SU-E-T-572: A Plan Quality Metric for Evaluating Knowledge-Based Treatment Plans.

Published

Conference Paper

PURPOSE: In prostate IMRT treatment planning, the variation in patient anatomy makes it difficult to estimate a priori the potentially achievable extent of dose reduction possible to the rectum and bladder. We developed a mutual information-based framework to estimate the achievable plan quality for a new patient, prior to any treatment planning or optimization. METHODS: The knowledge-base consists of 250 retrospective prostate IMRT plans. Using these prior plans, twenty query cases were each matched with five cases from the database. We propose a simple DVH plan quality metric (PQ) based on the weighted-sum of the areas under the curve (AUC) of the PTV, rectum and bladder. We evaluate the plan quality of knowledge-based generated plans, and established a correlation between the plan quality and case similarity. RESULTS: The introduced plan quality metric correlates well (r2 = 0.8) with the mutual similarity between cases. A matched case with high anatomical similarity can be used to produce a new high quality plan. Not surprisingly, a poorly matched case with low degree of anatomical similarity tends to produce a low quality plan, since the adapted fluences from a dissimilar case cannot be modified sufficiently to yield acceptable PTV coverage. CONCLUSIONS: The plan quality metric is well-correlated to the degree of anatomical similarity between a new query case and matched cases. Further work will investigate how to apply this metric to further stratify and select cases for knowledge-based planning.

Full Text

Duke Authors

Cited Authors

  • Chanyavanich, V; Lo, J; Das, S

Published Date

  • June 2012

Published In

Volume / Issue

  • 39 / 6Part19

Start / End Page

  • 3837 -

PubMed ID

  • 28517094

Pubmed Central ID

  • 28517094

Electronic International Standard Serial Number (EISSN)

  • 2473-4209

Digital Object Identifier (DOI)

  • 10.1118/1.4735661

Conference Location

  • United States