Impact of machine log-files uncertainties on the quality assurance of proton pencil beam scanning treatment delivery

Particle arc therapy: Status and potential

There is a rising interest in developing and utilizing arc delivery techniques with charged particle beams, e.g., proton, carbon or other ions, for clinical implementation. In this work, perspectives from the European Society for Radiotherapy and Oncology (ESTRO) 2022 physics workshop on particle arc therapy are reported. This outlook provides an outline and prospective vision for the path forward to clinically deliverable proton, carbon, and other ion arc treatments. Through the collaboration among industry, academic, and clinical research and development, the scientific landscape and outlook for particle arc therapy are presented here to help our community understand the physics, radiobiology, and clinical principles. The work is presented in three main sections: (i) treatment planning, (ii) treatment delivery, and (iii) clinical outlook.

An artificial neural network based approach for predicting the proton beam spot dosimetric characteristics of a pencil beam scanning technique

Utilising Machine Learning (ML) models to predict dosimetric parameters in pencil beam scanning proton therapy presents a promising and practical approach. The study developed Artificial Neural Network (ANN) models to predict proton beam spot size and relative positional errors using 9000 proton spot data. The irradiation log files as input variables and corresponding scintillation detector measurements as the label values. The ANN models were developed to predict six variables: spot size in thex-axis,y-axis, major axis, minor axis, and relative positional errors in thex-axis andy-axis. All ANN models used a Multi-layer perception (MLP) network using one input layer, three hidden layers, and one output layer. Model performance was validated using various statistical tools. The log file recorded spot size and relative positional errors, which were compared with scintillator-measured data. The Root Mean Squared Error (RMSE) values for the x-spot and y-spot sizes were 0.356 mm and 0.362 mm, respectively. Additionally, the maximum variation for the x-spot relative positional error was 0.910 mm, while for the y-spot, it was 1.610 mm. The ANN models exhibit lower prediction errors. Specifically, the RMSE values for spot size prediction in the x, y, major, and minor axes are 0.053 mm, 0.049 mm, 0.053 mm, and 0.052 mm, respectively. Additionally, the relative spot positional error prediction model for the x and y axes yielded maximum errors of 0.160 mm and 0.170 mm, respectively. The normality of models was validated using the residual histogram and Q-Q plot. The data over fit, and bias were tested using K (k = 5) fold cross-validation, and the maximum RMSE value of the K fold cross-validation among all the six ML models was less than 0.150 mm (R-Square 0.960). All the models showed excellent prediction accuracy. Accurately predicting beam spot size and positional errors enhances efficiency in routine dosimetric checks.

Ion recombination correction factors and detector comparison in a very-high dose rate proton scanning beam

PURPOSE

Accurate dosimetry is paramount to study the FLASH biological effect since dose and dose rate are critical dosimetric parameters governing its underlying mechanisms. With the goal of assessing the suitability of standard clinical dosimeters in a very-high dose rate (VHDR) experimental setup, we evaluated the ion collection efficiency of several commercially available air-vented ionization chambers (IC) in conventional and VHDR proton irradiation conditions.

METHODS

A cyclotron at the Orsay Proton Therapy Center was used to deliver VHDR pencil beam scanning irradiation. Ion recombination correction factors (k_s) were determined for several detectors (Advanced Markus, PPC05, Nano Razor, CC01) at the entrance of the plateau and at the Bragg peak, using the Niatel model, the Two-voltage method and Boag's analytical formula for continuous beams.

RESULTS

Mean dose rates ranged from 4 Gy/s to 385 Gy/s, and instantaneous dose rates up to 1000 Gy/s were obtained with the experimental set-up. Recombination correction factors below 2 % were obtained for all chambers, except for the Nano Razor, at VHDRs with variations among detectors, while k_s values were significantly smaller (0.8 %) for conventional dose rates.

CONCLUSIONS

While the collection efficiency of the probed ICs in scanned VHDR proton therapy is comparable to those in the conventional regime with recombination coefficiens smaller than 1 % for mean dose rates up to 177 Gy/s, the reduction in collection efficiency for higher dose rates cannot be ignored when measuring the absorbed dose in pre-clinical proton scanned FLASH experiments and clinical trials.

SWFT-Net: a deep learning framework for efficient fine-tuning spot weights towards adaptive proton therapy

Objective. One critical task for adaptive proton therapy is how to perform spot weight re-tuning and reoptimize plan, both of which are time-consuming and labor intensive. We proposed a deep learning framework (SWFT-Net) to speed up such a task, a starting point for us to move towards online adaptive proton therapy.Approach. For a H&N patient case, a reference intensity modulated proton therapy plan was generated. For data augmentation, spot weights were modified to generate three datasets (DS10, DS30, DS50), corresponding to different levels of weight adjustment. For each dataset, the samples were split into the training and testing groups at a ratio of 8:2 (6400 for training, 1706 for testing). To ease the difficulty of machine learning, the residuals of dose maps and spot weights (i.e. difference relative to a reference) were used as inputs and outputs, respectively. Quantitative analyses were performed in terms of normalized root mean square error (NRMSE) of spot weights, Gamma passing rate and dose difference within the PTV.Main results. The SWFT-Net is able to generate an adapted plan in less than a second with a NVIDIA GeForce RTX 3090 GPU. For the 1706 samples in the testing dataset, the NRMSE is 0.41% (DS10), 1.05% (DS30) and 2.04% (DS50), respectively. Cold/hot spots in the dose maps after adaptation are observed. The mean relative dose difference is 0.64% (DS10), 0.92% (DS30) and 0.88% (DS50), respectively. For all three datasets, the mean Gamma passing rate is consistently over 95% for both 1 mm/1% and 3 mm/3% settings.Significance. The proposed SWFT-Net is a promising tool to help realize adaptive proton therapy. It can be used as an alternative tool to other spot fine-tuning optimization algorithms, likely demonstrating superior performance in terms of speed, accuracy, robustness and minimum human interaction. This study lays down a foundation for us to move further incorporating other factors such as daily anatomical changes and propagated PTVs, and develop a truly online adaptive workflow in proton therapy.

Evaluation of the clinical value of automatic online dose restoration for adaptive proton therapy of head and neck cancer

INTRODUCTION

Intensity modulated proton therapy (IMPT) is highly sensitive to anatomical variations which can cause inadequate target coverage during treatment. This study compares not-adapted (NA) robust plans to two adaptive IMPT methods - a fully-offline adaptive (FOA) and a simplified automatic online adaptive strategy (dose restoration (DR)) to determine the benefit of DR, in head and neck cancer (HNC).

MATERIAL/METHODS

Robustly optimized clinical IMPT doses in planning-CTs (pCTs) were available for a cohort of 10 HNC patients. During robust re-optimization, DR used isodose contours, generated from the clinical dose on pCTs, and patient specific objectives to reproduce the clinical dose in every repeated-CT(rCT). For each rCT(n=50), NA, DR and FOA plans were robustly evaluated.

RESULTS

An improvement in DVH-metrics and robustness was seen for DR and FOA plans compared to NA plans. For NA plans, 74%(37/50) of rCTs did not fulfill the CTV coverage criteria (D98%>95%Dprescription). DR improved target coverage, target homogeneity and variability on critical risk organs such as the spinal cord. After DR, 52%(26/50) of rCTs met all clinical goals. Because of large anatomical changes and/or inaccurate patient repositioning, 48%(24/50) of rCTs still needed full offline adaptation to ensure an optimal treatment since dose restoration was not able to re-establish the initial plan quality.

CONCLUSION

Robust optimization together with fully-automatized DR avoided offline adaptation in 52% of the cases. Implementation of dose restoration in clinical routine could ensure treatment plan optimality while saving valuable human and material resources to radiotherapy departments.

Adaptive proton therapy

Radiation therapy treatments are typically planned based on a single image set, assuming that the patient's anatomy and its position relative to the delivery system remains constant during the course of treatment. Similarly, the prescription dose assumes constant biological dose-response over the treatment course. However, variations can and do occur on multiple time scales. For treatment sites with significant intra-fractional motion, geometric changes happen over seconds or minutes, while biological considerations change over days or weeks. At an intermediate timescale, geometric changes occur between daily treatment fractions. Adaptive radiation therapy is applied to consider changes in patient anatomy during the course of fractionated treatment delivery. While traditionally adaptation has been done off-line with replanning based on new CT images, online treatment adaptation based on on-board imaging has gained momentum in recent years due to advanced imaging techniques combined with treatment delivery systems. Adaptation is particularly important in proton therapy where small changes in patient anatomy can lead to significant dose perturbations due to the dose conformality and finite range of proton beams. This review summarizes the current state-of-the-art of on-line adaptive proton therapy and identifies areas requiring further research.

A machine learning-based framework for delivery error prediction in proton pencil beam scanning using irradiation log-files

PURPOSE

This study aims to investigate the use of machine learning models for delivery error prediction in proton pencil beam scanning (PBS) delivery.

METHODS

A dataset of planned and delivered PBS spot parameters was generated from a set of 20 prostate patient treatments. Planned spot parameters (spot position, MU and energy) were extracted from the treatment planning system (TPS) for each beam. Delivered spot parameters were extracted from irradiation log-files for each beam delivery following treatment. The dataset was used as a training dataset for three machine learning models which were trained to predict delivered spot parameters based on planned parameters. K-fold cross validation was employed for hyper-parameter tuning and model selection where the mean absolute error (MAE) was used as the model evaluation metric. The model with lowest MAE was then selected to generate a predicted dose distribution for a test prostate patient within a commercial TPS.

RESULTS

Analysis of the spot position delivery error between planned and delivered values resulted in standard deviations of 0.39 mm and 0.44 mm for x and y spot positions respectively. Prediction error standard deviation values of spot positions using the selected model were 0.22 mm and 0.11 mm for x and y spot positions respectively. Finally, a three-way comparison of dose distributions and DVH values for select OARs indicates that the random-forest-predicted dose distribution within the test prostate patient was in closer agreement to the delivered dose distribution than the planned distribution.

CONCLUSIONS

PBS delivery error can be accurately predicted using machine learning techniques.

Update on yesterday's dose-Use of delivery log-files for daily adaptive proton therapy (DAPT)

In daily adaptive proton therapy (DAPT), the treatment plan is re-optimized on a daily basis. It is a straightforward idea to incorporate information from the previous deliveries during the optimization to refine this daily proton delivery. A feedback signal was used to correct for delivery errors and errors from an inaccurate dose calculation used for plan optimization. This feedback signal consisted of a dose distribution calculated with a Monte Carlo algorithm and was based on the spot delivery information from the previous deliveries in the form of log-files. We therefore called the method Update On Yesterday's Dose (UYD). The UYD method was first tested with a simulated DAPT treatment and second with dose measurements using an anthropomorphic phantom. For both, the simulations and the measurements, a better agreement between the delivered and the intended dose distribution could be observed using UYD. Gamma pass rates (1%/1 mm) increased from around 75% to above 90%, when applying the closed-loop correction for the simulations, as well as the measurements. For a DAPT treatment, positioning errors or anatomical changes are incorporated during the optimization and therefore are less dominant in the overall dose uncertainty. Hence, the relevance of algorithm or delivery machine errors even increases compared to standard therapy. The closed-loop process described here is a method to correct for these errors, and potentially further improve DAPT.

Compact Method for Proton Range Verification Based on Coaxial Prompt Gamma-Ray Monitoring: a Theoretical Study

Range uncertainties in proton therapy hamper treatment precision. Prompt gamma-rays were suggested 16 years ago for real-time range verification, and have already shown promising results in clinical studies with collimated cameras. Simultaneously, alternative imaging concepts without collimation are investigated to reduce the footprint and price of current prototypes. In this manuscript, a compact range verification method is presented. It monitors prompt gamma-rays with a single scintillation detector positioned coaxially to the beam and behind the patient. Thanks to the solid angle effect, proton range deviations can be derived from changes in the number of gamma-rays detected per proton, provided that the number of incident protons is well known. A theoretical background is formulated and the requirements for a future proof-of-principle experiment are identified. The potential benefits and disadvantages of the method are discussed, and the prospects and potential obstacles for its use during patient treatments are assessed. The final milestone is to monitor proton range differences in clinical cases with a statistical precision of 1 mm, a material cost of 25000 USD and a weight below 10 kg. This technique could facilitate the widespread application of in vivo range verification in proton therapy and eventually the improvement of treatment quality.

Online daily adaptive proton therapy

It is recognized that the use of a single plan calculated on an image acquired some time before the treatment is generally insufficient to accurately represent the daily dose to the target and to the organs at risk. This is particularly true for protons, due to the physical finite range. Although this characteristic enables the generation of steep dose gradients, which is essential for highly conformal radiotherapy, it also tightens the dependency of the delivered dose to the range accuracy. In particular, the use of an outdated patient anatomy is one of the most significant sources of range inaccuracy, thus affecting the quality of the planned dose distribution. A plan should be ideally adapted as soon as anatomical variations occur, ideally online. In this review, we describe in detail the different steps of the adaptive workflow and discuss the challenges and corresponding state-of-the art developments in particular for an online adaptive strategy.