Purpose:
Proton radiotherapy for head‐and‐neck cancer (HNC) aims to improve organ‐at‐risk (OAR) sparing over photon radiotherapy. However, it may be less robust for setup and range uncertainties. The ...authors investigated OAR sparing and plan robustness for spot‐scanning proton planning techniques and compared these with volumetric modulated arc therapy (VMAT) photon plans.
Methods:
Ten HNC patients were replanned using two arc VMAT (RapidArc) and spot‐scanning proton techniques. OARs to be spared included the contra‐ and ipsilateral parotid and submandibular glands and individual swallowing muscles. Proton plans were made using Multifield Optimization (MFO, using three, five, and seven fields) and Single‐field Optimization (SFO, using three fields). OAR sparing was evaluated using mean dose to composite salivary glands (CompSal) and composite swallowing muscles (CompSwal). Plan robustness was determined for setup and range uncertainties (±3 mm for setup, ±3% HU) evaluating V95% and V107% for clinical target volumes.
Results:
Averaged over all patients CompSal/CompSwal mean doses were lower for the three‐field MFO plans (14.6/16.4 Gy) compared to the three‐field SFO plans (20.0/23.7 Gy) and VMAT plans (23.0/25.3 Gy). Using more than three fields resulted in differences in OAR sparing of less than 1.5 Gy between plans. SFO plans were significantly more robust than MFO plans. VMAT plans were the most robust.
Conclusions:
MFO plans had improved OAR sparing but were less robust than SFO and VMAT plans, while SFO plans were more robust than MFO plans but resulted in less OAR sparing. Robustness of the MFO plans did not increase with more fields.
Purpose:
For interstitial photodynamic therapy (iPDT) of bulky tumors, careful treatment planning is required in order to ensure that a therapeutic dose is delivered to the tumor, while minimizing ...damage to surrounding normal tissue. In clinical contexts, iPDT has typically been performed with either flat cleaved or cylindrical diffusing optical fibers as light sources. Here, the authors directly compare these two source geometries in terms of the number of fibers and duration of treatment required to deliver a prescribed light dose to a tumor volume.
Methods:
Treatment planning software for iPDT was developed based on graphics processing unit enhanced Monte Carlo simulations. This software was used to optimize the number of fibers, total energy delivered by each fiber, and the position of individual fibers in order to deliver a target light dose (D90) to 90% of the tumor volume. Treatment plans were developed using both flat cleaved and cylindrical diffusing fibers, based on tissue volumes derived from CT data from a head and neck cancer patient. Plans were created for four cases: fixed energy per fiber, fixed number of fibers, and in cases where both or neither of these factors were fixed.
Results:
When the number of source fibers was fixed at eight, treatment plans based on flat cleaved fibers required each to deliver 7180–8080 J in order to deposit 90 J/cm2 in 90% of the tumor volume. For diffusers, each fiber was required to deliver 2270–2350 J (333–1178 J/cm) in order to achieve this same result. For the case of fibers delivering a fixed 900 J, 13 diffusers or 19 flat cleaved fibers at a spacing of 1 cm were required to deliver the desired dose. With energy per fiber fixed at 2400 J and the number of fibers fixed at eight, diffuser fibers delivered the desired dose to 93% of the tumor volume, while flat cleaved fibers delivered this dose to 79%. With both energy and number of fibers allowed to vary, six diffusers delivering 3485–3600 J were required, compared to ten flat cleaved fibers delivering 2780–3600 J.
Conclusions:
For the same number of fibers, cylindrical diffusers allow for a shorter treatment duration compared to flat cleaved fibers. For the same energy delivered per fiber, diffusers allow for the insertion of fewer fibers in order to deliver the same light dose to a target volume.
. Automated treatment planning today is focussed on non-exact, two-step procedures. Firstly, dose-volume histograms (DVHs) or 3D dose distributions are predicted from the patient anatomy. Secondly, ...these are converted in multi-leaf collimator (MLC) apertures and monitor units (MUs) using a generic optimisation to obtain the final treatment plan. In contrast, we present a method to predict volumetric modulated arc therapy (VMAT) MLC apertures and MUs directly from patient anatomy using deep learning. The predicted plan is then provided as initialisation to the optimiser for fine-tuning.
. 148 patients (training: 101; validation: 23; test: 24), treated for right breast cancer, are replanned to obtain a homogeneous database of 3-arc VMAT plans (PTV
: 45.57 Gy; PTV
: 55.86 Gy) according to the clinical protocol, using RapidPlan
with automatic optimisation and extended convergence mode (clinical workflow). Projections of the CT and contours are created along the beam's eye view of all control points and given as input to a U-net type convolutional neural networks (CNN). The output are the MLC aperture and MU for all control points, from which a DICOM RTplan is built. This is imported and further optimised in the treatment planning system using automatic optimisation without convergence mode, with clinical PTV objectives and organs-at-risk (OAR) objectives based on the DVHs calculated from the imported plan (CNN workflow).
. Mean dose differences between the clinical and CNN workflow over the test set are 0.2 ± 0.5 Gy at
and 0.6 ± 0.4 Gy at
of PTV
and -0.4 ± 0.3 Gy at
and 0.7 ± 0.3 Gy at
of PTV
. For the OAR, they are -0.2 ± 0.2 Gy for
and 0.04 ± 0.8 Gy for
. The mean computation time is 60 and 25 min respectively.
. VMAT optimisation can be initialised by MLC apertures and MUs, directly predicted from patient anatomy using a CNN, reducing planning time with more than half while maintaining clinically acceptable plans. This procedure puts the planner in a supervising role over an AI-based treatment planning workflow.
Given the ongoing dilemma for college counseling centers to meet steady increases in demand for services, this study outlines the implementation of an adapted stepped care model in a university ...counseling center. Our adapted model focused, as do other stepped care models, on treatment planning and lower-intensity interventions, with the addition of the intensive therapy option being provided on a weekly basis. We adopted our stepped care model across a large center and hypothesized that after implementation we would be able to serve a similar number of clients as our previous model and that treatment outcomes for these clients would improve. Descriptive data and regression analyses demonstrated support for our hypotheses, including an increased likelihood of clinically significant improvement for clients postimplementation. Implications for adapting service delivery models using practice-based evidence are discussed.
Impact Statement
With high demand for services at university counseling centers, adjustment was needed to how the treatment was delivered to clients. Adapting an established system of stepped care-planning treatment to match each client's need and in our case prioritizing weekly sessions over-can be enacted in an entire center. This way of delivering services may be more efficient and allows for there to be limits in place to prevent therapists getting burned out.
Suicide is one of the leading causes of death among adolescents in the United States, and risk for recurring suicidal thoughts and behavior remains high after discharge from psychiatric hospitals. ...Safety planning, a brief intervention wherein the main focus is on identifying personal coping strategies and resources to mitigate suicidal crises, is a recommended best practice approach for intervening with individuals at risk for suicide. However, anecdotal as well as emerging empirical evidence indicate that adolescents at risk for suicide often do not use their safety plan during the high-risk postdischarge period. Thus, to be maximally effective, we argue that safety planning should be augmented with additional strategies for increasing safety plan use to prevent recurrent crises during high-risk transitions. The current article describes an adjunctive intervention for adolescents at elevated suicide risk that enhances safety planning with motivational interviewing (MI) strategies, with the goal of increasing adolescents' motivation and strengthening self-efficacy for safety plan use after discharge. We provide an overview of the intervention and its components, focusing the discussion on the in-person individual and family sessions delivered during hospitalization, and describe the theoretical basis for the MI-enhanced intervention. We then provide examples of applying MI during the process of safety planning, including example strategies that aim to elicit motivation and strengthen self-efficacy for safety plan use. We conclude with clinical case material and highlight how these strategies may be incorporated into the safety planning session.
Clinical Impact StatementQuestion: This article describes how motivational interviewing (MI) strategies can be applied in a safety planning session. Findings: This article provides example strategies illustrating how MI might be applied to guide the process of developing a safety plan. Meaning: MI strategies may offer a useful approach for facilitating client engagement during the process of safety planning. Next Steps: Large trials are needed to demonstrate efficacy of the MI-enhanced safety planning intervention in adolescent populations and should be explored for other populations.
Microwave hyperthermia (MH) requires the effective calibration of antenna excitations for the selective focusing of the microwave energy on the target region, with a nominal effect on the surrounding ...tissue. To this end, many different antenna calibration methods, such as optimization techniques and look-up tables, have been proposed in the literature. These optimization procedures, however, do not consider the whole nature of the electric field, which is a complex vector field; instead, it is simplified to a real and scalar field component. Furthermore, most of the approaches in the literature are system-specific, limiting the applicability of the proposed methods to specific configurations. In this paper, we propose an antenna excitation optimization scheme applicable to a variety of configurations and present the results of a convolutional neural network (CNN)-based approach for two different configurations. The data set for CNN training is collected by superposing the information obtained from individual antenna elements. The results of the CNN models outperform the look-up table results. The proposed approach is promising, as the phase-only optimization and phase–power-combined optimization show a 27% and 4% lower hotspot-to-target energy ratio, respectively, than the look-up table results for the linear MH applicator. The proposed deep-learning-based optimization technique can be utilized as a protocol to be applied on any MH applicator for the optimization of the antenna excitations, as well as for a comparison of MH applicators.
The graphics processing unit (GPU) has emerged as a competitive platform for computing
massively parallel problems. Many computing applications in medical physics can be
formulated as data-parallel ...tasks that exploit the capabilities of the GPU for reducing
processing times. The authors review the basic principles of GPU computing as well as the
main performance optimization techniques, and survey existing applications in three
areas of medical
physics, namely image reconstruction,
dose calculation
and treatment plan optimization, and image processing.
Purpose:
Radiation treatment planning involves a complex workflow that has multiple potential points of vulnerability. This study utilizes an incident reporting system to identify the origination and ...detection points of near-miss errors, in order to guide their departmental safety improvement efforts. Previous studies have examined where errors arise, but not where they are detected or applied a near-miss risk index (NMRI) to gauge severity.
Methods:
From 3/2012 to 3/2014, 1897 incidents were analyzed from a departmental incident learning system. All incidents were prospectively reviewed weekly by a multidisciplinary team and assigned a NMRI score ranging from 0 to 4 reflecting potential harm to the patient (no potential harm to potential critical harm). Incidents were classified by point of incident origination and detection based on a 103-step workflow. The individual steps were divided among nine broad workflow categories (patient assessment, imaging for radiation therapy (RT) planning, treatment planning, pretreatment plan review, treatment delivery, on-treatment quality management, post-treatment completion, equipment/software quality management, and other). The average NMRI scores of incidents originating or detected within each broad workflow area were calculated. Additionally, out of 103 individual process steps, 35 were classified as safety barriers, the process steps whose primary function is to catch errors. The safety barriers which most frequently detected incidents were identified and analyzed. Finally, the distance between event origination and detection was explored by grouping events by the number of broad workflow area events passed through before detection, and average NMRI scores were compared.
Results:
Near-miss incidents most commonly originated within treatment planning (33%). However, the incidents with the highest average NMRI scores originated during imaging for RT planning (NMRI = 2.0, average NMRI of all events = 1.5), specifically during the documentation of patient positioning and localization of the patient. Incidents were most frequently detected during treatment delivery (30%), and incidents identified at this point also had higher severity scores than other workflow areas (NMRI = 1.6). Incidents identified during on-treatment quality management were also more severe (NMRI = 1.7), and the specific process steps of reviewing portal and CBCT images tended to catch highest-severity incidents. On average, safety barriers caught 46% of all incidents, most frequently at physics chart review, therapist’s chart check, and the review of portal images; however, most of the incidents that pass through a particular safety barrier are not designed to be capable of being captured at that barrier.
Conclusions:
Incident learning systems can be used to assess the most common points of error origination and detection in radiation oncology. This can help tailor safety improvement efforts and target the highest impact portions of the workflow. The most severe near-miss events tend to originate during simulation, with the most severe near-miss events detected at the time of patient treatment. Safety barriers can be improved to allow earlier detection of near-miss events.
Hyperthermic intraperitoneal chemotherapy (HIPEC) is administered to treat residual microscopic disease after cytoreductive surgery (CRS). During HIPEC, fluid (41-43 °C) is administered and drained ...through a limited number of catheters, risking thermal and drug heterogeneities within the abdominal cavity that might reduce effectiveness. Treatment planning software provides a unique tool for optimizing treatment delivery. This study aimed to investigate the influence of treatment-specific parameters on the thermal and drug homogeneity in the peritoneal cavity in a computed tomography based rat model.
We developed computational fluid dynamics (CFD) software simulating the dynamic flow, temperature and drug distribution during oxaliplatin based HIPEC. The influence of location and number of catheters, flow alternations and flow rates on peritoneal temperature and drug distribution were determined. The software was validated using data from experimental rat HIPEC studies.
The predicted core temperature and systemic oxaliplatin concentration were comparable to the values found in literature. Adequate placement of catheters, additional inflow catheters and higher flow rates reduced intraperitoneal temperature spatial variation by −1.4 °C, −2.3 °C and −1.2 °C, respectively. Flow alternations resulted in higher temperatures (up to +1.5 °C) over the peritoneal surface. Higher flow rates also reduced the spatial variation of chemotherapy concentration over the peritoneal surface resulting in a more homogeneous effective treatment dose.
The presented treatment planning software provides unique insights in the dynamics during HIPEC, which enables optimization of treatment-specific parameters and provides an excellent basis for HIPEC treatment planning in human applications.
Purpose:
Latest generation linear accelerators (linacs), i.e., TrueBeam (Varian Medical Systems, Palo Alto, CA) and its stereotactic counterpart, TrueBeam STx, have several unique features, including ...high-dose-rate flattening-filter-free (FFF) photon modes, reengineered electron modes with new scattering foil geometries, updated imaging hardware/software, and a novel control system. An evaluation of five TrueBeam linacs at three different institutions has been performed and this work reports on the commissioning experience.
Methods:
Acceptance and commissioning data were analyzed for five TrueBeam linacs equipped with 120 leaf (5 mm width) MLCs at three different institutions. Dosimetric data and mechanical parameters were compared. These included measurements of photon beam profiles (6X, 6XFFF, 10X, 10XFFF, 15X), photon and electron percent depth dose (PDD) curves (6, 9, 12 MeV), relative photon output factors (Scp), electron cone factors, mechanical isocenter accuracy, MLC transmission, and dosimetric leaf gap (DLG). End-to-end testing and IMRT commissioning were also conducted.
Results:
Gantry/collimator isocentricity measurements were similar (0.27–0.28 mm), with overall couch/gantry/collimator values of 0.46–0.68 mm across the three institutions. Dosimetric data showed good agreement between machines. The average MLC DLGs for 6, 10, and 15 MV photons were 1.33 ± 0.23, 1.57 ± 0.24, and 1.61 ± 0.26 mm, respectively. 6XFFF and 10XFFF modes had average DLGs of 1.16 ± 0.22 and 1.44 ± 0.30 mm, respectively. MLC transmission showed minimal variation across the three institutions, with the standard deviation <0.2% for all linacs. Photon and electron PDDs were comparable for all energies. 6, 10, and 15 MV photon beam quality, %dd(10)x varied less than 0.3% for all linacs. Output factors (Scp) and electron cone factors agreed within 0.27%, on average; largest variations were observed for small field sizes (1.2% coefficient of variation, 10 MV, 2 × 2 cm2) and small cone sizes (<1% coefficient of variation, 6 × 6 cm2 cone), respectively.
Conclusions:
Overall, excellent agreement was observed in TrueBeam commissioning data. This set of multi-institutional data can provide comparison data to others embarking on TrueBeam commissioning, ultimately improving the safety and quality of beam commissioning.
PDF
