Five-year experience with setup and implementation of an integrated database system for clinical documentation and research

Kessel, Kerstin A.; Bøhn, Christian; Engelmann, Uwe; Oetzel, Dieter; Bougatf, Nina; Bendl, Rolf; Debus, Jürgen; Combs, Stephanie E.

doi:10.1016/j.cmpb.2014.02.002

Cited by 37 publications

(41 citation statements)

References 43 publications

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“…Other arguments might be the heterogeneity of data included, the molecular variability behind any tumor type is often not sub-classified in a simple clinical database, as well as the model used for calculating the score. Today, modern databases offer all clinical data including imaging, molecular data as well as all relevant clinical information [14]. Automatic generation of a prospective database-driven tool for decision making readily available for clinical routine seems a logical consequence offering the possibility to deliver an online scoring system for each tumor type; this information is then readily available for interdisciplinary decision making such as tumor boards.…”

Section: Discussionmentioning

confidence: 99%

Validation of an established prognostic score after re-irradiation of recurrent glioma

et al. 2017

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Section: Discussionmentioning

confidence: 99%

Validation of an established prognostic score after re-irradiation of recurrent glioma

et al. 2017

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“…Two groups of 77 patients, chosen from a large database [18], suffering from inoperable (even biopsy was not feasible; grade of meningioma is unknown), residual or recurrent meningioma, treated in two time periods with different radiation modalities, were analyzed and compared retrospectively. The performance of this study was approved by the local ethics committee (Ethikkommission Medizinische Fakult€ at Heidelberg).…”

Section: Methodsmentioning

confidence: 99%

Volumetric response of intracranial meningioma after photon or particle irradiation

et al. 2016

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Background: Meningiomas are usually slow growing, well circumscribed intracranial tumors. In symptom-free cases observation with close follow-up imaging could be performed. Symptomatic meningiomas could be surgically removed and/or treated with radiotherapy. The study aimed to evaluate the volumetric response of intracranial meningiomas at different time points after photon, proton, and a mixed photon and carbon ion boost irradiation. Patients and methods: In Group A 38 patients received proton therapy (median dose: 56 GyE in 1.8-2 GyE daily fractions) or a mixed photon/carbon ion therapy (50 Gy in 2 Gy daily fractions with intensity modulated radiotherapy (IMRT) and 18 GyE in 3 GyE daily dose carbon ion boost). Thirty-nine patients (Group B) were treated by photon therapy with IMRT or fractionated stereotactic radiotherapy technique (median dose: 56 Gy in 1.8-2 Gy daily fractions). The delineation of the tumor volume was based on the initial, one-and two-year follow-up magnetic resonance imaging and these volumes were compared to evaluate the volumetric tumor response. Results: Significant tumor volume shrinkage was detected at one-and at two-year follow-up both after irradiation by particles and by photons. No significant difference in tumor volume change was observed between photon, proton or combined photon plus carbon ion boost treated patients. WHO grade and gender appear to be determining factors for tumor volume shrinkage. Conclusion: Significant volumetric shrinkage of meningiomas could be observed independently of the applied radiation modality. Long-term follow-up is recommended to evaluate further dynamic of size reduction and its correlation with outcome data.

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“…Historically, the utilization of this data has been low but the future potential is well established 1. In the radiation oncology health space, the use of database systems has been reported for long (25 years)3 and short (5 years)4 periods, as well as for specific treatment sites 5. On a global level, the reported advantages ranged from informing treatment policy, resource allocation, clinical trials assessment, enhancing research and the publication of peer‐reviewed papers 3, 4, 5.…”

Section: Introductionmentioning

confidence: 99%

“…In the radiation oncology health space, the use of database systems has been reported for long (25 years)3 and short (5 years)4 periods, as well as for specific treatment sites 5. On a global level, the reported advantages ranged from informing treatment policy, resource allocation, clinical trials assessment, enhancing research and the publication of peer‐reviewed papers 3, 4, 5. For optimal outcomes, these publications also allude to the need for these database tools to be automated and streamlined in the collection, analysis, and presentation of large amounts of data.…”

Section: Introductionmentioning

confidence: 99%

Automated data mining of a plan‐check database and example application

Dunn¹,

Jolly²

2018

J Applied Clin Med Phys

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PurposeThe aim of this work was to present the development and example application of an automated data mining software platform that preforms bulk analysis of results and patient data passing through the 3D plan and delivery QA system, Mobius3D.MethodsPython, matlab, and Java were used to create an interface that reads JavaScript Object Notation (JSON) created for every approved Mobius3D pre‐treatment plan‐check. The aforementioned JSON files contain all the information for every pre‐treatment QA check performed by Mobius3D, including all 3D dose, CT, structure set information, as well as all plan information and patient demographics. Two Graphical User Interfaces (GUIs) were created, the first is called Mobius3D‐Database (M3D‐DB) and presents the check results in both filterable tabular and graphical form. These data are presented for all patients and includes mean dose differences, 90% coverage, 3D gamma pass rate percentages, treatment sites, machine, beam energy, Multi‐Leaf Collimator (MLC) mode, treatment planning system (TPS), plan names, approvers, dates and times. Group statistics and statistical process control levels are then calculated based on filter settings. The second GUI, called Mobius3D organ at risk (M3DOAR), analyzes dose‐volume histogram data for all patients and all Organs‐at‐Risk (OAR). The design of the software is such that all treatment parameters and treatment site information are able to be filtered and sorted with the results, plots, and statistics updated.ResultsThe M3D‐DB software can summarize and filter large numbers of plan‐checks from Mobius3D. The M3DOAR software is also able to analyze large amounts of dose‐volume data for patient groups which may prove useful in clinical trials, where OAR doses for large numbers of patients can be compared and correlated. Target DVHs can also be analyzed en mass.ConclusionsThis work demonstrates a method to extract the large amount of treatment data for every patient that is stored by Mobius3D but not easily accessible. With scripting, it is possible to mine this data for research and clinical trials as well as patient and TPS QA.

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Five-year experience with setup and implementation of an integrated database system for clinical documentation and research

Cited by 37 publications

References 43 publications

Validation of an established prognostic score after re-irradiation of recurrent glioma

Validation of an established prognostic score after re-irradiation of recurrent glioma

Volumetric response of intracranial meningioma after photon or particle irradiation

Automated data mining of a plan‐check database and example application

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