ArticleJournal of applied clinical medical physics2026
Streamlined data extraction and visualization utilizing third-party verification software for online adaptive radiation therapy.
Article in Journal of applied clinical medical physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The trial behind it
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Who cites it
1 citing paper in PubMed.
- Streamlined data extraction and visualization utilizing third-party verification software for online adaptive radiation therapy.Journal of applied clinical medical physics · 2026Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeOnline adaptive radiation therapy (oART) has generated a significant influx of daily treatment planning data. Present oART platforms have leveraged automation for decreased planning times at the expense of end-user access, making longitudinal oART data assessment time consuming. We have developed modified software that extracts oART data from the secondary calculation software associated with one of the oART platforms.
methodsA Python program was developed to query oART data from a web-based secondary dose calculation software. Structure, plan, and dosimetric data were collected for all generated oART plans over the course of 3 years. This software stores data in a Pandas dataframe to allow for comparisons and advanced visualization. The software includes some pre-defined visualization and analysis methods, as well as tools to de-identify and share cohorts of oART data.
resultsTwelve hundred oART sessions were pulled from our secondary dose verification software. Various visualization methods were implemented and are demonstrated in this technical note. This software demonstrated significant time savings in data extraction compared to manual session exports, but does not pull raw DICOM information, such as CT scans, structure sets, or dose files.
conclusionOur extraction software presents a streamlined way to collect, analyze, and visualize oART data using a dedicated oART platform. Further tools are needed to improve access and analysis of oART data.
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