Survey of patient-specific quality assurance practice for IMRT and VMAT.

A first-time survey across 15 cancer centers in Ontario, Canada, on the current practice of patient-specific quality assurance (PSQA) for intensity modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) delivery was conducted. The objectives were to assess the current state of PSQA practice, identify areas for potential improvement, and facilitate the continued improvement in standardization, consistency, efficacy, and efficiency of PSQA regionally. The survey asked 40 questions related to PSQA practice for IMRT/VMAT delivery. The questions addressed PSQA policy and procedure, delivery log evaluation, instrumentation, measurement setup and methodology, data analysis and interpretation, documentation, process, failure modes, and feedback. The focus of this survey was on PSQA activities related to routine IMRT/VMAT treatments on conventional linacs, including stereotactic body radiation therapy but excluding stereotactic radiosurgery. The participating centers were instructed to submit answers that reflected the collective view or opinion of their department and represented the most typical process practiced. The results of the survey provided a snapshot of the current state of PSQA practice in Ontario and demonstrated considerable variations in the practice. A large majority (80%) of centers performed PSQA measurements on all VMAT plans. Most employed pseudo-3D array detectors with a true composite (TC) geometry. No standard approach was found for stopping or reducing frequency of measurements. The sole use of delivery log evaluation was not widely implemented, though most centers expressed interest in adopting this technology. All used the Gamma evaluation method for analyzing PSQA measurements; however, no universal approach was reported on how Gamma evaluation and pass determination criteria were determined. All or some PSQA results were reviewed regularly in two-thirds of the centers. Planning related issues were considered the most frequent source for PSQA failures (40%), whereas the most frequent course of action for a failed PSQA was to review the result and decide whether to proceed to treatment.

Automated plan generation for prostate radiotherapy patients using deep learning and scripted optimization.

Background and Purpose: Treatment planning is a time-intensive task that could be automated. We aimed to develop a "single-click" workflow, fully deployed within a commercial treatment planning system (TPS), for autoplanning prostate radiotherapy treatment plans using predictions from a deep learning model (DLM). Materials and Methods: Automatically generated treatment plans were created with a single script, executed from within a commercial TPS scripting environment, that performed two stages sequentially. Initially, a 3D dose distribution was predicted with a ResUNet DLM. The DLM was trained and validated using previously treated datasets (n = 120) which used 3D contours as inputs. Following this, dose predictions were converted into treatment plans by extracting dose-volume metrics from the predictions to use as objectives for the inverse optimizer within the TPS. An independent test dataset (n = 20) was used to evaluate the similarity between automated and clinical plans. Results: For planning target volumes, the median percentage difference and interquartile range between the automatically generated plans and clinical plans were 0.4% [0.2-1.1%] for the V100%, -0.5% [(-1.0)-(-0.2)%] for D99% and -0.5% [(-1.0)-(-0.2)%] for D95%. Bladder and rectum volume-at-dose objectives agreed within -6.1% [(-12.5)-0.9%]. The conversion of the DLM prediction into a treatment plan took 15 min [13-16 min]. Conclusions: An automatic plan generation workflow that uses a DL model with scripted optimization was fully deployed in a commercial TPS. Autoplans were compared to previously treated clinical plans and were found to be non-inferior.

A Pilot Clinical and Technical Validation of an Immersive Virtual Reality Platform for 3D Anatomical Modeling and Contouring in Support of Surgical and Radiation Oncology Treatment Planning.

The aim of this study was to validate a novel medical virtual reality (VR) platform used for medical image segmentation and contouring in radiation oncology and 3D anatomical modeling and simulation for planning medical interventions, including surgery. The first step of the validation was to verify quantitatively and qualitatively that the VR platform can produce substantially equivalent 3D anatomical models, image contours, and measurements to those generated with existing commercial platforms. To achieve this, a total of eight image sets and 18 structures were segmented using both VR and reference commercial platforms. The image sets were chosen to cover a broad range of scanner manufacturers, modalities, and voxel dimensions. The second step consisted of evaluating whether the VR platform could provide efficiency improvements for target delineation in radiation oncology planning. To assess this, the image sets for five pediatric patients with resected standard-risk medulloblastoma were used to contour target volumes in support of treatment planning of craniospinal irradiation, requiring complete inclusion of the entire cerebral-spinal volume. Structures generated in the VR and the commercial platforms were found to have a high degree of similarity, with dice similarity coefficient ranging from 0.963 to 0.985 for high-resolution images and 0.920 to 0.990 for lower resolution images. Volume, cross-sectional area, and length measurements were also found to be in agreement with reference values derived from a commercial system, with length measurements having a maximum difference of 0.22 mm, angle measurements having a maximum difference of 0.04°, and cross-sectional area measurements having a maximum difference of 0.16 mm2. The VR platform was also found to yield significant efficiency improvements, reducing the time required to delineate complex cranial and spinal target volumes by an average of 50% or 29 min.

Predicting VMAT patient-specific QA results using a support vector classifier trained on treatment plan characteristics and linac QC metrics.

The use of treatment plan characteristics to predict patient-specific quality assurance (QA) measurement results has recently been reported as a strategy to help facilitate automated pre-treatment verification workflows or to provide a virtual assessment of delivery quality. The goal of this work is to investigate the potential of using treatment plan characteristics and linac performance metrics (i.e. quality control test results) in combination with machine learning techniques to predict the results of VMAT patient-specific QA measurements. Using features that describe treatment plan complexity and linac performance metrics, we trained a linear support vector classifier (SVC) to classify the results of VMAT patient-specific QA measurements. The 'targets' in this model were simple classes representing median dose difference between measured and expected dose distributions-'hot' if the median dose deviation was >1%, 'cold' if it was <-1%, and 'normal' if it was within ±1%. A total of 1620 unique patient-specific QA measurements were available for model development and testing. 75% of the data were used to develop and cross-validate the model, and the remaining 25% were used for an independent assessment of model performance. For the model development phase, a recursive feature elimination (RFE) cross-validation technique was used to eliminate unimportant features. Model performance was assessed using receiver operator characteristic (ROC) curve metrics. Of the ten features found to be most predictive of patient-specific QA measurement results, half were derived from treatment plan characteristics and half from quality control (QC) metrics characterizing linac performance. The model achieved a micro-averaged area under the ROC curve of 0.93, and a macro-averaged area under the ROC curve of 0.88. This work demonstrates the potential of using both treatment plan characteristics and routine linac QC results in the development of machine learning models for VMAT patient-specific QA measurements.

Simultaneous measurements of absorbed dose and linear energy transfer in therapeutic proton beams.

The biological response resulting from proton therapy depends on both the absorbed dose in the irradiated tissue and the linear energy transfer (LET) of the beam. Currently, optimization of proton therapy treatment plans is based only on absorbed dose. However, recent advances in proton therapy delivery have made it possible to vary the LET distribution for potential therapeutic gain, leading to investigations of using LET as an additional parameter in plan optimization. Having a method to measure and verify both absorbed dose and LET as part of a quality assurance program would be ideal for the safe delivery of such plans. Here we demonstrated the potential of an optically stimulated luminescence (OSL) technique to simultaneously measure absorbed dose and LET. We calibrated the ratio of ultraviolet (UV) to blue emission intensities from Al2O3:C OSL detectors as a function of LET to facilitate LET measurements. We also calibrated the intensity of the blue OSL emission for absorbed dose measurements and introduced a technique to correct for the LET-dependent dose response of OSL detectors exposed to therapeutic proton beams. We demonstrated the potential of our OSL technique by using it to measure LET and absorbed dose under new irradiation conditions, including patient-specific proton therapy treatment plans. In the beams investigated, we found the OSL technique to measure dose-weighted LET within 7.9% of Monte Carlo-simulated values and absorbed dose within 2.5% of ionization chamber measurements.

Time-Lapse Monitoring of DNA Damage Colocalized With Particle Tracks in Single Living Cells.

PURPOSE: Understanding the DNA damage and repair induced by hadron therapy (HT) beams is crucial for developing novel strategies to maximize the use of HT beams to treat cancer patients. However, spatiotemporal studies of DNA damage and repair for beam energies relevant to HT have been challenging. We report a technique that enables spatiotemporal measurement of radiation-induced damage in live cells and colocalization of this damage with charged particle tracks over a broad range of clinically relevant beam energies. The technique uses novel fluorescence nuclear track detectors with fluorescence confocal laser scanning microscopy in the beam line to visualize particle track traversals within the subcellular compartments of live cells within seconds after injury. METHODS AND MATERIALS: We designed and built a portable fluorescence confocal laser scanning microscope for use in the beam path, coated fluorescence nuclear track detectors with fluorescent-tagged live cells (HT1080 expressing enhanced green fluorescent protein tagged to XRCC1, a single-strand break repair protein), placed the entire assembly into a proton therapy beam line, and irradiated the cells with a fluence of ∼1 × 10(6) protons/cm(2). RESULTS: We successfully obtained confocal images of proton tracks and foci of DNA single-strand breaks immediately after irradiation. CONCLUSIONS: This technique represents an innovative method for analyzing biological responses in any HT beam line at energies and dose rates relevant to therapy. It allows precise determination of the number of tracks traversing a subcellular compartment and monitoring the cellular damage therein, and has the potential to measure the linear energy transfer of each track from therapeutic beams.

Nanoscale measurements of proton tracks using fluorescent nuclear track detectors.

PURPOSE: The authors describe a method in which fluorescence nuclear track detectors (FNTDs), novel track detectors with nanoscale spatial resolution, are used to determine the linear energy transfer (LET) of individual proton tracks from proton therapy beams by allowing visualization and 3D reconstruction of such tracks. METHODS: FNTDs were exposed to proton therapy beams with nominal energies ranging from 100 to 250 MeV. Proton track images were then recorded by confocal microscopy of the FNTDs. Proton tracks in the FNTD images were fit by using a Gaussian function to extract fluorescence amplitudes. Histograms of fluorescence amplitudes were then compared with LET spectra. RESULTS: The authors successfully used FNTDs to register individual proton tracks from high-energy proton therapy beams, allowing reconstruction of 3D images of proton tracks along with delta rays. The track amplitudes from FNTDs could be used to parameterize LET spectra, allowing the LET of individual proton tracks from therapeutic proton beams to be determined. CONCLUSIONS: FNTDs can be used to directly visualize proton tracks and their delta rays at the nanoscale level. Because the track intensities in the FNTDs correlate with LET, they could be used further to measure LET of individual proton tracks. This method may be useful for measuring nanoscale radiation quantities and for measuring the LET of individual proton tracks in radiation biology experiments.

Comparison of linear energy transfer scoring techniques in Monte Carlo simulations of proton beams.

Monte Carlo (MC) simulations are commonly used to study linear energy transfer (LET) distributions in therapeutic proton beams. Various techniques have been used to score LET in MC simulations. The goal of this work was to compare LET distributions obtained using different LET scoring techniques and examine the sensitivity of these distributions to changes in commonly adjusted simulation parameters. We used three different techniques to score average proton LET in TOPAS, which is a MC platform based on the Geant4 simulation toolkit. We determined the sensitivity of each scoring technique to variations in the range production thresholds for secondary electrons and protons. We also compared the depth-LET distributions that we acquired using each technique in a simple monoenergetic proton beam and in a more clinically relevant modulated proton therapy beam. Distributions of both fluence-averaged LET (LETΦ) and dose-averaged LET (LETD) were studied. We found that LETD values varied more between different scoring techniques than the LETΦ values did, and different LET scoring techniques showed different sensitivities to changes in simulation parameters.

Calibration of the Al2O3:C optically stimulated luminescence (OSL) signal for linear energy transfer (LET) measurements in therapeutic proton beams.

Optically stimulated luminescence (OSL) detectors (OSLDs) have shown potential for measurements of linear energy transfer (LET) in proton therapy beams. However, the technique lacks the efficiency needed for clinical implementation, and a faster, simpler approach to LET measurements is desirable. The goal of this work was to demonstrate and evaluate the potential of calibrating Al2O3:C OSLDs for LET measurements using new methods. We exposed batches of OSLDs to unmodulated proton beams of varying LET and calibrated three parameters of the resulting OSL signals as functions of fluence-averaged LET (ϕ-LET) and dose-averaged LET (D-LET). These three parameters included the OSL curve shape evaluated under continuous wave stimulation (CW-OSL), the OSL curve shape evaluated under pulsed stimulation (P-OSL), and the intensity ratio of the two main emission bands in the Al2O3:C OSL emission spectrum (ultraviolet [UV]/blue ratio). To test the calibration, we then irradiated new batches of OSLDs in modulated proton beams of varying LET, and used the OSL signal parameters to calculate ϕ-LET and D-LET under these new test conditions. Using the P-OSL curve shape, D-LET was measured within 5.7% of the expected value. We conclude that from a single 10 s readout (following initial calibration), both the absorbed dose and LET in proton therapy beams can be measured using OSLDs. This has potential future applications in the quality assurance of proton therapy treatment plans, particularly for those that may account for LET or relative biological effectiveness in their optimization. The methods demonstrated in this work may also be applicable to other particle therapy beams, including carbon ion beams.