International data-sharing for radiotherapy research: An open-source based infrastructure for multicentric clinical data mining

Roelofs, Erik; Dekker, André; Meldolesi, Elisa; Stiphout, Ruud G.P.M. van; Valentini, Vincenzo

doi:10.1016/j.radonc.2013.11.001

Cited by 67 publications

(52 citation statements)

References 26 publications

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“…An example of data sharing infrastructure based on a data warehousing architecture was implemented between the Policlinico Universitario Agostino Gemelli in Rome, Italy (Gemelli) and the MAASTRO Clinic in Maastricht, The Netherlands in mid 2010 [35]. This infrastructure (Figure 4) has been used to share rectal cancer data by pooling two existing datasets from both institutes in order to facilitate research projects such as the Thunder clinical trial [36] and 'knowledge engineering' research [37,38].…”

Section: Sharing Data: Data Warehouse and Semantic Web Approachesmentioning

confidence: 99%

VATE: VAlidation of high TEchnology based on large database analysis by learning machine

Meldolesi

Soest²,

Alitto

et al. 2014

Colorect. Cancer

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SummaryThe interaction between implementation of new technologies and different outcomes can allow a broad range of researches to be expanded. The purpose of this paper is to introduce the VAlidation of high TEchnology based on large database analysis by learning machine (VATE) project that aims to combine new technologies with outcomes related to rectal cancer in terms of tumor control and normal tissue sparing. Using automated computer bots and the knowledge for screening data it is possible to identify the factors that can mostly influence those outcomes. Population-based observational studies resulting from the linkage of different datasets will be conducted in order to develop predictive models that allow physicians to share decision with patients into a wider concept of tailored treatment. KeywordSOver the past decade, remarkable advances in cancer care with the adoption of newest diagnostic and treatment technologies has created new challenges [1].The use and role of medical imaging technologies in clinical oncology has greatly expanded from a primarily diagnostic tool to award a central role in the context of individualized medicine. Multiple imaging features involving descriptors of intensity distribution, spatial relationships between the various intensity levels, texture heterogeneity patterns, descriptors of shape and the relations of the tumor with the surrounding tissues have been analyzed for their relationship with treatment outcomes or Practice points• Progress in individualized medicine has created new challenges.• New technology implementation.• Necessity to develop systems that allow shared decision making by the physicians and the patients and chose a tailored treatment.• Standardization of the data collection -ontology.• Sharing data: Semantic Web and Resource Description Framework.• Statistical analysis.• Privacy protection of individual patients.• Development of predictive models based on individual patients features which complement existing consensus or guidelines.For reprint orders, please contact: reprints@futuremedicine.com

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Section: Sharing Data: Data Warehouse and Semantic Web Approachesmentioning

confidence: 99%

VATE: VAlidation of high TEchnology based on large database analysis by learning machine

Meldolesi

Soest²,

Alitto

et al. 2014

Colorect. Cancer

Self Cite

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“…Open-source-based infrastructure to facilitate data sharing between multiple centres is becoming available. 16 A similar initiative is underway in North America under the auspices of the American Society for Therapeutic Radiology and Oncology to develop a National Radiation Oncology Registry 17 with the goals of providing real-time comparative effectiveness data and circumventing the financial perils of technological introduction without thorough assessment. 18 Another approach to gathering evidence sees the interim funding of new medical technologies for treating patients on clinical trials to generate the additional benefit and safety information needed to make an informed general-coverage decision.…”

Section: Discussionmentioning

confidence: 99%

Trans Tasman Radiation Oncology Group: Development of the Assessment of New Radiation Oncology Technology and Treatments (ANROTAT) Framework

et al. 2014

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Introduction: The study aim was to develop a generic framework to derive the parameters to populate health-economic models for the rapid evaluation of new techniques and technologies in radiation oncology. Methods: A draft framework was developed through horizon scanning for relevant technologies, literature review to identify framework models, and a workshop program with radiation oncology professionals, biostatisticians, health economists and consumers to establish the Framework's structure. It was tested using four clinical protocols, comparing intensity modulated with 3D conformal therapy (post-prostatectomy, anal canal and nasopharynx) and image-guided radiation therapy techniques with off-line review of portal imaging (in the intact prostate). Results: The draft generic research framework consisted of five sequential stages, each with a number of components, and was assessed as to its suitability for deriving the evidence needed to populate the decision-analytic models required for the health-economic evaluations. A final Framework was established from this experience for use by future researchers to provide evidence of clinical efficacy and cost-utility for other novel techniques. The four clinical treatment sites tested during the project were considered suitable to use in future evaluations. Conclusions: Development of a generic research framework to predict early and long-term clinical outcomes, combined with health-economic data, produced a generally applicable method for the rapid evaluation of new techniques and technologies in radiation oncology. Its application to further health technology assessments in the radiation oncology sector will allow further refinement and support its generalisability.

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“…The Cancer Imaging Archive TCIA REST, download [24] CancerData.org -Sharing data for cancer research CancerData download [45] Mouse Tumor Biology -Database MTB download [44] National Biomedical Imaging Archive NBIA REST, download [24] Many Table 2 lists all examined databases providing cancer-related content as download that is free for non-commercial, scientific purposes, sorted by category.…”

Section: Imaging Datamentioning

confidence: 99%

Integrating Open Data on Cancer in Support to Tumor Growth Analysis

Jeanquartier

Jean-Quartier

Schreck

et al. 2016

Information Technology in Bio- And Medical Informatics

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Abstract. The general disease group of malignant neoplasms depicts one of the leading and increasing causes for death. The underlying complexity of cancer demands for abstractions to disclose an exclusive subset of information related to the disease. Our idea is to create a user interface for linking a simulation on cancer modeling to relevant additional publicly and freely available data. We are not only providing a categorized list of open datasets and queryable databases for the different types of cancer and related information, we also identify a certain subset of temporal and spatial data related to tumor growth. Furthermore, we describe the integration possibilities into a simulation tool on tumor growth that incorporates the tumor's kinetics.

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International data-sharing for radiotherapy research: An open-source based infrastructure for multicentric clinical data mining

Cited by 67 publications

References 26 publications

VATE: VAlidation of high TEchnology based on large database analysis by learning machine

VATE: VAlidation of high TEchnology based on large database analysis by learning machine

Trans Tasman Radiation Oncology Group: Development of the Assessment of New Radiation Oncology Technology and Treatments (ANROTAT) Framework

Integrating Open Data on Cancer in Support to Tumor Growth Analysis

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