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Knowledge-Based Automatic Adaptive Replanning Using ESAPI as Part of Adaptive Treatment Decision-Support System for Stage III Lung Cancer

B Gu*, J Kavanaugh , J Hilliard , Washington University in St. Louis, St. Louis, MO


(Monday, 7/30/2018) 3:45 PM - 4:15 PM

Room: Exhibit Hall | Forum 5

Purpose: Lung tumor volumetric and positional changes occurring during fractionated radiotherapy may prompt a physician to order a re-CT to evaluate the difference between the planned and delivered dose. The purpose of this study was to provide efficient quantitative decision support for a physician to evaluate the dosimetric impact of anatomic changes between the original CT and mid-treatment CT datasets, and to quickly assess the benefit of a reoptimized treatment plan.

Methods: An automated application was developed in the Microsoft Visual Studio utilizing the Varian Eclipse Application Programming Interface (ESAPI), with two Eclipse scripts created to run in the External Beam Planning module. The scripts created a verification plan by recalculating the initial plan on re-CT dataset, produced adaptive improvement predictions, reoptimized an adaptive plan using a custom RapidPlan model, and provided decision support through automatically extracting and comparing DVH metrics. A total of 18 patients that underwent ART were selected for the scripts testing. Efficiency was assessed by comparing the time required to generate the plans and reports through scripts compared to the manual planning process. Quality was assessed by comparing PTV coverage and OAR sparing between the manual and automated adaptive plan.

Results: The average time to generate the verification plan, reoptimized adaptive plan, and decision support reports was 6.5 minutes. The corresponding manual process takes 40 minutes. For all 18 patients, the replan achieved similar PTV prescription coverage as the initial plan (average relative difference of V95% and V100% is 0.08% and 0.7% respectively). In general, the OAR dose were lower from the replan comparing with the original plan, average relative heart dose lowered by 13%.

Conclusion: The decision-support tool provides a practical and efficient way to determine the efficacy and utility of adaptive radiotherapy for lung cancer patients while automatically generating the high quality adaptive plans.


Lung, Computer Software


TH- External beam- photons: adaptive therapy

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