TY - JOUR
T1 - Robust Optimization for Intensity Modulated Proton Therapy to Redistribute High Linear Energy Transfer from Nearby Critical Organs to Tumors in Head and Neck Cancer
AU - Liu, Chenbin
AU - Patel, Samir H.
AU - Shan, Jie
AU - Schild, Steven E.
AU - Vargas, Carlos E.
AU - Wong, William W.
AU - Ding, Xiaoning
AU - Bues, Martin
AU - Liu, Wei
N1 - Funding Information:
This research was supported by Arizona Biomedical Research Commission Investigator Award, the Lawrence W. and Marilyn W. Matteson Fund for Cancer Research, and the Kemper Marley Foundation. Disclosures: C.L. reports grants from the National Institutes of Health/National Cancer Institute, outside the submitted work; in addition, C.L. has a pending US patent, “An Accurate and Efficient Hybrid Method Based on Ray Casting to Calculate Physical Dose and Linear Energy Transfer (LET) Distribution for Intensity-Modulated Proton Therapy,” which is licensed to. decimal, LLC. S.E.S. reports personal fees from UpToDate and the status of editor and author, outside the submitted work.
Publisher Copyright:
© 2020 Elsevier Inc.
PY - 2020/5/1
Y1 - 2020/5/1
N2 - Purpose: We propose linear energy transfer (LET)-guided robust optimization in intensity modulated proton therapy for head and neck cancer. This method simultaneously considers LET and physical dose distributions of tumors and organs at risk (OARs) with uncertainties. Methods and Materials: Fourteen patients with head and neck cancer were included in this retrospective study. Cord, brain stem, brain, and oral cavity were considered. Two algorithms, voxel-wise worst-case robust optimization and LET-guided robust optimization (LETRO), were used to generate intensity modulated proton therapy plans for each patient. The latter method directly optimized LET distributions rather than indirectly as in previous methods. LET–volume histograms (LETVHs) were generated, and high LET was redistributed from nearby OARs to tumors in a user-defined way via LET–volume constraints. Dose–volume histogram indices, such as clinical target volume (CTV) D98% and D2%-D98%, cord Dmax, brain stem Dmax, brain Dmax, and oral cavity Dmean, were calculated. Plan robustness was quantified using the worst-case analysis method. LETVH indices analogous to dose–volume histogram indices were used to characterize LET distributions. The Wilcoxon signed rank test was performed to measure statistical significance. Results: In the nominal scenario, LETRO provided higher LET distributions in the CTV (unit: keV/μm; CTV LET98%: 1.18 vs 1.08, LETRO vs RO, P = .0031) while preserving comparable physical dose and plan robustness. LETRO achieved significantly reduced LET distributions in the cord, brain stem, and oral cavity compared with RO (cord LETmax: 7.20 vs 8.20, P = .0010; brain stem LETmax: 10.95 vs 12.05, P = .0007; oral cavity LETmean: 2.11 vs 3.12, P = .0052) and had comparable physical dose and plan robustness in all OARs. In the worst-case scenario, LETRO achieved significantly higher LETmean in the CTV, reduced LETmax in the brain, and was comparable to other LETVH indices (CTV LETmean: 3.26 vs 3.35, P = .0012; brain LETmax: 24.80 vs 22.00, P = .0016). Conclusions: LETRO robustly optimized LET and physical dose distributions simultaneously. It redistributed high LET from OARs to targets with slightly modified physical dose and plan robustness.
AB - Purpose: We propose linear energy transfer (LET)-guided robust optimization in intensity modulated proton therapy for head and neck cancer. This method simultaneously considers LET and physical dose distributions of tumors and organs at risk (OARs) with uncertainties. Methods and Materials: Fourteen patients with head and neck cancer were included in this retrospective study. Cord, brain stem, brain, and oral cavity were considered. Two algorithms, voxel-wise worst-case robust optimization and LET-guided robust optimization (LETRO), were used to generate intensity modulated proton therapy plans for each patient. The latter method directly optimized LET distributions rather than indirectly as in previous methods. LET–volume histograms (LETVHs) were generated, and high LET was redistributed from nearby OARs to tumors in a user-defined way via LET–volume constraints. Dose–volume histogram indices, such as clinical target volume (CTV) D98% and D2%-D98%, cord Dmax, brain stem Dmax, brain Dmax, and oral cavity Dmean, were calculated. Plan robustness was quantified using the worst-case analysis method. LETVH indices analogous to dose–volume histogram indices were used to characterize LET distributions. The Wilcoxon signed rank test was performed to measure statistical significance. Results: In the nominal scenario, LETRO provided higher LET distributions in the CTV (unit: keV/μm; CTV LET98%: 1.18 vs 1.08, LETRO vs RO, P = .0031) while preserving comparable physical dose and plan robustness. LETRO achieved significantly reduced LET distributions in the cord, brain stem, and oral cavity compared with RO (cord LETmax: 7.20 vs 8.20, P = .0010; brain stem LETmax: 10.95 vs 12.05, P = .0007; oral cavity LETmean: 2.11 vs 3.12, P = .0052) and had comparable physical dose and plan robustness in all OARs. In the worst-case scenario, LETRO achieved significantly higher LETmean in the CTV, reduced LETmax in the brain, and was comparable to other LETVH indices (CTV LETmean: 3.26 vs 3.35, P = .0012; brain LETmax: 24.80 vs 22.00, P = .0016). Conclusions: LETRO robustly optimized LET and physical dose distributions simultaneously. It redistributed high LET from OARs to targets with slightly modified physical dose and plan robustness.
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U2 - 10.1016/j.ijrobp.2020.01.013
DO - 10.1016/j.ijrobp.2020.01.013
M3 - Article
C2 - 31987967
AN - SCOPUS:85079518262
SN - 0360-3016
VL - 107
SP - 181
EP - 193
JO - International Journal of Radiation Oncology Biology Physics
JF - International Journal of Radiation Oncology Biology Physics
IS - 1
ER -