Inverse 4D conformal planning for lung SBRT using particle swarm optimization

Modiri, Arezoo; Gu, Xuejun; Hagan, A; Bland, R.E.; Iyengar, Puneeth; Timmerman, Robert; Sawant, Amit

doi:10.1088/0031-9155/61/16/6181

Cited by 19 publications

(31 citation statements)

References 64 publications

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“… and as quadratic differences in Ref. ). The DVH dose coverage deviation from prescription dose

D_{i}^{P}

for a given prescription cumulative volume is given by

f_{target}^{DVH} = \frac{1}{N_{v o x e l s, i}} {false\sum}_{j = 1}^{N_{v o x e l s, i}} false| D_{i}^{P} - D_{i, j} false| θ false(D_{i}^{P} - D_{i, j} false),

where the θ ( h ) is the Heaviside function defined by

θ false(h false) = \{\begin{matrix} 1 & if h \geq 0 \\ 0 & otherwise . \end{matrix}

N_{v o x e l s, i}

is the number of voxels of the i th structure, and j is the index of voxels.…”

Section: Methodsmentioning

confidence: 99%

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Adaptive method for multicriteria optimization of intensity‐modulated proton therapy

Kamal-Sayed

Tseung

et al. 2018

Medical Physics

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Purpose Provide an adaptive multicriteria optimization (MCO) method for intensity‐modulated proton therapy (IMPT) utilizing GPU technology. Previously described limitations of MCO such as Pareto approximation and limitation on the number of objectives were addressed. Methods The treatment planning process for IMPT must account for multiple objectives, which requires extensive treatment planning resources. Often a large number of objectives (>10) are required. Hence the need for an MCO algorithm that can handle large number of objectives. The novelty of the MCO method presented here lies on the introduction of the adaptive weighting scheme that can generate a well‐distributed and dense representation of the Pareto surface for a large number of objectives in an efficient manner. In our approach the generated Pareto surface is constructed for a set of DVH objectives. The MCO algorithm is based on the augmented weighted Chebychev metric (AWCM) method with an adaptive weighting scheme. This scheme uses the differential evolution (DE) method to generate a set of well‐distributed Pareto points. The quality of the Pareto points’ distribution in the objective space was assessed quantitatively using the Pareto sampling metric. The MCO algorithm was developed to perform multiple parallel searches to achieve a rapid mapping of the Pareto surface, produce clinically deliverable plans, and was implemented on a GPU cluster. The MCO algorithm was tested on two clinical cases with 10 and 18 objectives. For each case one of the MCO‐generated plans was selected for comparison with the clinically generated plan. The MCO plan was randomly selected out of the set of MCO plans that had target coverage similar to the clinically generated plan and the same or better sparing of the organs at risk (OAR). Additionally, a validation study of the AWCM method vs the weighted sum method was performed. Results The adaptive MCO algorithm generated Pareto points on the Pareto hypersurface in a fast (2–3 hr) and efficient manner for 2 cases with 10 and 18 objectives. The MCO algorithm generated a dense and well‐distributed set of Pareto points on the objective space, and was able to achieve minimization of the Pareto sampling metric. The selected MCO plan showed an improvement of the DVH objectives in comparison to the clinically optimized plan in both cases. For case one, the MCO plan showed a 48% reduction of the 50% dose to OARs and a 16% reduction of the 1% dose to OARs. For case 2, the MCO plan showed a 72% reduction of the 50% dose to OARs and a 42% reduction of the 1% dose to OARs. The comparison of AWCM to WS showed that the AWCM method has a dosimetric advantage over WS for both patient cases. Conclusion We introduced an adaptive MCO algorithm for IMPT accelerated using GPUs. The algorithm is based on an adaptive method for generating Pareto plans in the objective space. We have shown that the algorithm can provide rapid and efficient mapping of the multicriteria Pareto surface with clinically deliverable plans.

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“… and as quadratic differences in Ref. ). The DVH dose coverage deviation from prescription dose

D_{i}^{P}

for a given prescription cumulative volume is given by

f_{target}^{DVH} = \frac{1}{N_{v o x e l s, i}} {false\sum}_{j = 1}^{N_{v o x e l s, i}} false| D_{i}^{P} - D_{i, j} false| θ false(D_{i}^{P} - D_{i, j} false),

where the θ ( h ) is the Heaviside function defined by

θ false(h false) = \{\begin{matrix} 1 & if h \geq 0 \\ 0 & otherwise . \end{matrix}

N_{v o x e l s, i}

is the number of voxels of the i th structure, and j is the index of voxels.…”

Section: Methodsmentioning

confidence: 99%

“…It has been reported that DVH‐based objective functions show nonconvex behavior . MCO based on convex geometrical approximations has been tested clinically and it was reported that the navigated plans show discrepancies from the optimal deliverable plans .…”

Section: Introductionmentioning

confidence: 99%

Adaptive method for multicriteria optimization of intensity‐modulated proton therapy

Kamal-Sayed

Tseung

et al. 2018

Medical Physics

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“…Beam dose deposition matrices were calculated in Eclipse Treatment Planning System (V13.6, Varian Medical Systems, Palo Alto, CA) using the Anisotropic Analytical Algorithm (AAA) and exported into our in-house PSO engine for inverse planning. 31,32 The optimization task was to minimize the risk-based objective function. We employed apertures that were preconfigured by Eclipse and optimized the corresponding MU weights.…”

Section: B1 Inverse Planningmentioning

confidence: 99%

“…We used inverse planning and particle swarm optimization (PSO), a highly parallelized, metaheuristic, global optimization algorithm, to minimize our modeled overall risk. PSO has been customized and successfully used in various other RT inverse planning studies with nonconvex solution spaces . We allowed the optimization algorithm to choose among a large set of gantry angles that included, but was not limited to, the beam angles routinely used in clinical practice.…”

Section: Introductionmentioning

confidence: 99%

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Individualized estimates of overall survival in radiation therapy plan optimization — A concept study

et al. 2018

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Purpose Current radiation therapy planning uses a set of defined dose–volume constraints to ensure a specified level of tumor coverage while constraining the dose distribution in the organs at risk. Such constraints are aggregated, population‐based quantities that do not adequately consider patient‐specific risk factors. Furthermore, these constraints are calculated for each organ independently and it is therefore not guaranteed that the optimal trade‐off between organs is achieved. We introduce a novel radiotherapy planning approach where a patient‐specific all‐cause mortality risk is minimized using inverse plan optimization. As illustration of concept, our outcome risk model incorporates patient age, sex, cardiac risk factor (CRF), and smoking. Methods and materials We retrospectively analyzed a left‐sided breast cancer case and a Hodgkin's lymphoma case, both clinically treated with three‐dimensional conformal radiotherapy (3D‐CRT). Our objective function for inverse plan optimization was an equally weighted summation of risk models for cancer recurrence and mortality from radiation‐induced coronary heart disease and secondary lung and breast cancers incorporating patient age, sex, CRF, and smoking. We allowed the optimization algorithm to choose from a large set of gantry angles. The optimization task was to choose beams and optimize monitor units (MUs) so that overall survival was maximized (and the total risk of cancer recurrence and mortality from radiation‐induced causes were minimized). The sensitivity analysis was performed in the lymphoma case by changing the tumor control probability model from using mean dose (Model 1) to using generalized equivalent uniform dose (Model 2). Results For the breast case in this study, the 3D‐CRT clinical plan used eight beams while the proposed 3D‐CRT outcome‐optimized plan used five beams, reducing the total risk — summation of the risks of recurrence and secondary disease mortality — from 3% to 2%. The mean doses to clinical target volume (CTV) and internal mammary nodes (IMN) were increased in the outcome‐optimized plan by 1.9 and 1.8 Gy, respectively. For the Hodgkin's lymphoma case, the clinical 3D‐CRT plan used two beams, while the proposed 3D‐CRT outcome‐optimized plan used three beams, reducing the total risk by 6% (from 16% to 10%). Using either of the two tumor control models for the lymphoma case resulted in outcome‐optimized plans where tumor control was compensated at the cost of saving organs at risk. However, the impact of sensitivity to models was comparatively large. Using Model 1 resulted in a reduction in mean target dose by 15.2 vs 7.1 Gy for Model 2. In all cases, the chosen beams in outcome‐optimized plans were different from clinically used beams. Conclusions The proposed optimization strategy, supplanting dosimetric objectives with comprehensive individual risk estimates, has the potential to yield improved outcomes in terms of reduced mortality risk in cancer patients treated with radiotherapy. The approach is, however, currently limited by ga...

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