SU-E-T-235: Data Mining for Evaluating Treatment Performances Over a Large Quantity of Data to Monitor and Improve SBRT Workflow

Authors

  • Feng W,

    1. Columbia University Medical Center, New York, NY
    2. New York Presbyterian Hospital, New York, AA
    3. Columbia University, New York, NY
    4. Bayhealth Medical Center, Dover, DE
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  • Chu A,

    1. Columbia University Medical Center, New York, NY
    2. New York Presbyterian Hospital, New York, AA
    3. Columbia University, New York, NY
    4. Bayhealth Medical Center, Dover, DE
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  • Wuu C,

    1. Columbia University Medical Center, New York, NY
    2. New York Presbyterian Hospital, New York, AA
    3. Columbia University, New York, NY
    4. Bayhealth Medical Center, Dover, DE
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  • Nguyen K

    1. Columbia University Medical Center, New York, NY
    2. New York Presbyterian Hospital, New York, AA
    3. Columbia University, New York, NY
    4. Bayhealth Medical Center, Dover, DE
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Abstract

Purpose:

To quality assure a large quantity of retrospective treatment cases for treatment performances by randomly sampling is inefficient. Here we provide a method to efficiently monitor and investigate the QA of SBRT workflow over Mosaiq.

Methods:

The code developed with Microsoft SQL Server Management Studio 2008R2 and VBA was used for retrieving and sorting data from Mosaiq (version 2.3–2.6 during 2012–2015). SBRT patients were filtered by fractional dose over 350cGy and total fraction number less than 6, which SBRT prescriptions were defined. The quality assurance on the SBRT workflow was focused on the treatment deliveries such as patient positioning setup, CBCT indicated offsets and couch shifted corrections. The treatment delivery were done by Varian Truebeam systems and the record/verify by Mosaiq.

Results:

Total 82 SBRT patients corresponding to 103 courses and 854 CBCT images were found by the retrieval query. Most centers record daily pre-treatment (Pre-Tx: before treatment shift) image-guided shifts along treatment course for inter-fraction motion record, and it is useful to also verify it with post-treatment imaging (Post-Tx: after treatment CBCT verification) to verify intra-fraction motion. Analyzing the details of daily recorded shifts can reveals the information of patient-setup and staff's record/verify behaviors. 3 examples were provided as solid evidences and on-going rectification for preventing future mistakes.

Conclusions:

The report gave feasible examples for inspector to verify a large amount of data during site investigation. This program can also be extended to a scheduled data mining with software to periodical analyze the timely records in Mosaiq, for example, a various control charts for different QA purposes. As the current trend of automation in radiation therapy field, the data mining would be a necessary tool in the future, just as the automatic plan quality evaluation has been under development in Eclipse.

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