Automated inverse optimization facilitates lower doses to normal tissue in pancreatic stereotactic body radiotherapy

Ivaylo B Mihaylov, Eric A Mellon, Raphael Yechieli and Lorraine Portelance

PLOS ONE, 2018, vol. 13, issue 1, 1-12

Abstract: Purpose: Inverse planning is trial-and-error iterative process. This work introduces a fully automated inverse optimization approach, where the treatment plan is closely tailored to the unique patient anatomy. The auto-optimization is applied to pancreatic stereotactic body radiotherapy (SBRT). Materials and methods: The automation is based on stepwise reduction of dose-volume histograms (DVHs). Five uniformly spaced points, from 1% to 70% of the organ at risk (OAR) volumes, are used. Doses to those DVH points are iteratively decreased through multiple optimization runs. With each optimization run the doses to the OARs are decreased, while the dose homogeneity over the target is increased. The iterative process is terminated when a pre-specified dose heterogeneity over the target is reached. Twelve pancreatic cases were retrospectively studied. Doses to the target, maximum doses to duodenum, bowel, stomach, and spinal cord were evaluated. In addition, mean doses to liver and kidneys were tallied. The auto-optimized plans were compared to the actual treatment plans, which are based on national protocols. Results: The prescription dose to 95% of the planning target volume (PTV) is the same for the treatment and the auto-optimized plans. The average difference for maximum doses to duodenum, bowel, stomach, and spinal cord are -4.6 Gy, -1.8 Gy, -1.6 Gy, and -2.4 Gy respectively. The negative sign indicates lower doses with the auto-optimization. The average differences in the mean doses to liver and kidneys are -0.6 Gy, and -1.1 Gy to -1.5 Gy respectively. Conclusions: Automated inverse optimization holds great potential for personalization and tailoring of radiotherapy to particular patient anatomies. It can be utilized for normal tissue sparing or for an isotoxic dose escalation.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0191036

DOI: 10.1371/journal.pone.0191036

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