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Development of An Automated Pre-Treatment Data-Transfer Evaluation

J Rembish*, N Bice, C Kabat, S Stathakis, N Papanikolaou, P Myers, UT Health San Antonio, San Antonio, TX

Presentations

(Sunday, 7/12/2020)   [Eastern Time (GMT-4)]

Room: AAPM ePoster Library

Purpose: To assure safe and high-quality treatments for radiotherapy patients, radiation oncology teams conduct pre-treatment plan evaluations which assess the machine treatment parameters, patient set-up instructions, prescriptions, and radiation dose distributions. The current process is performed manually, which consumes a great deal of time and introduces the risk of human errors (e.g., oversight of parameter discrepancies, failure to achieve optimal target coverage, unnecessary dose to surrounding healthy tissue). Automation of the pre-treatment evaluation process has the potential to reduce these errors and improve clinical efficiency.


Methods: Using Python, a script was created to automatically compare the data from the DICOM export, and the plan information stored in the Mosaiq SQL database. Various machine parameters, prescription details, and patient information are taken into consideration when cross-comparing between the two systems. The results are saved as a spreadsheet with a color-coded scheme to allow easy observation of any discrepancies. These scripts are publicly available through PyMedPhys.


Results: A data-transfer check can be performed between a plan’s DICOM files and Mosaiq’s database to confirm consistency between general patient information and beam specific information. The comparison can be performed in a matter of seconds, making it significantly less time demanding than a manual comparison.


Conclusion: The data transfer portion of a pre-treatment check can be automated and used to drastically reduce the amount of time required to complete the task while minimizing opportunity for human error. Implementing this into clinical workflow has the potential to increase efficiency while also increasing error detection sensitivity.

Download ePoster [PDF]

Keywords

Quality Assurance, Software

Taxonomy

Not Applicable / None Entered.

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