Data Commons to Support Pediatric Cancer Research

Author(s):  
Samuel L. Volchenboum ◽  
Suzanne M. Cox ◽  
Allison Heath ◽  
Adam Resnick ◽  
Susan L. Cohn ◽  
...  

The falling costs and increasing fidelity of high-throughput biomedical research data have led to a renaissance in cancer surveillance and treatment. Yet, the amount, velocity, and complexity of these data have overcome the capacity of the increasing number of researchers collecting and analyzing this information. By centralizing the data, processing power, and tools, there is a valuable opportunity to share resources and thus increase the efficiency, power, and impact of research. Herein, we describe current data commons and how they operate in the oncology landscape, including an overview of the International Neuroblastoma Risk Group data commons as a paradigm case. We outline the practical steps and considerations in building data commons. Finally, we discuss the unique opportunities and benefits of creating a data commons within the context of pediatric cancer research, highlighting the particular advantages for clinical oncology and suggested next steps.

2017 ◽  
Vol 37 ◽  
pp. 746-752 ◽  
Author(s):  
Samuel L. Volchenboum ◽  
Suzanne M. Cox ◽  
Allison Heath ◽  
Adam Resnick ◽  
Susan L. Cohn ◽  
...  

2020 ◽  
pp. 444-453 ◽  
Author(s):  
Andrey Fedorov ◽  
Reinhard Beichel ◽  
Jayashree Kalpathy-Cramer ◽  
David Clunie ◽  
Michael Onken ◽  
...  

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.


2011 ◽  
Vol 38 (1) ◽  
pp. 128-135 ◽  
Author(s):  
Diana Lam ◽  
Sandra L. Wootton-Gorges ◽  
John P. McGahan ◽  
Robin Stern ◽  
John M. Boone

2016 ◽  
Vol 18 (2) ◽  
pp. 165-168 ◽  
Author(s):  
Divya Sharma ◽  
Thomas Lee ◽  
Adam J. Friedman ◽  
Kelley Pagliai Redbord

2019 ◽  
Author(s):  
Reinder Broekstra ◽  
Els Maeckelberghe ◽  
Judith Aris-Meijer ◽  
Ronald Stolk ◽  
Sabine Otten

Abstract Background: Large-scale, centralized data repositories are playing a critical and unprecedented role in fostering innovative health research, leading to new opportunities as well as dilemmas for the medical sciences. Uncovering the reasons as to why citizens do or do not contribute to such repositories, for example, to population-based biobanks, is therefore crucial. We investigated and compared the views of existing participants and non-participants on contributing to large-scale, centralized health research data repositories with those of ex-participants regarding the decision to end their participation. This comparison could yield new insights into motives of participation and non-participation, in particular the behavioural change of withdrawal. Methods: We conducted 36 in-depth interviews with ex-participants, participants, and non-participants of a three-generation, population-based biobank in the Netherlands. The interviews focused on the respondents’ decision-making processes relating to their participation in a large-scale, centralized repository for health research data. Results: The decision of participants and non-participants to contribute to the biobank was motivated by a desire to help others. Whereas participants perceived only benefits relating to their participation and were unconcerned about potential risks, non­-participants and ex-participants raised concerns about the threat of large-scale, centralized public data repositories and public institutes, such as social exclusion or commercialization. Our analysis of ex-participants’ perceptions suggests that intrapersonal characteristics, such as levels of trust in society and public goods, participation conceived as a social norm, and basic societal values account for differences between participants and non-participants. Conclusions: Our findings indicate the fluidity of motives centring on helping others in decisions to participate in large-scale, centralized health research data repositories. Efforts to improve participation should focus on enhancing the trustworthiness of such data repositories and developing layered strategies for communication with participants and with the public. Accordingly, personalized approaches for recruiting participants and transmitting information along with appropriate regulatory frameworks are required, which have important implications for current data management and informed consent procedures.


2020 ◽  
Author(s):  
Reinder Broekstra ◽  
Els Maeckelberghe ◽  
Judith Aris-Meijer ◽  
Ronald Stolk ◽  
Sabine Otten

Abstract Background: Large-scale, centralized data repositories are playing a critical and unprecedented role in fostering innovative health research, leading to new opportunities as well as dilemmas for the medical sciences. Uncovering the reasons as to why citizens do or do not contribute to such repositories, for example, to population-based biobanks, is therefore crucial. We investigated and compared the views of existing participants and non-participants on contributing to large-scale, centralized health research data repositories with those of ex-participants regarding the decision to end their participation. This comparison could yield new insights into motives of participation and non-participation, in particular the behavioural change of withdrawal. Methods: We conducted 36 in-depth interviews with ex-participants, participants, and non-participants of a three-generation, population-based biobank in the Netherlands. The interviews focused on the respondents’ decision-making processes relating to their participation in a large-scale, centralized repository for health research data. Results: The decision of participants and non-participants to contribute to the biobank was motivated by a desire to help others. Whereas participants perceived only benefits relating to their participation and were unconcerned about potential risks, non­-participants and ex-participants raised concerns about the threat of large-scale, centralized public data repositories and public institutes, such as social exclusion or commercialization. Our analysis of ex-participants’ perceptions suggests that intrapersonal characteristics, such as levels of trust in society and public goods, participation conceived as a social norm, and basic societal values account for differences between participants and non-participants.Conclusions: Our findings indicate the fluidity of motives centring on helping others in decisions to participate in large-scale, centralized health research data repositories. Efforts to improve participation should focus on enhancing the trustworthiness of such data repositories and developing layered strategies for communication with participants and with the public. Accordingly, personalized approaches for recruiting participants and transmitting information along with appropriate regulatory frameworks are required, which have important implications for current data management and informed consent procedures.


1980 ◽  
Vol 19 (01) ◽  
pp. 16-22
Author(s):  
D. Komitowski ◽  
C. O. Köhler ◽  
D. Naumann ◽  
B. Lance

The information system of experimental oncopathology of the German Cancer Research Center is a computerized data processing program for studying the etiology, pathogenesis and therapy of experimental cancer. This program is adapted to correlate stored data with those from the thesaurus of human pathology. The system is developed and administered by the histodiagnostic facility which serves to collect and register standardized, centralized and current data from all sources. These are: individual investigators, animal laboratory, and central histodiagnostic facility. To record uniform data, a standardized protocol is introduced which entails data sets for information about animals, substances under study, experimental design, and necropsy and histological changes. The data entry takes place semi-automatically by using different codes grouped into three files: for substances, for animals, and for pathological changes. The code for pathological findings is based on SNOP. For data processing the system ALIS is employed which permits input, check and update; reorganisation and confirmation; evaluation.The information system is adapted to the organization and research programs of the German Cancer Research Center. It is a flexible system applicable for different conditions in registering and processing diverse information about animal experiments.


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