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A Graphical User Interface (GUI) Toolkit for Treatment Plan Quality Analysis in Right Lung SBRT

A Brito Delgado1*, K Rasmussen2 , K Kauweloa3 , T Medrano Pesqueira4 , D Cohen5 , T Eng6 , N Kirby7 , D Saenz8 , Z Shi9 , S Stathakis10 , N Papanikolaou11 , A Gutierrez12 , (1) University of Kansas Hospital, Overland Park, KS, (2) University of Texas HSC SA, San Antonio, TX, (3) University of Kansas Medical Center, Overland Park, KS, (4) Centro Estatal de Oncologia, Hermosillo, Sonora, Mexico ,(5) Jefferson Health New Jersey, Sewell, ,(6) Emory University, Atlanta, ,(7) University of Texas HSC SA, San Antonio, TX, (8) University of Texas HSC SA, San Antonio, TX, (9) University of Texas HSC SA, San Antonio, TX, (10) University Of Texas Health, San Antonio, TX, (11) University of Texas HSC SA, San Antonio, TX, (12) Miami Cancer Institute, Miami, FL


(Sunday, 7/14/2019) 1:00 PM - 2:00 PM

Room: Stars at Night Ballroom 4

Purpose: A MATLAB graphical user interphase (GUI) toolkit was developed for Analytical Hierarchy Process (AHP) plan quality analysis of right lung Stereotactic Body Radiation Therapy (SBRT) treatment plans. This work presents the first automation of the AHP technique for plan quality analysis of right lung SBRT treatment plans.

Methods: The toolkit includes three GUIs: a survey GUI, a Dose Volume Histogram (DVH) GUI and an AHP GUI. First, the user must answer the lung SBRT survey. Then each plan alternative must be exported from the treatment planning system into the DVH GUI to extract the information relevant to our AHP process. Finally, both plan alternatives and the user’s survey responses are loaded into the AHP GUI, which will compute the alternative rankings. This toolkit was built using MATLAB GUIs in order to provide a user-friendly interface. The graphical user interface (GUI) was created using the MATLAB 2016b software (Mathworks, Natick, MA).

Results: The AHP GUI builds a 2x2 matrix with the plan alternatives’ scores for each parameter used for plan quality analysis. The matrices are then processed as per the AHP computation method. Resulting from this computation method each plan is assigned a ranking for each plan parameter. These plan alternative rankings are input along with the user’s survey results in order to obtain a final plan score for each plan alternative. The same process is repeated for to obtain the final score for Plan 2.

Conclusion: The MATLAB GUIs show that AHP can be successfully applied and automated for treatment plan quality analysis. This toolkit can help streamline and make clinic processes more efficient by decreasing the time of interaction between radiation oncologist and dosimetrist necessary to find the best plan alternative.


DICOM-RT, Computer Software, Quality Control


TH- Dataset analysis/biomathematics: Machine learning techniques

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