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SU-E-T-572:一种用于评估基于知识的治疗计划的计划质量指标。

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

作者信息

Chanyavanich V, Lo J, Das S

机构信息

Emory University, Atlanta, GA.

Duke University Medical Center, Durham, NC.

出版信息

Med Phys. 2012 Jun;39(6Part19):3837. doi: 10.1118/1.4735661.

Abstract

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.

摘要

目的

在前列腺调强放射治疗(IMRT)治疗计划中,患者解剖结构的差异使得很难预先估计直肠和膀胱可能实现的剂量降低程度。我们开发了一种基于互信息的框架,以便在进行任何治疗计划或优化之前估计新患者可实现的计划质量。

方法

知识库由250个回顾性前列腺IMRT计划组成。使用这些先前的计划,将20个查询病例分别与数据库中的5个病例进行匹配。我们基于计划靶体积(PTV)、直肠和膀胱曲线下面积(AUC)的加权和提出了一种简单的剂量体积直方图(DVH)计划质量指标(PQ)。我们评估基于知识生成的计划的质量,并建立计划质量与病例相似度之间的相关性。

结果

引入的计划质量指标与病例之间的相互相似度具有良好的相关性(r2 = 0.8)。具有高解剖相似度的匹配病例可用于生成新的高质量计划。毫不奇怪,解剖相似度低的不匹配病例往往会产生低质量的计划,因为来自不同病例的适配注量不能充分修改以产生可接受的PTV覆盖。

结论

计划质量指标与新查询病例和匹配病例之间的解剖相似度程度密切相关。进一步的工作将研究如何应用该指标进一步分层和选择基于知识的计划病例。

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