A new optimization algorithm for HDR brachytherapy that improves DVH-based planning: Truncated Conditional Value-at-Risk (TCVaR)

Wu, Victor W; Epelman, Marina A.; Pasupathy, Kalyan S.; Sir, Mustafa Y.; Deufel, Christopher L.

doi:10.1088/2057-1976/abb4bc

Cited by 4 publications

(6 citation statements)

References 40 publications

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“…CVaR + represents a convex DVC that captures the mean upper‐tail dose of a structure's dose‐volume histogram and has previously been used to formulate linear RT optimization problems. 26 , 27 , 28 …”

Section: Methodsmentioning

confidence: 99%

“…To avoid non‐convex functions of dose, dose‐volume constraints (DVC), which are typically represented by value‐at‐risk (VaR) metrics (e.g., V 20Gy , D 0.1cc ), are instead represented by upper conditional value‐at risk (CVaR + ) metrics in constraint (1f). CVaR + represents a convex DVC that captures the mean upper‐tail dose of a structure's dose‐volume histogram and has previously been used to formulate linear RT optimization problems 26–28 …”

Section: Methodsmentioning

confidence: 99%

“…CVaR + represents a convex DVC that captures the mean upper-tail dose of a structure's dose-volume histogram and has previously been used to formulate linear RT optimization problems. [26][27][28] In Stage 2, the objective is to minimize dosebased metrics similar to traditional plan optimization approaches. Stage 1 (biological objective) and Stage 2 (dose-based objective) are intentionally separated to eliminate the need to weight outcomes directly against dose metrics and maintain user interpretability of the utility objective and outcome weighting factors in Stage 1.…”

Section: Optimization Overviewmentioning

confidence: 99%

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Direct incorporation of patient‐specific efficacy and toxicity estimates in radiation therapy plan optimization

Polan

Epelman

et al. 2022

Medical Physics

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Purpose Current radiation therapy (RT) treatment planning relies mainly on pre‐defined dose‐based objectives and constraints to develop plans that aim to control disease while limiting damage to normal tissues during treatment. These objectives and constraints are generally population‐based, in that they are developed from the aggregate response of a broad patient population to radiation. However, correlations of new biologic markers and patient‐specific factors to treatment efficacy and toxicity provide the opportunity to further stratify patient populations and develop a more individualized approach to RT planning. We introduce a novel intensity‐modulated radiation therapy (IMRT) optimization strategy that directly incorporates patient‐specific dose response models into the planning process. In this strategy, we integrate the concept of utility‐based planning where the optimization objective is to maximize the predicted value of overall treatment utility, defined by the probability of efficacy (e.g., local control) minus the weighted sum of toxicity probabilities. To demonstrate the feasibility of the approach, we apply the strategy to treatment planning for non‐small cell lung cancer (NSCLC) patients. Methods and materials We developed a prioritized approach to patient‐specific IMRT planning. Using a commercial treatment planning system (TPS), we calculate dose based on an influence matrix of beamlet‐dose contributions to regions‐of‐interest. Then, outside of the TPS, we hierarchically solve two optimization problems to generate optimal beamlet weights that can then be imported back to the TPS. The first optimization problem maximizes a patient's overall plan utility subject to typical clinical dose constraints. In this process, we facilitate direct optimization of efficacy and toxicity trade‐off based on individualized dose‐response models. After optimal utility is determined, we solve a secondary optimization problem that minimizes a conventional dose‐based objective subject to the same clinical dose constraints as the first stage but with the addition of a constraint to maintain the optimal utility from the first optimization solution. We tested this method by retrospectively generating plans for five previously treated NSCLC patients and comparing the prioritized utility plans to conventional plans optimized with only dose metric objectives. To define a plan utility function for each patient, we utilized previously published correlations of dose to local control and grade 3–5 toxicities that include patient age, stage, microRNA levels, and cytokine levels, among other clinical factors. Results The proposed optimization approach successfully generated RT plans for five NSCLC patients that improve overall plan utility based on personalized efficacy and toxicity models while accounting for clinical dose constraints. Prioritized utility plans demonstrated the largest average improvement in local control (16.6%) when compared to plans generated with conventional planning objectives. However, for some p...

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Optimization Overviewmentioning

confidence: 99%

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Direct incorporation of patient‐specific efficacy and toxicity estimates in radiation therapy plan optimization

Polan

Epelman

et al. 2022

Medical Physics

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“…The purpose was to increase the dose to the tumor dose points which receive the lowest dose, as the DI V 100 alone does not distinguish between the doses that are lower than the prescription dose. An approximation of DIs called truncated CVaR was used in Deufel et al (2020) and Wu et al (2020); in these approaches, a linear CVaR-based model was solved repeatedly to achieve accurate DI approximations.…”

Section: Solution Approachesmentioning

confidence: 99%

Emerging technologies in brachytherapy

Song¹,

Robar²,

Morén³

et al. 2021

Phys. Med. Biol.

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Brachytherapy is a mature treatment modality. The literature is abundant in terms of review articles and comprehensive books on the latest established as well as evolving clinical practices. The intent of this article is to part ways and look beyond the current state-of-the-art and review emerging technologies that are noteworthy and perhaps may drive the future innovations in the field. There are plenty of candidate topics that deserve a deeper look, of course, but with practical limits in this communicative platform, we explore four topics that perhaps is worthwhile to review in detail at this time. First, intensity modulated brachytherapy (IMBT) is reviewed. The IMBT takes advantage of anisotropic radiation profile generated through intelligent high-density shielding designs incorporated onto sources and applicators such to achieve high quality plans. Second, emerging applications of 3D printing (i.e. additive manufacturing) in brachytherapy are reviewed. With the advent of 3D printing, interest in this technology in brachytherapy has been immense and translation swift due to their potential to tailor applicators and treatments customizable to each individual patient. This is followed by, in third, innovations in treatment planning concerning catheter placement and dwell times where new modelling approaches, solution algorithms, and technological advances are reviewed. And, fourth and lastly, applications of a new machine learning technique, called deep learning, which has the potential to improve and automate all aspects of brachytherapy workflow, are reviewed. We do not expect that all ideas and innovations reviewed in this article will ultimately reach clinic but, nonetheless, this review provides a decent glimpse of what is to come. It would be exciting to monitor as IMBT, 3D printing, novel optimization algorithms, and deep learning technologies evolve over time and translate into pilot testing and sensibly phased clinical trials, and ultimately make a difference for cancer patients. Today’s fancy is tomorrow’s reality. The future is bright for brachytherapy.

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“…Romeijn et al [59] were the first to propose a dose planning model with MTD constraints for IMRT. An optimization model for HDR BT with MTD constraints was proposed in [60], but has later also been included in optimization models in Paper B and [61]. The following model with MTD constraints is adapted from [60].…”

Section: Mean-tail-dose Modelsmentioning

confidence: 99%

Treatment Planning of High Dose-Rate Brachytherapy - Mathematical Modelling and Optimization

Morén

2021

Linköping Studies in Science and Technology. Dissertations

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Cancer is a widespread class of diseases that each year affects millions of people. It is mostly treated with chemotherapy, surgery, radiation therapy, or combinations thereof. High doserate (HDR) brachytherapy (BT) is one modality of radiation therapy, which is used to treat for example prostate cancer and gynecologic cancer. In BT, catheters (i.e., hollow needles) or applicators are used to place a single, small, but highly radioactive source of ionizing radiation close to or within a tumour, at dwell positions. An emerging technique for HDR BT treatment is intensity modulated brachytherapy (IMBT), in which static or dynamic shields are used to further shape the dose distribution, by hindering the radiation in certain directions.The topic of this thesis is the application of mathematical optimization to model and solve the treatment planning problem. The treatment planning includes decisions on catheter placement, that is, how many catheters to use and where to place them, as well as decisions for dwell times. Our focus is on the latter decisions. The primary treatment goals are to give the tumour a sufficiently high radiation dose while limiting the dose to the surrounding healthy organs, to avoid severe side effects. Because these aims are typically in conflict, optimization models of the treatment planning problem are inherently multiobjective. Compared to manual treatment planning, there are several advantages of using mathematical optimization for treatment planning. First, the optimization of treatment plans requires less time, compared to the time-consuming manual planning. Secondly, treatment plan quality can be improved by using optimization models and algorithms. Finally, with the use of sophisticated optimization models and algorithms the requirements of experience and skill level for the planners are lower. The use of optimization for treatment planning of IMBT is especially important because the degrees of freedom are too many for manual planning.The contributions of this thesis include the study of properties of treatment planning models, suggestions for extensions and improvements of proposed models, and the development of new optimization models that take clinically relevant, but uncustomary aspects, into account in the treatment planning. A common theme is the modelling of constraints on dosimetric indices, each of which is a restriction on the portion of a volume that receives at least a specified dose, or on the lowest dose that is received by a portion of a volume. Modelling dosimetric indices explicitly yields mixed-integer programs which are computationally demanding to solve. We have therefore investigated approximations of dosimetric indices, for example using smooth non-linear functions or convex functions. Contributions of this thesis are also a literature review of proposed treatment planning models for HDR BT, including mathematical analyses and comparisons of models, and a study of treatment planning for IMBT, which shows how robust optimization can be used to mitigate the risks fro...

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A new optimization algorithm for HDR brachytherapy that improves DVH-based planning: Truncated Conditional Value-at-Risk (TCVaR)

Cited by 4 publications

References 40 publications

Direct incorporation of patient‐specific efficacy and toxicity estimates in radiation therapy plan optimization

Direct incorporation of patient‐specific efficacy and toxicity estimates in radiation therapy plan optimization

Emerging technologies in brachytherapy

Treatment Planning of High Dose-Rate Brachytherapy - Mathematical Modelling and Optimization

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