scholarly journals Quantitative Imaging Informatics for Cancer Research

2020 ◽  
pp. 444-453 ◽  
Author(s):  
Andrey Fedorov ◽  
Reinhard Beichel ◽  
Jayashree Kalpathy-Cramer ◽  
David Clunie ◽  
Michael Onken ◽  
...  
Keyword(s):  
Cancer Research ◽  
Best Practices ◽  
Cancer Imaging ◽  
Quantitative Imaging ◽  
Imaging Informatics ◽  
Imaging Data ◽  
Communication Interface ◽  
3D Slicer ◽  
And Training ◽  
Data Commons

PURPOSE We summarize Quantitative Imaging Informatics for Cancer Research (QIICR; U24 CA180918), one of the first projects funded by the National Cancer Institute (NCI) Informatics Technology for Cancer Research program. METHODS QIICR was motivated by the 3 use cases from the NCI Quantitative Imaging Network. 3D Slicer was selected as the platform for implementation of open-source quantitative imaging (QI) tools. Digital Imaging and Communications in Medicine (DICOM) was chosen for standardization of QI analysis outputs. Support of improved integration with community repositories focused on The Cancer Imaging Archive (TCIA). Priorities included improved capabilities of the standard, toolkits and tools, reference datasets, collaborations, and training and outreach. RESULTS Fourteen new tools to support head and neck cancer, glioblastoma, and prostate cancer QI research were introduced and downloaded over 100,000 times. DICOM was amended, with over 40 correction proposals addressing QI needs. Reference implementations of the standard in a popular toolkit and standalone tools were introduced. Eight datasets exemplifying the application of the standard and tools were contributed. An open demonstration/connectathon was organized, attracting the participation of academic groups and commercial vendors. Integration of tools with TCIA was improved by implementing programmatic communication interface and by refining best practices for QI analysis results curation. CONCLUSION Tools, capabilities of the DICOM standard, and datasets we introduced found adoption and utility within the cancer imaging community. A collaborative approach is critical to addressing challenges in imaging informatics at the national and international levels. Numerous challenges remain in establishing and maintaining the infrastructure of analysis tools and standardized datasets for the imaging community. Ideas and technology developed by the QIICR project are contributing to the NCI Imaging Data Commons currently being developed.

Observations from the Data Integration and Imaging Informatics (DI-Cubed) Project

2020 ◽  
Author(s):  
David Clunie ◽  
Hubert Hickman ◽  
Wendy Ver Hoef ◽  
Smita Hastak ◽  
Julie Evans ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Data Integration ◽  
Imaging Informatics ◽  
Data Element ◽  
Imaging Data ◽  
Common Data Element ◽  
Preliminary Work ◽  
Data Commons ◽  
Disease Specific

In this paper we explore extending the concept of common cross-study Common Data Element concepts beyond simple demographics to cover disease-specific concepts relevant to imaging. We test interactively linking the resulting database to the associated images in a federated manner. We examine the use of existing standards, not only for terminology, but for interchange of serialized data in forms familiar to imaging and clinical trials specialists and their dedicated systems. Our intent is to perform preliminary work to inform both the upcoming Imaging Data Commons specifically, as well as more general integration projects beyond imaging.

The Science of Wisdom in a Polarized World: Knowns and Unknowns

2020 ◽  
Author(s):  
Igor Grossmann ◽  
Nic M. Weststrate ◽  
Monika Ardelt ◽  
Justin Peter Brienza ◽  
Mengxi Dong ◽  
...  
Keyword(s):  
Individual Differences ◽  
Problem Solving ◽  
Best Practices ◽  
Cognitive Sciences ◽  
Methodological Challenges ◽  
The Common ◽  
Empirical Approaches ◽  
And Training ◽  
Contextual Differences

Interest in wisdom in the cognitive sciences, psychology, and education has been paralleled by conceptual confusions about its nature and assessment. To clarify these issues and promote consensus in the field, wisdom researchers met in Toronto in July of 2019, resolving disputes through discussion. Guided by a survey of scientists who study wisdom-related constructs, we established a common wisdom model, observing that empirical approaches to wisdom converge on the morally-grounded application of metacognition to reasoning and problem-solving. After outlining the function of relevant metacognitive and moral processes, we critically evaluate existing empirical approaches to measurement and offer recommendations for best practices. In the subsequent sections, we use the common wisdom model to selectively review evidence about the role of individual differences for development and manifestation of wisdom, approaches to wisdom development and training, as well as cultural, subcultural, and social-contextual differences. We conclude by discussing wisdom’s conceptual overlap with a host of other constructs and outline unresolved conceptual and methodological challenges.

Organizing and Managing Data Analytics Teams for Procurement

2020 ◽  
Author(s):  
Benjamin Shao ◽  
Robert D. St. Louis
Keyword(s):  
Best Practices ◽  
Business Strategy ◽  
Data Analytics ◽  
Team Members ◽  
Procurement Process ◽  
Using Data ◽  
And Training ◽  
Different Levels ◽  
The Way

Many companies are forming data analytics teams to put data to work. To enhance procurement practices, chief procurement officers (CPOs) must work effectively with data analytics teams, from hiring and training to managing and utilizing team members. This chapter presents the findings of a study on how CPOs use data analytics teams to support the procurement process. Surveys and interviews indicate companies are exhibiting different levels of maturity in using data analytics, but both the goal of CPOs (i.e., improving performance to support the business strategy) and the way to interact with data analytics teams for achieving that goal are common across companies. However, as data become more reliably available and technologies become more intelligently embedded, the best practices of organizing and managing data analytics teams for procurement will need to be constantly updated.

scholarly journals Incorporating radiomics into clinical trials: expert consensus on considerations for data-driven compared to biologically driven quantitative biomarkers

2021 ◽  
Author(s):  
Laure Fournier ◽  
Lena Costaridou ◽  
Luc Bidaut ◽  
Nicolas Michoux ◽  
Frederic E. Lecouvet ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Quantitative Imaging ◽  
A Priori ◽  
External Validation ◽  
Data Driven ◽  
Imaging Biomarkers ◽  
Clinical Validation ◽  
Biological Processes ◽  
Imaging Data ◽  
Selection Of

Abstract Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. Key Points • Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the dataset compared to sample size, making adequate diversity of the data, cross-validation and external validation essential to mitigate the risks of spurious associations and overfitting. • Use of radiomic signatures within clinical trials requires multistep standardisation of image acquisition, image analysis and data mining processes. • Biological correlation may be established after clinical validation but is not mandatory.

scholarly journals WORCS: A workflow for open reproducible code in science

Data Science ◽  
2021 ◽  
pp. 1-21
Author(s):  
Caspar J. Van Lissa ◽  
Andreas M. Brandmaier ◽  
Loek Brinkman ◽  
Anna-Lena Lamprecht ◽  
Aaron Peikert ◽  
...  
Keyword(s):  
Best Practices ◽  
Source Code ◽  
R Package ◽  
Open Science ◽  
Research Projects ◽  
Tabular Data ◽  
Step Procedure ◽  
Starting Point ◽  
Conducting Research ◽  
And Training

Adopting open science principles can be challenging, requiring conceptual education and training in the use of new tools. This paper introduces the Workflow for Open Reproducible Code in Science (WORCS): A step-by-step procedure that researchers can follow to make a research project open and reproducible. This workflow intends to lower the threshold for adoption of open science principles. It is based on established best practices, and can be used either in parallel to, or in absence of, top-down requirements by journals, institutions, and funding bodies. To facilitate widespread adoption, the WORCS principles have been implemented in the R package worcs, which offers an RStudio project template and utility functions for specific workflow steps. This paper introduces the conceptual workflow, discusses how it meets different standards for open science, and addresses the functionality provided by the R implementation, worcs. This paper is primarily targeted towards scholars conducting research projects in R, conducting research that involves academic prose, analysis code, and tabular data. However, the workflow is flexible enough to accommodate other scenarios, and offers a starting point for customized solutions. The source code for the R package and manuscript, and a list of examplesof WORCS projects, are available at https://github.com/cjvanlissa/worcs.

scholarly journals Sensor, Signal, and Imaging Informatics

2017 ◽  
Vol 26 (01) ◽  
pp. 120-124
Author(s):  
W. Hsu ◽  
S. Park ◽  
Charles Kahn
Keyword(s):  
Medical Informatics ◽  
Scientific Work ◽  
Imaging Informatics ◽  
Sensor Signal ◽  
Imaging Data ◽  
Extensive Search ◽  
Challenges And Opportunities ◽  
Final Selection ◽  
Selection Of

Summary Objective: To summarize significant contributions to sensor, signal, and imaging informatics published in 2016. Methods: We conducted an extensive search using PubMed® and Web of Science® to identify the scientific contributions published in 2016 that addressed sensors, signals, and imaging in medical informatics. The three section editors selected 15 candidate best papers by consensus. Each candidate article was reviewed by the section editors and at least two other external reviewers. The final selection of the six best papers was conducted by the editorial board of the Yearbook. Results: The selected papers of 2016 demonstrate the important scientific advances in management and analysis of sensor, signal, and imaging information. Conclusion: The growing volume of signal and imaging data provides exciting new challenges and opportunities for research in medical informatics. Evolving technologies provide faster and more effective approaches for pattern recognition and diagnostic evaluation. The papers selected here offer a small glimpse of the high-quality scientific work published in 2016 in the domain of sensor, signal, and imaging informatics.

Telehealth music therapy for persons with dementia and/or caregivers

2021 ◽  
Vol 13 (3) ◽  
pp. 206-210
Author(s):  
Amy Clements-Cortés ◽  
Melissa Mercadal-Brotons ◽  
Tereza Raquel Alcântara Silva ◽  
Shirlene Vianna Moreira
Keyword(s):  
Older Adults ◽  
Social Interaction ◽  
Best Practices ◽  
Music Therapy ◽  
Music Therapists ◽  
Starting Point ◽  
And Training ◽  
Practical Recommendations

The COVID-19 pandemic has placed many restrictions on social interaction. Although these restrictions are challenging for everyone, they are particularly difficult for older adults who are often isolated. While telehealth has been around for a number of years, it had not been practiced to any great extent by music therapists until COVID-19. Telehealth will continue to prove valuable even when the pandemic ends, and as a result, it is timely to assess the benefits and recommendations for best practices. This paper provides a starting point of reflection for telehealth for persons with dementia, offering practical recommendations and implications for planning and training.

scholarly journals Radiomics in radiation oncology—basics, methods, and limitations

2020 ◽  
Vol 196 (10) ◽  
pp. 848-855
Author(s):  
Philipp Lohmann ◽  
Khaled Bousabarah ◽  
Mauritius Hoevels ◽  
Harald Treuer
Keyword(s):  
Mathematical Models ◽  
Quantitative Imaging ◽  
Human Perception ◽  
Image Features ◽  
Radiotherapy Planning ◽  
Large Field ◽  
Imaging Features ◽  
Imaging Data ◽  
Complete Evaluation ◽  
Vast Number

Abstract Over the past years, the quantity and complexity of imaging data available for the clinical management of patients with solid tumors has increased substantially. Without the support of methods from the field of artificial intelligence (AI) and machine learning, a complete evaluation of the available image information is hardly feasible in clinical routine. Especially in radiotherapy planning, manual detection and segmentation of lesions is laborious, time consuming, and shows significant variability among observers. Here, AI already offers techniques to support radiation oncologists, whereby ultimately, the productivity and the quality are increased, potentially leading to an improved patient outcome. Besides detection and segmentation of lesions, AI allows the extraction of a vast number of quantitative imaging features from structural or functional imaging data that are typically not accessible by means of human perception. These features can be used alone or in combination with other clinical parameters to generate mathematical models that allow, for example, prediction of the response to radiotherapy. Within the large field of AI, radiomics is the subdiscipline that deals with the extraction of quantitative image features as well as the generation of predictive or prognostic mathematical models. This review gives an overview of the basics, methods, and limitations of radiomics, with a focus on patients with brain tumors treated by radiation therapy.

scholarly journals Spatial regulation of clathrin-mediated endocytosis through position-dependent site maturation

2019 ◽  
Author(s):  
Ross TA Pedersen ◽  
Julian E Hassinger ◽  
Paul Marchando ◽  
David G Drubin
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Transition Point ◽  
Quantitative Imaging ◽  
Imaging Data ◽  
Cortical Actin ◽  
Initiation Phase ◽  
Spatial Regulation ◽  
Polarized Cells ◽  
Endocytosis Pathway ◽  
Dependent Site

AbstractDuring clathrin-mediated endocytosis (CME), over 50 different proteins assemble on the plasma membrane to reshape it into a cargo-laden vesicle. It has long been assumed that cargo triggers local CME site assembly in Saccharomyces cerevisiae based on the discovery that cortical actin patches clustered near exocytic sites are CME sites. Quantitative imaging data reported here lead to a radically different view of which CME steps are regulated and which steps are deterministic. We quantitatively and spatially describe progression through the CME pathway and pinpoint a cargo-sensitive regulatory transition point that governs progression from the initiation phase of CME to the internalization phase. Thus, site maturation, rather than site initiation, accounts for the previously observed polarized distribution of actin patches in this organism. While previous studies suggested that cargo ensures its own internalization by regulating either CME initiation rates or frequency of abortive events, our data instead identify maturation through a checkpoint in the pathway as the cargo-sensitive step.SummaryPedersen, Hassinger, et al. investigate steps of the clathrin-mediated endocytosis pathway that are subject to regulation. They report position-dependent differences in endocytic site maturation rates in polarized cells and suggest that cargo controls endocytic internalization through tuning site maturation rather than site initiation.

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