Literature DB >> 27102842

A national approach for automated collection of standardized and population-based radiation therapy data in Sweden.

Tufve Nyholm1, Caroline Olsson2, Måns Agrup3, Peter Björk4, Thomas Björk-Eriksson5, Giovanna Gagliardi6, Hanne Grinaker7, Adalsteinn Gunnlaugsson8, Anders Gustafsson9, Magnus Gustafsson10, Bengt Johansson11, Stefan Johnsson12, Magnus Karlsson13, Ingrid Kristensen8, Per Nilsson8, Leif Nyström13, Eva Onjukka6, Johan Reizenstein11, Johan Skönevik13, Karin Söderström13, Alexander Valdman14, Björn Zackrisson13, Anders Montelius15.   

Abstract

PURPOSE: To develop an infrastructure for structured and automated collection of interoperable radiation therapy (RT) data into a national clinical quality registry.
MATERIALS AND METHODS: The present study was initiated in 2012 with the participation of seven of the 15 hospital departments delivering RT in Sweden. A national RT nomenclature and a database for structured unified storage of RT data at each site (Medical Information Quality Archive, MIQA) have been developed. Aggregated data from the MIQA databases are sent to a national RT registry located on the same IT platform (INCA) as the national clinical cancer registries.
RESULTS: The suggested naming convention has to date been integrated into the clinical workflow at 12 of 15 sites, and MIQA is installed at six of these. Involvement of the remaining 3/15 RT departments is ongoing, and they are expected to be part of the infrastructure by 2016. RT data collection from ARIA®, Mosaiq®, Eclipse™, and Oncentra® is supported. Manual curation of RT-structure information is needed for approximately 10% of target volumes, but rarely for normal tissue structures, demonstrating a good compliance to the RT nomenclature. Aggregated dose/volume descriptors are calculated based on the information in MIQA and sent to INCA using a dedicated service (MIQA2INCA). Correct linkage of data for each patient to the clinical cancer registries on the INCA platform is assured by the unique Swedish personal identity number.
CONCLUSIONS: An infrastructure for structured and automated prospective collection of syntactically interoperable RT data into a national clinical quality registry for RT data is under implementation. Future developments include adapting MIQA to other treatment modalities (e.g. proton therapy and brachytherapy) and finding strategies to harmonize structure delineations. How the RT registry should comply with domain-specific ontologies such as the Radiation Oncology Ontology (ROO) is under discussion.
Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  Data integration; Data standardization; Medical informatics; Radiation Oncology informatics; Radiation therapy data

Mesh:

Year:  2016        PMID: 27102842     DOI: 10.1016/j.radonc.2016.04.007

Source DB:  PubMed          Journal:  Radiother Oncol        ISSN: 0167-8140            Impact factor:   6.280


  7 in total

1.  Treatment data and technical process challenges for practical big data efforts in radiation oncology.

Authors:  C S Mayo; M Phillips; T R McNutt; J Palta; A Dekker; R C Miller; Y Xiao; J M Moran; M M Matuszak; P Gabriel; A S Ayan; J Prisciandaro; M Thor; N Dixit; R Popple; J Killoran; E Kaleba; M Kantor; D Ruan; R Kapoor; M L Kessler; T S Lawrence
Journal:  Med Phys       Date:  2018-09-18       Impact factor: 4.071

Review 2.  The big data effort in radiation oncology: Data mining or data farming?

Authors:  Charles S Mayo; Marc L Kessler; Avraham Eisbruch; Grant Weyburne; Mary Feng; James A Hayman; Shruti Jolly; Issam El Naqa; Jean M Moran; Martha M Matuszak; Carlos J Anderson; Lynn P Holevinski; Daniel L McShan; Sue M Merkel; Sherry L Machnak; Theodore S Lawrence; Randall K Ten Haken
Journal:  Adv Radiat Oncol       Date:  2016-10-13

3.  Automated customized retrieval of radiotherapy data for clinical trials, audit and research.

Authors:  Marina Romanchikova; Karl Harrison; Neil G Burnet; Andrew Cf Hoole; Michael Pf Sutcliffe; Michael Andrew Parker; Rajesh Jena; Simon James Thomas
Journal:  Br J Radiol       Date:  2018-01-31       Impact factor: 3.039

Review 4.  Cancer risk assessment in modern radiotherapy workflow with medical big data.

Authors:  Fu Jin; Huan-Li Luo; Juan Zhou; Ya-Nan He; Xian-Feng Liu; Ming-Song Zhong; Han Yang; Chao Li; Qi-Cheng Li; Xia Huang; Xiu-Mei Tian; Da Qiu; Guang-Lei He; Li Yin; Ying Wang
Journal:  Cancer Manag Res       Date:  2018-06-22       Impact factor: 3.989

5.  Initial experience with introducing national guidelines for CT- and MRI-based delineation of organs at risk in radiotherapy.

Authors:  Caroline Olsson; Tufve Nyholm; Elinore Wieslander; Eva Onjukka; Adalsteinn Gunnlaugsson; Johan Reizenstein; Stefan Johnsson; Ingrid Kristensen; Johan Skönevik; Magnus Karlsson; Ulf Isacsson; Anna Flejmer; Edvard Abel; Fredrik Nordström; Leif Nyström; Kjell Bergfeldt; Björn Zackrisson; Alexander Valdman
Journal:  Phys Imaging Radiat Oncol       Date:  2019-09-23

6.  Deep learning-based classification and structure name standardization for organ at risk and target delineations in prostate cancer radiotherapy.

Authors:  Christian Jamtheim Gustafsson; Michael Lempart; Johan Swärd; Emilia Persson; Tufve Nyholm; Camilla Thellenberg Karlsson; Jonas Scherman
Journal:  J Appl Clin Med Phys       Date:  2021-10-08       Impact factor: 2.102

7.  Transparency in quality of radiotherapy for breast cancer in the Netherlands: a national registration of radiotherapy-parameters.

Authors:  Nansi Maliko; Marcel R Stam; Liesbeth J Boersma; Marie-Jeanne T F D Vrancken Peeters; Michel W J M Wouters; Eline KleinJan; Maurice Mulder; Marion Essers; Coen W Hurkmans; Nina Bijker
Journal:  Radiat Oncol       Date:  2022-04-12       Impact factor: 3.481

  7 in total

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