A model for prostate brachytherapy planning with sources and needles position optimization

Ferrari, Germán; Kazareski, Yoel; Laca, Federico; Testuri, Carlos E.

doi:10.1016/j.orhc.2013.12.002

Cited by 10 publications

(3 citation statements)

References 12 publications

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“…After implementation, the numbers of patients suffering side effects was substantially reduced, and procedures were shortened since fewer seeds were implanted. Ferrari, Kazareski, Laca, and Testuri (2014) build on Lee and Zaider's (2008) models and other models optimising the positioning of radioactive seeds. They add in extra constraints, for example constraints relating to the needles that position the seeds.…”

Section: Radiotherapy Treatment Planningmentioning

confidence: 99%

“…Holder and LLagostera (2008) model how best to apply photodynamic therapy to deep tissue cancers, and assess whether the only drug approved in the USA for this treatment at the time would yield acceptable results. Tejada et al (2014), Arrospide et al (2015), Madadi et al (2015), Wang and Zhang (2017) and Deviations from prescribed radiation doses in tumour, surrounding organs and normal tissue Lee and Zaider (2008) and Ferrari et al (2014) Number of needles, number of radioactive seeds They develop biological models of the concentration of the drug in different tissues, and the rate at which cellular damage occurs when the drug is activated with light. Their linear model to optimise the application of the light source is adapted from a model for external radiotherapy planning.…”

Section: External Radiotherapy: Intensity Modulated Radiation Therapymentioning

confidence: 99%

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Operational research techniques applied throughout cancer care services: a review

2018

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Cancer is a disease affecting increasing numbers of people. In the UK, the proportion of people affected by cancer is projected to increase from 1 in 3 in 1992, to nearly 1 in 2 by 2020. Health services to tackle cancer can be grouped broadly into prevention, diagnosis, staging, and treatment. We review examples of Operational Research (OR) papers addressing decisions encountered in each of these areas. In conclusion, we find many examples of OR research on screening strategies, as well as on treatment planning and scheduling. On the other hand, our search strategy uncovered comparatively few examples of OR models applied to reducing cancer risks, optimising diagnostic procedures, and staging. Improvements to cancer care services have been made as a result of successful OR modelling. There is potential for closer working with clinicians to enable the impact of other OR studies to be of greater benefit to cancer sufferers. ARTICLE HISTORY

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Section: Radiotherapy Treatment Planningmentioning

confidence: 99%

Section: External Radiotherapy: Intensity Modulated Radiation Therapymentioning

confidence: 99%

Operational research techniques applied throughout cancer care services: a review

2018

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“…Common methods focus on meeting the dose-volume criteria with limited constraints on the needle or seed locations (which can determine if a plan is implantable or not). These include various optimization techniques such as: mixed-integer linear programming [2], inverse treatment planning with compressed sensing [5], genetic algorithms [3], and fast simulated annealing [12]. Most of these techniques are very sensitive to their initialization, and the associated optimization costs have multiple minima and a huge search space.…”

Section: Introductionmentioning

confidence: 99%

Rapid treatment planning for low-dose-rate prostate brachytherapy with TP-GAN

Aleef,

Spadinger,

Peacock

et al. 2021

Preprint

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Treatment planning in low-dose-rate prostate brachytherapy (LDR-PB) aims to produce arrangement of implantable radioactive seeds that deliver a minimum prescribed dose to the prostate whilst minimizing toxicity to healthy tissues. There can be multiple seed arrangements that satisfy this dosimetric criterion, not all deemed 'acceptable' for implant from a physician's perspective. This leads to plans that are subjective to the physician's/centre's preference, planning style, and expertise. We propose a method that aims to reduce this variability by training a model to learn from a large pool of successful retrospective LDR-PB data (961 patients) and create consistent plans that mimic the high-quality manual plans. Our model is based on conditional generative adversarial networks that use a novel loss function for penalizing the model on spatial constraints of the seeds. An optional optimizer based on a simulated annealing (SA) algorithm can be used to further fine-tune the plans if necessary (determined by the treating physician). Performance analysis was conducted on 150 test cases demonstrating comparable results to that of the manual prehistorical plans. On average, the clinical target volume covering 100% of the prescribed dose was 98.9% for our method compared to 99.4% for manual plans. Moreover, using our model, the planning time was significantly reduced to an average of 2.5 mins/plan with SA, and less than 3 seconds without SA. Compared to this, manual planning at our centre takes around 20 mins/plan.

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