ArticleJournal of applied clinical medical physics2018
Automated data mining of a plan-check database and example application.
Article in Journal of applied clinical medical physics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Assessing the sensitivity and suitability of a range of detectors for SIMT PSQA.Journal of applied clinical medical physics · 2024Article
- Treatment of Cancer Gene Changes in Chronic Myeloid Leukemia by Big Data Analysis Platform-Based Dasatinib.Applied bionics and biomechanics · 2022Article
- Use of Receiver Operating Curve Analysis and Machine Learning With an Independent Dose Calculation System Reduces the Number of Physical Dose Measurements Required for Patient-Specific Quality Assurance.International journal of radiation oncology, biology, physics · 2021Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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