An open-source genetic algorithm for determining optimal seed distributions for low-dose-rate prostate brachytherapy

Knowledge-based inverse treatment planning for low-dose-rate prostate brachytherapy

PURPOSE

Permanent low-dose-rate brachytherapy is a widely used treatment modality for managing prostate cancer. In such interventions, treatment planning can be a challenging task and requires experience and skills of the planner. We developed a novel knowledge-based (KB) optimization method based on previous treatment plans. The purpose of this method was to generate clinically acceptable plans that do not require extensive manual adjustments in clinical scenarios.

METHODS

Objective functions used in current inverse planning methods are preferably based on spatial invariant dose objectives rather than spatial dose distributions. Therefore, they are prone to return suboptimal plans resulting in time consuming plan adjustments. To overcome this limitation, a KB approach is introduced. The KB model uses the dose distributions of previous clinical plans projected onto a standardized geometry. From those standardized distributions a template plan is generated. The treatment plans were optimized with an in-house developed planning system by solving a constraint inverse optimization problem that mimics the projected template dose plan constraint to DVH metrics. The method is benchmarked under an IRB-approved retrospective study by comparing optimization time, dosimetric performance, and clinical acceptability against current clinical practice. The quality of the KB model is evaluated with a Turing test.

RESULTS

The KB model consists of five high-quality treatment plans. Those plans were selected by one of our experts and showed all desired dosimetric features. After generating the model treatment plans were created with one run of the optimizer for the remaining 20 patients. The optimization time including needle optimization ranged from 6 to 29 s. Based on a Wilcoxon signed rank test the new plans are dosimetrically equivalent to current clinical practice. The Turing test showed that the proposed method generates plans that are equivalent to current clinical practice and that the dose prediction drives the optimization to achieve high-quality treatment plans.

CONCLUSIONS

This study demonstrated that the proposed KB model was able to capture user-specific features in isodose lines which can be used to generate acceptable treatment plans with a single run of the optimization engine in under a minute. This could potentially reduce the time in the operating room and the time a patient is under anesthesia.

Hybrid optimization based on non-coplanar needles for brachytherapy dose planning

Purpose

An ideal dose distribution in a target is the ultimate goal of preoperative dose planning. Furthermore, avoiding vital organs or tissues such as blood vessels or bones during the puncture procedure is significant in low-dose-rate brachytherapy. The aim of this work is to develop a hybrid inverse optimization method based on non-coplanar needles to assist the physician during conformal dose planning, which cannot be properly achieved with a traditional coplanar template.

Material and methods

The hybrid inverse optimization technique include two novel technologies: an inverse optimization algorithm and a dose volume histogram evaluation method. Brachytherapy treatment planning system was designed as an experimental platform. Left lung adenocarcinoma case was used to test the performance of the method in non-coplanar and coplanar needles, and malignant tumor of spine case was involved to test the practical application of this technique. In addition, the optimization time of every test was also recorded.

Results

The proposed method can achieve an ideal dose distribution, avoiding vital organs (bones). In the first experiment, 13 non-coplanar needles and 24 seeds were used to get an ideal dose distribution to cover the target, whereas 11 coplanar needles and 23 seeds were used to cover the same target. In the second experiment, the new method used 22 non-coplanar needles and 65 seeds to cover the target, while 63 seeds and 22 needles were used in the actual operation. In addition, the computation time of the hybrid inverse optimization method was 20.5 seconds in the tumor of 94.67 cm³ by using 22 needles, which was fast enough for clinical application.

Conclusions

The hybrid inverse optimization method achieved high conformity in the clinical practice. The non-coplanar needle can help to achieve a better dose distribution than the coplanar needle.

A radiobiology-based inverse treatment planning method for optimisation of permanent l-125 prostate implants in focal brachytherapy

Treatment plans for ten patients, initially treated with a conventional approach to low dose-rate brachytherapy (LDR, 145 Gy to entire prostate), were compared with plans for the same patients created with an inverse-optimisation planning process utilising a biologically-based objective. The 'biological optimisation' considered a non-uniform distribution of tumour cell density through the prostate based on known and expected locations of the tumour. Using dose planning-objectives derived from our previous biological-model validation study, the volume of the urethra receiving 125% of the conventional prescription (145 Gy) was reduced from a median value of 64% to less than 8% whilst maintaining high values of TCP. On average, the number of planned seeds was reduced from 85 to less than 75. The robustness of plans to random seed displacements needs to be carefully considered when using contemporary seed placement techniques. We conclude that an inverse planning approach to LDR treatments, based on a biological objective, has the potential to maintain high rates of tumour control whilst minimising dose to healthy tissue. In future, the radiobiological model will be informed using multi-parametric MRI to provide a personalised medicine approach.