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Clinical Implementation of Automated IMRT Treatment Planning Using Hierarchical Constrained Optimization (HCO)

M Zarepisheh

M Zarepisheh*, L Hong , Y Zhou , J Mechalakos , M Hunt , G Mageras , J Deasy , Memorial Sloan Kettering Cancer Center, New York, NY


WE-RAM3-GePD-T-2 (Wednesday, August 2, 2017) 10:30 AM - 11:00 AM Room: Therapy ePoster Lounge

Purpose: To develop and clinically implement a fully automated approach to IMRT treatment planning using HCO and the treatment planning system (TPS) Application Program Interface (API).

Methods: This study formulates IMRT treatment planning as an HCO problem (also known as prioritized optimization). HCO prioritizes the clinical goals and optimizes them in ordered steps. In this study, we maximize tumor coverage first and then minimize critical organ doses in subsequent steps. Maximum dose criteria to clinical volumes are always respected through hard constraints applied in all steps. The fluence map is smoothed in the last step for delivery efficiency. The API serves to pull patient data needed for optimization (e.g., beam geometries, influence matrix). To solve the resultant large-scale non-linear constrained optimization problems, we use commercial optimization engines. Subsequently, the optimal fluence map is imported back using the API to TPS for leaf sequencing and final dose calculation. The entire aforementioned workflow is automated, requiring user interaction solely to prepare the contours and beam arrangement prior to launching the HCO API.

Results: HCO IMRT automatic planning was first implemented for 24 Gy single fraction paraspinal SBRT patients. All HCO plans used 9 fixed IMRT fields. Optimization required ~1-3 hours, mainly depending on the size of the PTV volumes (range 15.0 to 188.3 cc). All HCO plans met all clinical planning criteria. As pre-clinical dosimetric study, we compared HCO plans with previously treated 4-7 partial arcs VMAT clinical plans for 25 patients. On average, the HCO plan increased PTV coverage (V100%) by 2.3%, CTV minimum dose by 11.6%, and decreased esophagus V18Gy by 40.1%.

Conclusion: HCO shows promise as a powerful tool to automate IMRT treatment planning. Using TPS API, we developed a plugin which fully automates the workflow and we are expecting to implement this new technology to other disease sites.

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