Program Information
Knowledge Based DVH Prediction Using a Geometric Dose Transform
D Staub*, J Wang , S Jiang , UT Southwestern Medical Center, Dallas, TX
Presentations
MO-G-304-2 (Monday, July 13, 2015) 5:15 PM - 6:00 PM Room: 304
Purpose: To demonstrate a novel method for predicting patient dose-volume histograms (DVHs) using a prior database of optimized radiotherapy treatment plans. Such predicted DVHs could be useful for automating treatment planning.
Methods: Our initial demonstration utilized a database of 100 prostate intensity-modulated radiotherapy (IMRT) data-sets. Each data-set contained a CT image with contours of the planning target volume (PTV), rectum, and bladder, the parameters of a clinically approved IMRT plan, and a corresponding simulated dose distribution. We applied a novel geometric transformation to remove the influence of the PTV size, shape, and location on the dose distribution. We termed the transformed distribution the geometrically normalized dose distribution (GNDD). This normalization transform was applied to 80 data-sets randomly selected from the database, and a population GNDD was computed as the average. Next, the population GNDD was mapped onto each of the remaining 20 patient datasets using the reverse of the geometric normalization transform, and predicted DVHs were calculated from the reverse transformed dose distributions (GNDD-DVHs). In addition, a state of the art machine learning based method from the literature was tested for comparison.
Results: DVH prediction accuracy was quantified by calculating the relative root mean squared error (rRMSE) on predicted DVHs for the 20 test patients using their known DVHs. For bladder, rectum, and PTV average rRMSEs for the GNDD method were 9.7 ± 4.2%, 13.9 ± 6.0%, and 2.3 ± 0.5% respectively. Prediction results using GNDD were roughly equivalent to that from the machine learning method.
Conclusion: We developed a new method for predicting DVH curves from a database of prior patient plans. We demonstrated that our simple approach achieves accuracy comparable to a method using a complicated machine learning based approach.
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