Integrating Multimodal Radiation Therapy Data into i2b2

@article{Zapletal2018IntegratingMR,
  title={Integrating Multimodal Radiation Therapy Data into i2b2},
  author={Eric Zapletal and Jean-Emmanuel Bibault and Philippe Giraud and Anita Burgun-Parenthoine},
  journal={Applied Clinical Informatics},
  year={2018},
  volume={9},
  pages={377 - 390}
}
Background  Clinical data warehouses are now widely used to foster clinical and translational research and the Informatics for Integrating Biology and the Bedside (i2b2) platform has become a de facto standard for storing clinical data in many projects. However, to design predictive models and assist in personalized treatment planning in cancer or radiation oncology, all available patient data need to be integrated into i2b2, including radiation therapy data that are currently not addressed in… 

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References

SHOWING 1-10 OF 33 REFERENCES
Labeling for Big Data in radiation oncology: The Radiation Oncology Structures ontology
TLDR
A new ontology, specific to radiation oncology, is described, that will be used to integrate dosimetric data in the Assistance Publique—Hôpitaux de Paris CDW that stores data from 6.5 million patients.
The ONCO-I2b2 Project: Integrating Biobank Information and Clinical Data to Support Translational Research in Oncology
TLDR
OnCO-i2b2, funded by the Lombardia region, grounds on the software developed by the Informatics for Integrating Biology and the Bedside (i 2b2) NIH project, and new software modules purposely designed, data coming from multiple sources are integrated and jointly queried.
Standardized data collection to build prediction models in oncology: a prototype for rectal cancer.
TLDR
The strategy to collect and analyze data properly for decision support is described and the concept of an 'umbrella protocol' within the framework of 'rapid learning healthcare' is introduced.
Combining clinical and genomics queries using i2b2 – Three methods
TLDR
This work investigated the capability for complex, integrative genomic and clinical queries to be supported in the Informatics for Integrating Biology and the Bedside (i2b2) translational software package and developed three different data integration approaches.
Evaluating the informatics for integrating biology and the bedside system for clinical research
TLDR
The i2b2 hive was found to be a useful cohort-selection tool for fulfilling common types of requests for research data, and especially in the estimation of initial cohort sizes.
High Throughput Tools to Access Images from Clinical Archives for Research
TLDR
A new Medical Imaging Informatics Bench to Bedside (mi2b2) module is described, available now as an open source addition to the i 2b2 software platform that allows medical imaging examinations collected during routine clinical care to be made available to translational investigators directly from their institution's clinical PACS for research and educational use in compliance with the Health Insurance Portability and Accountability Act (HIPAA) Omnibus Rule.
Integrating Heterogeneous Biomedical Data for Cancer Research: the CARPEM infrastructure
TLDR
This article identifies a set of scientific and technical principles needed to build a translational research platform compatible with ethical requirements, data protection and data-integration problems, and describes the solution adopted by the CARPEM cancer research program.
BigQ: a NoSQL based framework to handle genomic variants in i2b2
TLDR
BigQ, an extension of the i2b2 framework, which integrates patient clinical phenotypes with genomic variant profiles generated by Next Generation Sequencing, is developed and an evaluation of the query performance of the system is reported, showing that the implemented solution scales linearly in terms of query time and disk space with the number of variants.
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