Dose deformation-invariance in adaptive prostate radiation therapy: Implication for treatment simulations

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Purpose: To compare two coverage-based planning (CP) techniques with standard fixed marginbased planning (FM), considering the dosimetric impact of interfraction deformable organ motion exclusively for high-risk prostate treatments. Methods: Nineteen prostate cancer patients with 8-13 prostate CT images of each patient were used to model patient-specific interfraction deformable organ changes. The model was based on the principal component analysis (PCA) method and was used to predict the patient geometries for virtual treatment course simulation. For each patient, an IMRT plan using zero margin on target structures, prostate (CTV prostate ) and seminal vesicles (CTV SV ), were created, then evaluated by simulating 1000 30-fraction virtual treatment courses. Each fraction was prostate centroid aligned. Patients whose D 98 failed to achieve 95% coverage probability objective D 98,95 ≥ 78 Gy (CTV prostate ) or D 98,95 ≥ 66 Gy (CTV SV ) were replanned using planning techniques: (1) FM (PTV prostate = CTV prostate + 5 mm, PTV SV = CTV SV + 8 mm), (2) CP OM which optimized uniform PTV margins for CTV prostate and CTV SV to meet the coverage probability objective, and (3) CP COP which directly optimized coverage probability objectives for all structures of interest. These plans were intercompared by computing probabilistic metrics, including 5% and 95% percentile DVHs (pDVH) and TCP/NTCP distributions. Results: All patients were replanned using FM and two CP techniques. The selected margins used in FM failed to ensure target coverage for 8/19 patients. Twelve CP OM plans and seven CP COP plans were favored over the other plans by achieving desirable D 98,95 while sparing more normal tissues. Conclusions: Coverage-based treatment planning techniques can produce better plans than FM, while relative advantages of CP OM and CP COP are patient-specific. C 2014 American Association of Physicists in Medicine. [http://dx

Section: A Prostate Plansmentioning

confidence: 99%

Coverage‐based treatment planning to accommodate deformable organ variations in prostate cancer treatment

Vile

Sharma

et al. 2014

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“…Instead of repeating the dose calculation for every possible position of the patient to explicitly find each D x , we adopt the static dose cloud assumption, which is commonly used in treatment planning optimization. 6,19 Under this assumption, the position of the patient does not affect the dose distribution, and if a shift of a patient occurs, the nominal dose distribution is delivered to the shifted patient. This assumption improves the computational efficiency: each D x can be assembled from the rows of the nominal D with no additional dose calculation.…”

Section: D1 Clinical Liver Cases and Prescriptionsmentioning

confidence: 99%

Robust spatiotemporal fractionation schemes in the presence of patient setup uncertainty

Gaddy

Unkelbach²,

Papp

2019

Purpose: Spatiotemporal fractionation schemes for photon radiotherapy have recently arisen as a promising technique for healthy tissue sparing. Because spatiotemporally fractionated treatments have a characteristic pattern of delivering high doses to different parts of the tumor in each fraction, uncertainty in patient positioning is an even more pressing concern than in conventional uniform fractionation. Until now, such concerns in patient setup uncertainty have not been addressed in the context of spatiotemporal fractionation. Methods: A stochastic optimization model is used to incorporate patient setup uncertainty to optimize spatiotemporally fractionated plans using expected penalties for deviations from prescription values. First, a robust uniform reference plan is optimized with a stochastic optimization model. Then, a spatiotemporal plan is optimized with a constrained stochastic optimization model that minimizes a primary clinical objective and constrains the spatiotemporal plan to be at least as good as the uniform reference plan with respect to all other objectives. A discrete probability distribution is defined to characterize the random setup error occurring in each fraction. For the optimization of uniform plans, the expected penalties are computed exactly by exploiting the symmetry of the fractions, and for the spatiotemporal plans, quasi-Monte Carlo sampling is used to approximate the expectation. Results: Using five clinical liver cases, it is demonstrated that spatiotemporally fractionated treatment plans maintain the same robust tumor coverage as a stochastic uniform reference plan and exhibit a reduction in the expected mean BED of the uninvolved liver. This is observed for a spectrum of probability distributions of random setup errors with shifts in the patient position of up to 5 mm from the nominal position. For probability distributions with small variance in the patient position, the spatiotemporal plans exhibit an 8-30% reduction in expected mean BED in the healthy liver tissue for shifts up to 2.5 mm and reductions of 5-25% for shifts up to 5 mm. Conclusions: In the presence of patient setup uncertainty, spatiotemporally fractionated treatment plans exhibit the same robust tumor coverage as their uniformly fractionated counterparts and still retain the benefit in sparing healthy tissues.

“…More details of this patient cohort have been described in earlier published work. 20,27,28 A single physician at VCU contoured the structures of prostate (CTV prostate ), seminal vesicles (CTV SV ), rectum, bladder, left femur, and right femur on each image. Contours of CTV SV , rectum, and bladder were modified slightly to eliminate overlapping region with CTV prostate or with other structures.…”

Section: A Prostate Plansmentioning

confidence: 99%

“…The details on the CP COP algorithm can be found in our previous work. 19,20 As in our prior work, dose shift invariance 28 was assumed to reduce required computations during the CP COP optimization. CP OM utilizes the method developed by Gordon and Siebers, 18 starting with the IMRT plan using 0 PTV margins for CTV prostate and CTV SV and same dose-volume objectives as those used in FM technique.…”

Section: B2 Planning Techniques: Fm Cp Cop and Cp Ommentioning

confidence: 99%

Coverage‐based treatment planning to accommodate delineation uncertainties in prostate cancer treatment

Gordon

Siebers

2015

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