can better align the studies within the emerging agenda and current challenges.

Translational Personas and Hospital Library Services.

Academic health centers, CTSA hubs, and hospital libraries experience similar funding challenges and charges to do more with less. In recent years academic health center and hospital librarians have risen to these challenges by examining their service models, and beyond that, examining their patron base and users' needs. To meet the needs of employees, patients, and those who assist patients, hospital librarians can employ the CTS Personas, a project of the Clinical and Translational Science Awards (CTSA) Program National Center for Data to Health. The Persona profiles, which outline the motivations, goals, pain points, wants, and needs of twelve employees and two patients in translational science, provide vital information and insights that can inform everything from designing software tools and educational services, to advertising these services, to designing impactful and collaborative library spaces.

Visualizing Patterns and Trends of 25 Years of Published Health Literacy Research.

BACKGROUND: With an increase in the number of disciplines contributing to health literacy scholarship, we sought to explore the nature of interdisciplinary research in the field. OBJECTIVE: This study sought to describe disciplines that contribute to health literacy research and to quantify how disciplines draw from and contribute to an interdisciplinary evidence base, as measured by citation networks. METHODS: We conducted a literature search for health literacy articles published between 1991 and 2015 in four bibliographic databases, producing 6,229 unique bibliographic records. We employed a scientometric tool (CiteSpace [Version 4.4.R1]) to quantify patterns in published health literacy research, including a visual path from cited discipline domains to citing discipline domains. KEY RESULTS: The number of health literacy publications increased each year between 1991 and 2015. Two spikes, in 2008 and 2013, correspond to the introduction of additional subject categories, including information science and communication. Two journals have been cited more than 2,000 times-the Journal of General Internal Medicine (n = 2,432) and Patient Education and Counseling (n = 2,252). The most recently cited journal added to the top 10 list of cited journals is the Journal of Health Communication (n = 989). Three main citation paths exist in the health literacy data set. Articles from the domain "medicine, medical, clinical" heavily cite from one domain (health, nursing, medicine), whereas articles from the domain "psychology, education, health" cite from two separate domains (health, nursing, medicine and psychology, education, social). CONCLUSIONS: Recent spikes in the number of published health literacy articles have been spurred by a greater diversity of disciplines contributing to the evidence base. However, despite the diversity of disciplines, citation paths indicate the presence of a few, self-contained disciplines contributing to most of the literature, suggesting a lack of interdisciplinary research. To address complex and evolving challenges in the health literacy field, interdisciplinary team science, that is, integrating science from across multiple disciplines, should continue to grow. [Health Literacy Research and Practice. 2017;1(4):e182-e191.]. PLAIN LANGUAGE SUMMARY: The addition of diverse disciplines conducting health literacy scholarship has spurred recent spikes in the number of publications. However, citation paths suggest that interdisciplinary research can be strengthened. Findings directly align with the increasing emphasis on team science, and support opportunities and resources that incentivize interdisciplinary health literacy research.

Emerging trends and new developments in regenerative medicine: a scientometric update (2000 - 2014).

INTRODUCTION: Our previous scientometric review of regenerative medicine provides a snapshot of the fast-growing field up to the end of 2011. The new review identifies emerging trends and new developments appearing in the literature of regenerative medicine based on relevant articles and reviews published between 2000 and the first month of 2014. AREAS COVERED: Multiple datasets of publications relevant to regenerative medicine are constructed through topic search and citation expansion to ensure adequate coverage of the field. Networks of co-cited references representing the literature of regenerative medicine are constructed and visualized based on a combined dataset of 71,393 articles published between 2000 and 2014. Structural and temporal dynamics are identified in terms of most active topical areas and cited references. New developments are identified in terms of newly emerged clusters and research areas. Disciplinary-level patterns are visualized in dual-map overlays. EXPERT OPINION: While research in induced pluripotent stem cells remains the most prominent area in the field of regenerative medicine, research related to clinical and therapeutic applications in regenerative medicine has experienced a considerable growth. In addition, clinical and therapeutic developments in regenerative medicine have demonstrated profound connections with the induced pluripotent stem cell research and stem cell research in general. A rapid adaptation of graphene-based nanomaterials in regenerative medicine is evident. Both basic research represented by stem cell research and application-oriented research typically found in tissue engineering are now increasingly integrated in the scientometric landscape of regenerative medicine. Tissue engineering is an interdisciplinary field in its own right. Advances in multiple disciplines such as stem cell research and graphene research have strengthened the connections between tissue engineering and regenerative medicine.