SU-E-T-217: Automated Data Mining of Lung SBRT Cases for Predicting Dosimetric Indices in Prospective Plans

Medical Physics
T AtwoodD Hristov

Abstract

To develop an automated technique for evaluating lung stereotactic body radiotherapy (SBRT) plan quality and predicting achievable lung dose constraints. We examined the workflow scripting capabilities of a radiation oncology software package (MIM Software Inc) for automatically generating mean dose gradients and overlap volume histograms (OVHs) for lung SBRT plans. Mean dose gradients were created to assess plan quality by quantifying the dose fall-off from the planning target volume (PTV) into the ipsilateral lung. OVHs were created to evaluate potential predictors of achievable lung dose constraints by measuring the amount of ipsilateral lung volume overlapping the uniformly expanded PTV. Based on the knowledge that OVHs will depend on the size and spatial relationship of the lung and PTV structures, we hypothesize that OVH-derived indices will therefore serve as predictors of achievable lung dose. The workflow scripts were evaluated in a pilot study of 10 lung SBRT plans (50 Gy in 5 fractions) with varying PTV sizes and locations. The scripted workflows were successful in automatically generating mean dose gradients and OVHs for the PTV and ipsilateral lung. The workflows were able to produce the aforementioned data in a ma...Continue Reading

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