22 research outputs found
Criteria for evaluation of disease extent by 123I-metaiodobenzylguanidine scans in neuroblastoma: a report for the International Neuroblastoma Risk Group (INRG) Task Force
BackgroundNeuroblastoma is an embryonic tumour of the sympathetic nervous system, metastatic in half of the patients at diagnosis, with a high preponderance of osteomedullary disease, making accurate evaluation of metastatic sites and response to therapy challenging. Metaiodobenzylguanidine (mIBG), taken into cells via the norepinephrine transporter, provides a sensitive and specific method of assessing tumour in both soft tissue and bone sites. The goal of this report was to develop consensus guidelines for the use of mIBG scans in staging, response assessment and surveillance in neuroblastoma.MethodsThe International Neuroblastoma Risk Group (INRG) Task Force, including a multidisciplinary group in paediatric oncology of North and South America, Europe, Oceania and Asia, formed a subcommittee on metastatic disease evaluation, including expert nuclear medicine physicians and oncologists, who developed these guidelines based on their experience and the medical literature, with approval by the larger INRG Task Force.ResultsGuidelines for patient preparation, radiotracer administration, techniques of scanning including timing, energy, specific views, and use of single photon emission computed tomography are included. Optimal timing of scans in relation to therapy and for surveillance is reviewed. Validated semi-quantitative scoring methods in current use are reviewed, with recommendations for use in prognosis and response evaluation.ConclusionsMetaiodobenzylguanidine scans are the most sensitive and specific method of staging and response evaluation in neuroblastoma, particularly when used with a semi-quantitative scoring method. Use of the optimal techniques for mIBG in staging and response, including a semi-quantitative score, is essential for evaluation of the efficacy of new therapy
When algorithm selection meets Bi-linear Learning to Rank: accuracy and inference time trade off with candidates expansion
Colonial institutions : uses, subversions, and material afterlives
Archaeologically based explorations of colonialism or institutions are common case-studies in global historical archaeology, but the "colonial institution"-the role of institutions as operatives of colonialism-has often been neglected. In this thematic edition we argue that in order to fully understand the interconnected, global world one must explicitly dissect the colonial institution as an entwined, dual manifestation that is central to understanding both power and power relations in the modern world. Following Ann Laura Stoler, we have selected case studies from the Australia, Europe, UK and the USAwhich reveal that the study of colonial institutions should not be limited to the functional life of these institutions-or solely those that take the form of monumental architecture-but should include the long shadow of "imperial debris" (Stoler 2008) and immaterial institutions
Identifying Indices of Happiness and Unhappiness Among Adults With Autism: Potential Targets for Behavioral Assessment and Intervention
Interactive Data Analytics for the Humanities
In this vision paper, we argue that current solutions to data analytics are not suitable for complex tasks from the humanities, as they are agnostic of the user and focused on static, predefined tasks with large-scale benchmarks. Instead, we believe that the human must be put into the loop to address small data scenarios that require expert domain knowledge and fluid, incrementally defined tasks, which are common for many humanities use cases. Besides the main challenges, we discuss existing and urgently required solutions to interactive data acquisition, model development, model interpretation, and system support for interactive data analytics. In the envisioned interactive systems, human users not only provide annotations to a machine learner, but train a model by using the system and demonstrating the task. The learning system will actively query the user for feedback, refine its model in real-time, and is able to explain its decisions. Our vision links natural language processing research with recent advances in machine learning, computer vision, and data management systems, as realizing this vision relies on combining expertise from all of these scientific fields