PTV‐based IMPT optimization incorporating planning risk volumes vs robust optimization. Issue 2 (15th January 2013)

• Record Type: Journal Article
• Authors: Liu, Wei; Frank, Steven J.; Li, Xiaoqiang; Li, Yupeng; Zhu, Ron. X.; Mohan, Radhe
• Is Part Of: Medical physics. Volume 40:Issue 2(2013)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2013-01-15
• Subjects: Therapeutic applications, including brachytherapy -- Optimization -- Dose‐volume analysis -- Dosimetry/exposure assessment; brain -- cancer -- dosimetry -- optimisation -- radiation therapy; robust optimization -- IMPT -- planning risk volume -- robustness evaluation; Radiation therapy; Optimization -- Dosimetry -- Tissues -- Cancer -- Protons -- Proton therapy -- Brain -- Error analysis -- Medical imaging -- Intensity modulated radiation therapy; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4774363
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: : Robust optimization leads to intensity‐modulated proton therapy (IMPT) plans that are less sensitive to uncertainties and superior in terms of organs‐at‐risk (OARs) sparing, target dose coverage, and homogeneity compared to planning target volume (PTV)‐based optimized plans. Robust optimization incorporates setup and range uncertainties, which implicitly adds margins to both targets and OARs and is also able to compensate for perturbations in dose distributions within targets and OARs caused by uncertainties. In contrast, the traditional PTV‐based optimization considers only setup uncertainties and adds a margin only to targets but no margins to the OARs. It also ignores range uncertainty. The purpose of this work is to determine if robustly optimized plans are superior to PTV‐based plans simply because the latter do not assign margins to OARs during optimization. Methods: : The authors retrospectively selected from their institutional database five patients with head and neck (H&N) cancer and one with prostate cancer for this analysis. Using their original images and prescriptions, the authors created new IMPT plans using three methods: PTV‐based optimization, optimization based on the PTV and planning risk volumes (PRVs) (i.e., "PTV+PRV‐based optimization"), and robust optimization using the "worst‐case" dose distribution. The PRVs were generated by uniformly expanding OARs by 3 mm for the H&N cases and 5 mm for the prostate case. The dose‐volume histograms (DVHs) from the worst‐case dose distributions were used to assess and compare plan quality. Families of DVHs for each uncertainty for all structures of interest were plotted along with the nominal DVHs. The width of the "bands" of DVHs was used to quantify the plan sensitivity to uncertainty. Results: : Compared with conventional PTV‐based and PTV+PRV‐based planning, robust optimization led to a smaller bandwidth for the targets in the face of uncertainties {clinical target volume [CTV] bandwidth: 0.59 [robust], 3.53 [PTV+PRV], and 3.53 [PTV] Gy (RBE)}. It also resulted in higher doses to 95% of the CTV {D95% : 60.8 [robust] vs 59.3 [PTV+PRV] vs 59.6 [PTV] Gy (RBE)}, smaller D5% (doses to 5% of the CTV) minus D95% {D5% − D95% : 13.2 [robust] vs 17.5 [PTV+PRV] vs 17.2 [PTV] Gy (RBE)}. At the same time, the robust optimization method irradiated OARs less {maximum dose to 1 cm 3 of the brainstem: 48.3 [robust] vs 48.8 [PTV+PRV] vs 51.2 [PTV] Gy (RBE); mean dose to the oral cavity: 22.3 [robust] vs 22.9 [PTV+PRV] vs 26.1 [PTV] Gy (RBE); maximum dose to 1% of the normal brain: 66.0 [robust] vs 68.0 [PTV+PRV] vs 69.3 [PTV] Gy (RBE)}. Conclusions: : For H&N cases studied, OAR sparing in PTV+PRV‐based optimization was inferior compared to robust optimization but was superior compared to PTV‐based optimization; however, target dose robustness and homogeneity were comparable in the PTV+PRV‐based and PTV‐based optimizations. The same pattern held for the prostate case. The authors' data suggest that the superiority of robust optimization is not due simply to its inclusion of margins for OARs, but that this is due mainly to the ability of robust optimization to compensate for perturbations in dose distributions within target volumes and normal tissues caused by uncertainties.