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. 2016 Aug;120(2):349-55.
doi: 10.1016/j.radonc.2016.06.010. Epub 2016 Jul 6.

Evaluating inter-campus plan consistency using a knowledge based planning model

Sean L Berry  1 , Rongtao Ma  2 , Amanda Boczkowski  2 , Andrew Jackson  2 , Pengpeng Zhang  2 , Margie Hunt  2
Affiliations

Evaluating inter-campus plan consistency using a knowledge based planning model

Sean L Berry et al. Radiother Oncol. 2016 Aug.

Abstract

Background and purpose: We investigate whether knowledge based planning (KBP) can identify systematic variations in intensity modulated radiotherapy (IMRT) plans between multiple campuses of a single institution.

Material and methods: A KBP model was constructed from 58 prior main campus (MC) esophagus IMRT radiotherapy plans and then applied to 172 previous patient plans across MC and 4 regional sites (RS). The KBP model predicts DVH bands for each organ at risk which were compared to the previously planned DVHs for that patient.

Results: RS1's plans were the least similar to the model with less heart and stomach sparing, and more variation in liver dose, compared to MC. RS2 produced plans most similar to those expected from the model. RS3 plans displayed more variability from the model prediction but overall, the DVHs were no worse than those of MC. RS4 did not present any statistically significant results due to the small sample size (n=11).

Conclusions: KBP can retrospectively highlight subtle differences in planning practices, even between campuses of the same institution. This information can be used to identify areas needing increased consistency in planning output and subsequently improve consistency and quality of care.

Keywords: IMRT; Knowledge based planning; Quality assurance; Radiation therapy.

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Conflict of interest statement

CONFLICT OF INTEREST STATEMENTA license for RapidPlan® KBP software was provided by Varian Medical Systems (VMS) to the authors as a part of a software evaluation agreement between Memorial Sloan Kettering Cancer Center and VMS. Sean Berry, Margie Hunt, and Pengpeng Zhang hold research grants from VMS unrelated to this work.

Figures

Figure 1
Figure 1
Typical values of P (percentage of bins of the planned DVH that exceeded the predicted bounds), F (percentage of bins exceeding the upper bound relative to the total number of bins exceeding either bound), mRSR (modified restricted sum of residuals), and gEUD (shown in all panels of this example: heart, a = 1.6) for different relationships between the planned DVH (solid line) and predicted DVH bounds (dashed lines).
Figure 2
Figure 2
The clinical planned DVH (solid black line), predicted DVH bounds (dashed lines), and replanned DVH (solid gray line) for patient 291 at RS2 where the DVH metrics were noticeably high. Re-planning resulted in DVH’s much closer to their estimates.
See this image and copyright information in PMC

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