A systematically compiled set of quantitative metrics to describe spatial characteristics of radiotherapy dose distributions and aid in treatment planning

A data-driven approach to model spatial dose characteristics for catheter placement of high dose-rate brachytherapy for prostate cancer

BACKGROUND

High dose rate brachytherapy (HDR BT) is a common treatment modality for cancer. In HDR BT, a radioactive source is placed inside or close to a tumor, aiming to give a high enough dose to the tumor, while sparing nearby healthy tissue and organs at risk. Treatment planning of HDR BT for prostate cancer consists of two types of decisions, placement of catheters, modeled with binary variables, and dwell times, modeled with continuous non-negative variables. Optimal spatial placement of catheters is important for avoiding local recurrence and complications, but such characteristics have not been modeled for the combined treatment planning problem of catheter placement and dwell time optimization.

METHOD

We propose a data-driven approach using linear regression, mutual information, and random forests to find convex estimates of spatial dose characteristics that correlate well with contiguous volumes receiving a too-high (hot spots) or too-low dose (cold spots). These estimates were incorporated in retrospective treatment plan optimization of 28 prostate cancer patients.

RESULTS

The proposed hot-spot terms reduced the volume receiving twice the prescribed dose by 29% at 14 catheters. Also, the results illustrate the trade-offs between the number of catheters and spatial dose characteristics.

CONCLUSIONS

Our study demonstrates that incorporating a term for hot spots in the objective function of the treatment planning model is more effective in reducing hot spots than catheter placements that are not optimized for hot spots.

Definition of a framework for volumetric modulated arc therapy plan quality assessment with integration of dose-, complexity-, and robustness metrics

Background and purpose

Conventionally, the quality of radiotherapy treatment plans is assessed through visual inspection of dose distributions and dose-volume histograms. This study developed a framework to evaluate plan quality using dose, complexity, and robustness metrics. Additionally, a method for predicting plan robustness metrics using dose and complexity metrics was introduced for cases where plan robustness evaluation is unavailable or impractical.

Materials and methods

The framework and prediction models were developed and validated using 103-bronchial Volumetric Modulated Arc Therapy (VMAT)-plans. The application of the framework was demonstrated using 25-VMAT-plans. To identify significant metrics for plan evaluation, 122-metrics were analysed and narrowed down using multivariate Spearman correlation. Metric limits were set with Statistical process control (SPC). Robustness metrics were predicted using multivariable or single linear regression models based on dose-and complexity-metrics.

Results

Twenty-five-metrics were selected based on the amount and strength of correlations. R95(dose coverage) and HI95/5(homogeneity index) stood out among the dose-metrics, while the complexity-metrics showed similar correlations. Average scenarios dose at 95 % Clinical Target Volume D95mean(CTV) and Errorbar-based Volume-Histograms (EVH) were notable for robustness metrics. Approximately 99 % of evaluated metrics fell within established SPC limits. The prediction model for D95mean(CTV) showed good performance (adjusted R2 = 0.88, mean squared error (MSE) = 3.84 × 10-6), while the model for EVH demonstrated moderate reliability (adjusted R2 = 0.52, MSE = 0.2). No statistically significant differences were found between the predicted (using dose-and complexity-metrics) and calculated robustness metrics (EVH (p-value = 0.9) and D95mean(CTV) (p-value = 1)).

Conclusions

The developed framework enables early detection of sub-optimal, complex and non-robust treatment plans. The predictive model can be used when robustness evaluations are impractical.

Effect of different optimization parameters in single isocenter multiple brain metastases radiosurgery

PURPOSE

Automated treatment planning for multiple brain metastases differs from traditional planning approaches. It is therefore helpful to understand which parameters for optimization are available and how they affect the plan quality. This study aims to provide a reference for designing multi-metastases treatment plans and to define quality endpoints for benchmarking the technique from a scientific perspective.

METHODS

In all, 20 patients with a total of 183 lesions were retrospectively planned according to four optimization scenarios. Plan quality was evaluated using common plan quality parameters such as conformity index, gradient index and dose to normal tissue. Therefore, different scenarios with combinations of optimization parameters were evaluated, while taking into account dependence on the number of treated lesions as well as influence of different beams.

RESULTS

Different scenarios resulted in minor differences in plan quality. With increasing number of lesions, the number of monitor units increased, so did the dose to healthy tissue and the number of interlesional dose bridging in adjacent metastases. Highly modulated cases resulted in 4-10% higher V10% compared to less complex cases, while monitor units did not increase. Changing the energy to a flattening filter free (FFF) beam resulted in lower local V12Gy (whole brain-PTV) and even though the number of monitor units increased by 13-15%, on average 46% shorter treatment times were achieved.

CONCLUSION

Although no clinically relevant differences in parameters where found, we identified some variation in the dose distributions of the different scenarios. Less complex scenarios generated visually more dose overlap; therefore, a more complex scenario may be preferred although differences in the quality metrics appear minor.

Organ at risk dose-volume metrics in a series of hypofractionated breast radiotherapy with integrated boost

Dose and volume metrics to organs at risk are used for evaluation and optimization in radiotherapy planning. However, the numerous choices of metrics can be confusing. In a series of patients treated with hypofractionation and an integrated boost for breast cancer, we aim to determine if a parsimonious selection of representative metrics can be identified. The dosimetries of 42 patients receiving 42 Gy to the breast, with or without nodal irradiation, and 51 Gy integrated boost to tumor bed in 15 fractions were reviewed. For each organ-heart, lungs, and contralateral breast-cumulative dose-volume histograms were used to extract values for 3 basic metric classes: Two additional classes were considered: Pearson correlation coefficient R was calculated between pairs of values within each basic class and with the 2 additional classes for each organ. The interquartile ranges of correlations for D.yy, Vrel.xx, and Vabs.xx were as follows: The mean dose correlated with all basic classes for the heart and lungs, and with dose D.yy and volumes at Vrel.10-Vabs.10 for the contralateral breast. The standard deviation correlated with Vrel.xx and Vabs.xx for the heart and lungs (R ≥ 0.70). Among the D.yy, D.50 (median dose) correlated with the mean and standard deviation for all organs (R = 0.65-0.96). The mean, standard deviation, and median doses were the preeminent correlators. These statistics appear to be parsimonious representatives of doses to organs. Further studies with other radiotherapy series will be necessary to validate these observations.

Penalty weight tuning in high dose rate brachytherapy using multi-objective Bayesian optimization

Objective.Treatment plan optimization in high dose rate brachytherapy often requires manual fine-tuning of penalty weights for each objective, which can be time-consuming and dependent on the planner's experience. To automate this process, this study used a multi-criteria approach called multi-objective Bayesian optimization with q-noisy expected hypervolume improvement as its acquisition function (MOBO-qNEHVI).Approach.The treatment plans of 13 prostate cancer patients were retrospectively imported to a research treatment planning system, RapidBrachyMTPS, where fast mixed integer optimization (FMIO) performs dwell time optimization given a set of penalty weights to deliver 15 Gy to the target volume. MOBO-qNEHVI was used to find patient-specific Pareto optimal penalty weight vectors that yield clinically acceptable dose volume histogram metrics. The relationship between the number of MOBO-qNEHVI iterations and the number of clinically acceptable plans per patient (acceptance rate) was investigated. The performance time was obtained for various parameter configurations.Main results.MOBO-qNEHVI found clinically acceptable treatment plans for all patients. With increasing the number of MOBO-qNEHVI iterations, the acceptance rate grew logarithmically while the performance time grew exponentially. Fixing the penalty weight of the tumour volume to maximum value, adding the target dose as a parameter, initiating MOBO-qNEHVI with 25 parallel sampling of FMIO, and running 6 MOBO-qNEHVI iterations found solutions that delivered 15 Gy to the hottest 95% of the clinical target volume while respecting the dose constraints to the organs at risk. The average acceptance rate for each patient was 89.74% ± 8.11%, and performance time was 66.6 ± 12.6 s. The initiation took 22.47 ± 7.57 s, and each iteration took 7.35 ± 2.45 s to find one Pareto solution.Significance.MOBO-qNEHVI combined with FMIO can automatically explore the trade-offs between treatment plan objectives in a patient specific manner within a minute. This approach can reduce the dependency of plan quality on planner's experience and reduce dose to the organs at risk.

Espadon, an R package for automation, exploitation and processing of DICOM files in medical physics and clinical research

INTRODUCTION

One of the main issues in the field of clinical research is to enhance clinical databases with information from imaging (CT, MR, PET-scan), contouring (RTstruct), or produced by TPS such as dose distribution (RTdose) or treatment plans (RTplan). To perform these analyses automatically, we propose the new open-source package "espadon", developed in R environment. This package also opens up numerous perspectives for TPS-independant calculation, automation and processing of DICOM data.

RESULTS

The espadon package converts DICOM objects into espadon objects. Several tools have been developed to manipulate these objects and extract the desired information. In addition to decode DICOM files and pseudonomize them, the great advantage of espadon is that it presents the links between patient data (images, structures, treatment plans) in a didactic way, respecting the dates of the examinations. It can visualize volumes or structures in 2D or 3D, resample volumes, segment them, and change geometric frames of reference. It integrates dose-volume histogram functions on a selection, with Monte Carlo calculations of random shifts of contours. It offers the automatic calculation of several usual radiotherapy indices, as well as the calculation of Gamma and Chi indices.

CONCLUSIONS

Espadon is a toolkit designed to be easily used by radiotherapists, medical physicists or students. Espadon's functions are implemented in an R script, and allow the automatic extraction or calculation of data from DICOM files, which can be used for statistical modelling or machine-learning in the R environment. This package is available on the Comprehensive R Archive Network (CRAN) repository.

Automated Contouring and Planning in Radiation Therapy: What Is 'Clinically Acceptable'?

Developers and users of artificial-intelligence-based tools for automatic contouring and treatment planning in radiotherapy are expected to assess clinical acceptability of these tools. However, what is 'clinical acceptability'? Quantitative and qualitative approaches have been used to assess this ill-defined concept, all of which have advantages and disadvantages or limitations. The approach chosen may depend on the goal of the study as well as on available resources. In this paper, we discuss various aspects of 'clinical acceptability' and how they can move us toward a standard for defining clinical acceptability of new autocontouring and planning tools.

Plan quality assessment in clinical practice: Results of the 2020 ESTRO survey on plan complexity and robustness

PURPOSE

Plan complexity and robustness are two essential aspects of treatment plan quality but there is a great variability in their management in clinical practice. This study reports the results of the 2020 ESTRO survey on plan complexity and robustness to identify needs and guide future discussions and consensus.

METHODS

A survey was distributed online to ESTRO members. Plan complexity was defined as the modulation of machine parameters and increased uncertainty in dose calculation and delivery. Robustness was defined as a dose distribution's sensitivity towards errors stemming from treatment uncertainties, patient setup, or anatomical changes.

RESULTS

A total of 126 radiotherapy centres from 33 countries participated, 95 of them (75%) from Europe and Central Asia. The majority controlled and evaluated plan complexity using monitor units (56 centres) and aperture shapes (38 centres). To control robustness, 98 (97% of question responses) photon and 5 (50%) proton centres used PTV margins for plan optimization while 75 (94%) and 5 (50%), respectively, used margins for plan evaluation. Seventeen (21%) photon and 8 (80%) proton centres used robust optimisation, while 10 (13%) and 8 (80%), respectively, used robust evaluation. Primary uncertainties considered were patient setup (photons and protons) and range calculation uncertainties (protons). Participants expressed the need for improved commercial tools to control and evaluate plan complexity and robustness.

CONCLUSION

Clinical implementation of methods to control and evaluate plan complexity and robustness is very heterogeneous. Better tools are needed to manage complexity and robustness in treatment planning systems. International guidelines may promote harmonization.