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Impact of Respiratory Motion On Robustly-Optimized Intensity-Modulated Proton Therapy to Treat Lung Cancers

W Liu

W Liu1*, Z Liao2 , S Schild3 , P Park4 , H Li5 , Y Li6 , X Li7 , J Shen8 , A Anand9 , N Sahoo10 , L Dong11 , M Bues12 , X Zhu13 , R Mohan14 , (1) Mayo Clinic Arizona, Phoenix, AZ, (2) MD Anderson Cancer Center, Houston, TX, (3) Mayo Clinic Arizona, Phoenix, AZ, (4) ,Scottsdale, GA, (5) M.D. Anderson Cancer Center, Houston, TX, (6) Varian Medical Systems, Houston, TX, (7) ,Houston, AA, (8) Mayo Clinic Arizona, Phoenix, AA, (9) Mayo Clinic Arizona, Phoenix, ,(10) MD Anderson Cancer Center, Houston, TX, (11) Scripps Proton Therapy Center, San Diego, CA, (12) Mayo Clinic Arizona, Phoenix, AZ, (13) UT MD Anderson Cancer Center, Houston, TX, (14) UT MD Anderson Cancer Center, Houston, TX


SU-E-T-452 Sunday 3:00PM - 6:00PM Room: Exhibit Hall

We compared conventionally optimized intensity-modulated proton therapy (IMPT) treatment plans against the worst-case robustly optimized treatment plans for lung cancer. The comparison of the two IMPT optimization strategies focused on the resulting plans' ability to retain dose objectives under the influence of patient set-up, inherent proton range uncertainty, and dose perturbation caused by respiratory motion.

For each of the 9 lung cancer cases two treatment plans were created accounting for treatment uncertainties in two different ways: the first used the conventional method: delivery of prescribed dose to the planning target volume (PTV) that is geometrically expanded from the internal target volume (ITV). The second employed the worst-case robust optimization scheme that addressed set-up and range uncertainties through beamlet optimization. The plan optimality and plan robustness were calculated and compared. Furthermore, the effects on dose distributions of the changes in patient anatomy due to respiratory motion was investigated for both strategies by comparing the corresponding plan evaluation metrics at the end-inspiration and end-expiration phase and absolute differences between these phases. The mean plan evaluation metrics of the two groups were compared using two-sided paired t-tests.

Without respiratory motion considered, we affirmed that worst-case robust optimization is superior to PTV-based conventional optimization in terms of plan robustness and optimality. With respiratory motion considered, robust optimization still leads to more robust dose distributions to respiratory motion for targets and comparable or even better plan optimality [D95% ITV: 96.6% versus 96.1% (p=0.26), D5% - D95% ITV: 10.0% versus 12.3% (p=0.082), D1% spinal cord: 31.8% versus 36.5% (p =0.035)].

Worst-case robust optimization led to superior solutions for lung IMPT. Despite of the fact that robust optimization did not explicitly account for respiratory motion it produced motion-resistant treatment plans. However, further research is needed to incorporate respiratory motion into IMPT robust optimization.

Funding Support, Disclosures, and Conflict of Interest: This research was supported by the NCI through grants P01CA021239 and K25CA168984, by the Fraternal Order of Eagles Cancer Research Fund, by The Lawrence W. and Marilyn W. Matteson Fund for Cancer Research, and by the University Cancer Foundation via the Institutional Research Grant program at MD Anderson Cancer Center.

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