Program Information
Cross Validation of Knowledge-Based Treatment Planning for Prostate LDR Brachytherapy Using Principle Component Analysis
J Roper1*, B Ghavidel1 , V Chanyavanich2 , K Godette1 , E Schreibmann1 , (1) Winship Cancer Institute of Emory University, GA, (2) Rocky Mountain Cancer Centers, CO
Presentations
WE-DE-201-4 (Wednesday, August 3, 2016) 10:15 AM - 12:15 PM Room: 201
Purpose: To validate a knowledge-based algorithm for prostate LDR brachytherapy treatment planning.
Methods: A dataset of 100 cases was compiled from an active prostate seed implant service. Cases were randomized into 10 subsets. For each subset, the 90 remaining library cases were registered to a common reference frame and then characterized on a point by point basis using principle component analysis (PCA). Each test case was converted to PCA vectors using the same process and compared with each library case using a Mahalanobis distance to evaluate similarity. Rank order PCA scores were used to select the best-matched library case. The seed arrangement was extracted from the best-matched case and used as a starting point for planning the test case. Any subsequent modifications were recorded that required input from a treatment planner to achieve V100>95%, V150<60%, V200<20%. To simulate operating-room planning constraints, seed activity was held constant, and the seed count could not increase.
Results: The computational time required to register test-case contours and evaluate PCA similarity across the library was 10s. Preliminary analysis of 2 subsets shows that 9 of 20 test cases did not require any seed modifications to obtain an acceptable plan. Five test cases required fewer than 10 seed modifications or a grid shift. Another 5 test cases required approximately 20 seed modifications. An acceptable plan was not achieved for 1 outlier, which was substantially larger than its best match. Modifications took between 5s and 6min.
Conclusion: A knowledge-based treatment planning algorithm for prostate LDR brachytherapy is being cross validated using 100 prior cases. Preliminary results suggest that for this size library, acceptable plans can be achieved without planner input in about half of the cases while varying amounts of planner input are needed in remaining cases. Computational time and planning time are compatible with clinical practice.
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