Journal of Contemporary Brachytherapy (Aug 2016)

ENT COBRA (Consortium for Brachytherapy Data Analysis): interdisciplinary standardized data collection system for head and neck patients treated with interventional radiotherapy (brachytherapy)

  • Luca Tagliaferri,
  • György Kovács,
  • Rosa Autorino,
  • Ashwini Budrukkar,
  • Jose Luis Guinot,
  • Guido Hildebrand,
  • Bengt Johansson,
  • Rafael Martìnez Monge,
  • Jens E. Meyer,
  • Peter Niehoff,
  • Angeles Rovirosa,
  • Zoltàn Takàcsi-Nagy,
  • Nicola Dinapoli,
  • Vito Lanzotti,
  • Andrea Damiani,
  • Tamer Soror,
  • Vincenzo Valentini

DOI
https://doi.org/10.5114/jcb.2016.61958
Journal volume & issue
Vol. 8, no. 4
pp. 336 – 343

Abstract

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Purpose : Aim of the COBRA (Consortium for Brachytherapy Data Analysis) project is to create a multicenter group (consortium) and a web-based system for standardized data collection. Material and methods: GEC-ESTRO (Groupe Européen de Curiethérapie – European Society for Radiotherapy & Oncology) Head and Neck (H&N) Working Group participated in the project and in the implementation of the consortium agreement, the ontology (data-set) and the necessary COBRA software services as well as the peer reviewing of the general anatomic site-specific COBRA protocol. The ontology was defined by a multicenter task-group. Results : Eleven centers from 6 countries signed an agreement and the consortium approved the ontology. We identified 3 tiers for the data set: Registry (epidemiology analysis), Procedures (prediction models and DSS), and Research (radiomics). The COBRA-Storage System (C-SS) is not time-consuming as, thanks to the use of “brokers”, data can be extracted directly from the single center’s storage systems through a connection with “structured query language database” (SQL-DB), Microsoft Access®, FileMaker Pro®, or Microsoft Excel®. The system is also structured to perform automatic archiving directly from the treatment planning system or afterloading machine. The architecture is based on the concept of “on-purpose data projection”. The C-SS architecture is privacy protecting because it will never make visible data that could identify an individual patient. This C-SS can also benefit from the so called “distributed learning” approaches, in which data never leave the collecting institution, while learning algorithms and proposed predictive models are commonly shared. Conclusions : Setting up a consortium is a feasible and practicable tool in the creation of an international and multi-system data sharing system. COBRA C-SS seems to be well accepted by all involved parties, primarily because it does not influence the center’s own data storing technologies, procedures, and habits. Furthermore, the method preserves the privacy of all patients.

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