Therapy Workflow Research Articles

On-line adaptive and real-time intrafraction motion management of spine-SBRT on an MR-linac

Purpose: The superior soft-tissue contrast of MRI-guided radiotherapy offers enhanced localization accuracy of the spinal cord in spine Stereotactic Body Radiotherapy (SBRT). This work includes a planning study for spine-SBRT on an MR-Linac. Additionally, a patient with spine metastasis was treated using an adaptive radiation therapy workflow. We report our initial experience of targeting accuracy, image-guided localization, on-line adaptive planning, and treatment with real-time intrafraction imaging with automatic beam gating.Methods: Six spine-SBRT patients were retrospectively re-planned to 18 Gy in 1-fraction on a commercial, Monte Carlo-based MR-Linac treatment planning system. Plans were generated using 9–13 step-and-shoot intensity-modulated radiation therapy 6 MV-flattening filter free beams and optimized to achieve plan quality criteria recommended by RTOG-0631. One thoracic vertebral body clinical case was treated to 27 Gy in 3-fractions utilizing ART, where daily anatomical changes were accounted for via re-planning and treatment in an on-line manner to account for limited ability to correct rotational setup uncertainties.Results: Plans met all critical-tissue constraints outlined in RTOG-0631 and AAPM Task Group-101, while covering 90% of the target with the prescription dose. Clinically, visibility of the spinal cord allowed for patient setup focusing on spinal cord-alignment. Utilization of the online ART workflow, while re-contouring the target and spinal cord, enabled an increase in prescription dose coverage from 89 to 95% in two of three fractions while maintaining acceptable doses to organs-at-risk. Real-time MR-cine imaging demonstrated sufficient quality for the automatic beam gating algorithm to provide intrafraction motion management of the spinal canal utilizing a 3.0 mm gating boundary and 1–2% region of excursion allowance, in the sagittal plane. A decrease in coverage, below the 95% threshold was noted in post-treatment volumetric imaging due to lateral movement not observed during real-time gating.Conclusion: Achieved plan quality and deliverability was within accepted standards. MR-guidance with an on-line ART workflow offered increased accuracy in the localization of the spinal cord at the time of treatment to enhance both tissue sparing and target volume coverage. Increased spatial resolution of cine-images, and tracking in three-dimensions would be beneficial for future spine-SBRT treatments on the MR-Linac.

Deep learning-based 4D-synthetic CTs from sparse-view CBCTs for dose calculations in adaptive proton therapy.

Time-resolved 4D cone beam-computed tomography (4D-CBCT) allows a daily assessment of patient anatomy and respiratory motion. However, 4D-CBCTs suffer from imaging artifacts that affect the CT number accuracy and prevent accurate proton dose calculations. Deep learning can be used to correct CT numbers and generate synthetic CTs (sCTs) that can enable CBCT-based proton dose calculations. In this work, sparse view 4D-CBCTs were converted into 4D-sCT utilizing a deep convolutional neural network (DCNN). 4D-sCTs were evaluated in terms of image quality and dosimetric accuracy to determine if accurate proton dose calculations for adaptive proton therapy workflows of lung cancer patients are feasible. A dataset of 45 thoracic cancer patients was utilized to train and evaluate a DCNN to generate 4D-sCTs, based on sparse view 4D-CBCTs reconstructed from projections acquired with a 3D acquisition protocol. Mean absolute error (MAE) and mean error were used as metrics to evaluate the image quality of single phases and average 4D-sCTs against 4D-CTs acquired on the same day. The dosimetric accuracy was checked globally (gamma analysis) and locally for target volumes and organs-at-risk (OARs) (lung, heart, and esophagus). Furthermore, 4D-sCTs were also compared to 3D-sCTs. To evaluate CT number accuracy, proton radiography simulations in 4D-sCT and 4D-CTs were compared in terms of range errors. The clinical suitability of 4D-sCTs was demonstrated by performing a 4D dose reconstruction using patient specific treatment delivery log files and breathing signals. 4D-sCTs resulted in average MAEs of 48.1±6.5HU (single phase) and 37.7±6.2HU (average). The global dosimetric evaluation showed gamma pass ratios of 92.3%±3.2% (single phase) and 94.4%±2.1% (average). The clinical target volume showed high agreement in D98 between 4D-CT and 4D-sCT, with differences below 2.4% for all patients. Larger dose differences were observed in mean doses of OARs (up to 8.4%). The comparison with 3D-sCTs showed no substantial image quality and dosimetric differences for the 4D-sCT average. Individual 4D-sCT phases showed slightly lower dosimetric accuracy. The range error evaluation revealed that lung tissues cause range errors about three times higher than the other tissues. In this study, we have investigated the accuracy of deep learning-based 4D-sCTs for daily dose calculations in adaptive proton therapy. Despite image quality differences between 4D-sCTs and 3D-sCTs, comparable dosimetric accuracy was observed globally and locally. Further improvement of 3D and 4D lung sCTs could be achieved by increasing CT number accuracy in lung tissues.

Simplifying analytical development of viral vector production: robust and sensitive methods for common expression systems

The number of viral vector-based gene therapies in clinical trials has recently grown into the thousands due to the tremendous genetic disease-curing potential they harbor. Despite this growth, the comprehensive characterization of critical quality attributes for the safety and efficacy of the material produced for these trials remains a challenge for both manufacturers and regulatory bodies alike. The demands on analytical development teams are oversized compared to legacy biopharmaceuticals and require a unique focus to address issues such as identification, characterization, and enumeration of undesired byproducts. Application of established regulatory guidance, such as limits to residual host cell DNA, requires additional scrutiny due to possible encapsidation and oncogenic potential. This article will focus on the current state of analytical methods in gene therapy workflows, and how leveraging the work Thermo Fisher Scientific has developed can help simplify the burden on analytical development teams.

Applications and benefits of the CTS Rotea Counterflow Centrifugation System in cell therapy workflows

The Gibco™ CTS™ Rotea™ Counterflow Centrifugation System is a closed cell processing system designed for cell therapy workflows. It has the flexibility to perform a variety of cell processing applications across several cell types. Here, we provide some key data to highlight the Rotea system’s ability to isolate white blood cells (WBCs) starting with leukopaks, as well as perform high speed T cell wash & concentrate steps and efficient cell washing.

Feasibility of postoperative spine stereotactic body radiation therapy in proximity of carbon and titanium hybrid implants using a robotic radiotherapy device

Background and purposeTo assess the feasibility of postoperative stereotactic body radiation therapy (SBRT) for patients with hybrid implants consisting of carbon fiber reinforced polyetheretherketone and titanium (CFP-T) using CyberKnife.Materials and methodsAll essential steps within a radiation therapy (RT) workflow were evaluated. First, the contouring process of target volumes and organs at risk (OAR) was done for patients with CFP-T implants. Second, after RT-planning, the accuracy of the calculated dose distributions was tested in a slab phantom and an anthropomorphic phantom using film dosimetry. As a third step, the accuracy of the mandatory image guided radiation therapy (IGRT) including automatic matching was assessed using the anthropomorphic phantom. For this goal, a standard quality assurance (QA) test was modified to carry out its IGRT part in presence of CFP-T implants.ResultsUsing CFP-T implants, target volumes could precisely delineated. There was no need for compromising the contours to overcome artifact obstacles. Differences between measured and calculated dose values were below 11% for the slab phantom, and at least 95% of the voxels were within 5% dose difference. The comparisons for the anthropomorphic phantom showed a gamma-passing rate (5%, 1 mm) of at least 97%. Additionally the test results with and without CFP-T implants were comparable. No issues concerning the IGRT were detected. The modified machine QA test resulted in a targeting error of 0.71 mm, which corresponds to the results of the unmodified standard tests.ConclusionDose calculation and delivery of postoperative spine SBRT is feasible in proximity of CFP-T implants using a CyberKnife system.

Accelerating cell & gene therapy workflows with next-generation analytical tools

Modern medicines call for modern technologies. Revolutionary cell and gene therapies offer significant promise to treat life-threatening diseases. However, getting therapies to market quickly and efficiently requires accurate testing of critical quality attributes, including accurate viral vector analysis and cell characterization. This article discusses how a variety of innovative analytical tools can aid in the automation and scalability needed on the cell and gene therapy road to discovery, including rapid and accurate analysis of cells, molecules, and contaminants, along with consistent, high-quality data across project phases. These next-generation analytical solutions are also assessed for the ease with which they fit into current workflows and their adaptability to changing needs. 

GPU-accelerated Monte Carlo-based online adaptive proton therapy: A feasibility study.

To develop an online graphic processing unit (GPU)-accelerated Monte Carlo-based adaptive radiation therapy (ART) workflow for pencil beam scanning (PBS) proton therapy to address interfraction anatomical changes in patients treated with PBS. A four-step workflow was developed using our in-house developed GPU-accelerated Monte Carlo-based treatment planning system to implement online Monte Carlo-based ART for PBS. The first step conducts diffeomorphic demon-based deformable image registration (DIR) to propagate contours on the initial planning CT (pCT) to the verification CT (vCT) to form a new structure set. The second step performs forward dose calculation of the initial plan on the vCT with the propagated contours after manual approval (possible modifications involved). The third step triggers a reoptimization of the plan depending on whether the verification dose meets the clinical requirements or not. A robust evaluation will be done for both the verification plan in the second step and the reopotimized plan in the third step. The fourth step involves a two-stage (before and after delivery) patient-specific quality assurance (PSQA) of the reoptimized plan. The before-delivery PSQA is to compare the plan dose to the dose calculated using an independent fast open-source Monte Carlo code, MCsquare. The after-delivery PSQA is to compare the plan dose to the dose recalculated using the log file (spot MU, spot position, and spot energy) collected during the delivery. Jaccard index (JI), dice similarity coefficients (DSCs), and Hausdorff distance (HD) were used to assess the quality of the propagated contours in the first step. A commercial plan evaluation software, ClearCheck™, was integrated into the workflow to carry out efficient plan evaluation. 3D Gamma analysis was used during the fourth step to ensure the accuracy of the plan dose from reoptimization. Three patients with three different disease sites were chosen to evaluate the feasibility of the online ART workflow for PBS. For all three patients, the propagated contours were found to have good volume conformance [JI (lowest-highest: 0.833-0.983) and DSC (0.909-0.992)] but suboptimal boundary coincidence [HD (2.37-20.76mm)] for organs-at-risk. The verification dose evaluated by ClearCheck™ showed significant degradation of the target coverage due to the interfractional anatomical changes. Reoptimization on the vCT resulted in great improvement of the plan quality to a clinically acceptable level. 3D Gamma analyses of PSQA confirmed the accuracy of the plan dose before delivery (mean Gamma index=98.74% with a threshold of 2%/2mm/10%), and after delivery based on the log files (mean Gamma index=99.05% with a threshold of 2%/2mm/10%). The average time cost for the complete execution of the workflow was around 858s, excluding the time for manual intervention. The proposed online ART workflow for PBS was demonstrated to be efficient and effective by generating a reoptimized plan that significantly improved the plan quality.

Less Time Is Less Motion: Analysis of Practical Efficiencies Gained With a Modified Workflow Integrating Planar kV Midimaging With CBCT for Spine Stereotactic Body Radiation Therapy

PurposeOur purpose was to optimize an image guided radiation therapy (IGRT) workflow to achieve practical setup accuracy in spine stereotactic body radiation therapy (SBRT). We assessed the time-saving efficiencies gained from incorporating planar kV midimaging as a surrogate for cone beam computed tomography (CBCT) for intrafraction motion monitoring. Methods and MaterialsWe selected 5 thoracic spine SBRT patients treated in 5 fractions and analyzed patient shifts captured by a modified IGRT workflow using planar kV midimaging integrated with CBCT to maintain a tolerance of 1 mm and 1°. We determined the frequency at which kV midimaging captured intrafraction motion as validated on repeat CBCT and assessed the potential time and dosimetric advantages of our modified IGRT workflow. ResultsPatient motion, detected as out-of-tolerance shifts on planar kV midimaging, occurred during 6 of 25 fractions (24%) and were validated on repeat CBCT 100% of the time. Observed intrafraction absolute shifts (mean ± standard deviation) for the 25 fractions were 0.39 ± 0.21, 0.56 ± 0.22, and 0.45 ± 0.21 mm for lateral-longitude-vertical translations and 0.38 ± 0.12°, 0.32 ± 0.09°, and 0.47 ± 0.14° for pitch-roll-yaw rotation, which if uncorrected, could have significantly affected target coverage and increased spinal cord dose. The average times for pretreatment imaging, midtreatment verification, and total treatment time were 8.94, 2.81, and 16.21 minutes. Our modified IGRT workflow reduced the total number of CBCTs required from 120 to 35 (70%) and imaging dose from 126.2 to 43.4 cGy (65.6%) while maintaining high fidelity for our patient population. ConclusionsAccurate patient positioning was effectively achieved with use of multiple 2-dimensional-3-dimensional kV images and an average of 1 verification CBCT scan per fraction. Integration of planar kV midimaging can effectively reduce treatment time associated with spine SBRT delivery and minimize the potential dosimetric effect of intrafraction motion on target coverage and spinal cord dose.

588 Utilizing a Quality Improvement Approach to Increase Compliance with Patient Positioning in the Burn Unit

IntroductionPatient positioning, notably positioning patients in “anti-deformity positioning”, is a standard practice in burn rehabilitation as it assists with edema management, scar contracture prevention, and wound healing. Successfully and consistently providing proper positioning of burn patients requires the combined effort of the multi-disciplinary burn team. The primary goal at our center was to increase the frequency that patients were correctly positioned to over 90% at the time of random audits.MethodsAt a medium-sized, academic burn unit, once to twice weekly audits were conducted by burn lead therapists on the compliance of proper patient positioning over a 6-month period. Using this data as a trigger, a quality improvement project was designed using the PDSA (Plan-Do-Study-Act) cycle to help identify reasons behind lack of compliance. Surveys were distributed to the therapy and nursing staff to identify any barriers to care. Effects on positioning compliance post-intervention were monitored.ResultsIn the 6 months prior to intervention, our patients were positioned correctly an average of 76% of the time. Therapy and nursing surveys identified the following barriers to care: Nursing needed more education on positioning, and the approach was too heavily reliant on nursing efforts alone. To address these barriers, therapists provided education to both day and night shift nurses, communicated daily about positioning expectations, shifted the project from a nursing approach to a multidisciplinary approach, and made changes in therapy workflow. Immediately following the intervention, the compliance rates were 91% for the first month and 85% for the second month.ConclusionsCoordinating efforts of the entire burn team improves consistency for positioning in burn patients. Utilizing the PDSA cycle allowed us to identify areas for improvement and to develop appropriate interventions aimed at both increased education for nursing staff and workflow improvements for our therapists. Following the completion of our interventions we were able to obtain an immediate improvement in our compliance with proper positioning of burn patients.

Versatile Detection and Monitoring of Ionizing Radiation Treatment Using Radiation-Responsive Gel Nanosensors.

Modern radiation therapy workflow involves complex processes intended to maximize the radiation dose delivered to tumors while simultaneously minimizing excess radiation to normal tissues. Safe and accurate delivery of radiation doses is critical to the successful execution of these treatment plans and effective treatment outcomes. Given extensive differences in existing dosimeters, the choice of devices and technologies for detecting biologically relevant doses of radiation has to be made judiciously, taking into account anatomical considerations and modality of treatment (invasive, e.g., interstitial brachytherapy vs noninvasive, e.g., external-beam therapy radiotherapy). Rapid advances in versatile radiation delivery technologies necessitate new detection platforms and devices that are readily adaptable into a multitude of form factors in order to ensure precision and safety in dose delivery. Here, we demonstrate the adaptability of radiation-responsive gel nanosensors as a platform technology for detecting ionizing radiation using three different form factors with an eye toward versatile use in the clinic. In this approach, ionizing radiation results in the reduction of monovalent gold salts leading to the formation of gold nanoparticles within gels formulated in different morphologies including one-dimensional (1D) needles for interstitial brachytherapy, two-dimensional (2D) area inserts for skin brachytherapy, and three-dimensional (3D) volumetric dose distribution in tissue phantoms. The formation of gold nanoparticles can be detected using distinct but complementary modes of readout including optical (visual) and photothermal detection, which further enhances the versatility of this approach. A linear response in the readout was seen as a function of radiation dose, which enabled straightforward calibration of each of these devices for predicting unknown doses of therapeutic relevance. Taken together, these results indicate that the gel nanosensor technology can be used to detect ionizing radiation in different morphologies and using different detection methods for application in treatment planning, delivery, and verification in radiotherapy and in trauma care.

Comprehensive Commissioning and Clinical Implementation of GammaTiles STaRT for Intracranial Brain Cancer

PurposeTo validate the dose calculation accuracy and dose distribution of GammaTiles for brain tumors, and to suggest a surgically targeted radiation therapy (STaRT) workflow for planning, delivery, radiation safety documentation, and posttreatment validation. Methods and MaterialsNovel surgically targeted radiation therapy, GammaTiles, uses Cs-131 radiation isotopes embedded in collagen-based tiles that can be resorbed after surgery. GammaTile target delineation and dose calculation were performed on MIM Symphony software. Point-based and complex seed distribution calculations in MIM Symphony were verified with hand calculations and BrachyVision calculations. Vendor-provided 2-dimensional dose distribution calculation accuracy was validated using gafchromic EBT3 film measurements at various depths. A workflow was established for safe and effective GammaTile implants. ResultsGood agreement was observed between different calculations. Calculation accuracy of less than 0.5% was achieved for all points except one, which had rounding issues for very low doses and resulted in just below 5% difference. Differences in anisotropy and geometry positioning were noticed in the delineation of Cs-131 IsoRay seeds in the compared systems, resulting in minor discrepancies in the calculated dosimetry distributions. Film measurements showed profiles with relatively good agreement of 0% to 5% in nongradient regions with higher differences between 5% to 10% in the sharp dose fall-off regions. ConclusionsA comprehensive evaluation of GammaTile geometry, dose distribution, and clinical workflow was conducted. Safe intro-operative implantation of GammaTiles requires extensive preplanning and interdisciplinary collaboration. A STaRT workflow was outlined to provide a guideline for an accurate treatment planning and safe implant process at other institutions.

Machine-assisted interpolation algorithm for semi-automated segmentation of highly deformable organs.

Accurate and robust auto-segmentation of highly deformable organs (HDOs), for example, stomach or bowel, remains an outstanding problem due to these organs' frequent and large anatomical variations. Yet, time-consuming manual segmentation of these organs presents a particular challenge to time-limited modern radiotherapy techniques such as on-line adaptive radiotherapy and high-dose-rate brachytherapy. We propose a machine-assisted interpolation (MAI) that uses prior information in the form of sparse manual delineations to facilitate rapid, accurate segmentation of the stomach from low field magnetic resonance images (MRI) and the bowel from computed tomography (CT) images. Stomach MR images from 116 patients undergoing 0.35T MRI-guided abdominal radiotherapy and bowel CT images from 120 patients undergoing high dose rate pelvic brachytherapy treatment were collected. For each patient volume, the manual delineation of the HDO was extracted from every 8th slice. These manually drawn contours were first interpolated to obtain an initial estimate of the HDO contour. A two-channel 64×64pixel patch-based convolutional neural network (CNN) was trained to localize the position of the organ's boundary on each slice within a five-pixel wide road using the image and interpolated contour estimate. This boundary prediction was then input, in conjunction with the image, to an organ closing CNN which output the final organ segmentation. A Dense-UNet architecture was used for both networks. The MAI algorithm was separately trained for the stomach segmentation and the bowel segmentation. Algorithm performance was compared against linear interpolation (LI) alone and against fully automated segmentation (FAS) using a Dense-UNet trained on the same datasets. The Dice Similarity Coefficient (DSC) and mean surface distance (MSD) metrics were used to compare the predictions from the three methods. Statistically significance was tested using Student's t test. For the stomach segmentation, the mean DSC from MAI (0.91 ± 0.02) was 5.0% and 10.0% higher as compared to LI and FAS, respectively. The average MSD from MAI (0.77 ± 0.25mm) was 0.54 and 3.19mm lower compared to the two other methods. Only 7% of MAI stomach predictions resulted in a DSC<0.8, as compared to 30% and 28% for LI and FAS, respectively. For the bowel segmentation, the mean DSC of MAI (0.90 ± 0.04) was 6% and 18% higher, and the average MSD of MAI (0.93 ± 0.48mm) was 0.42 and 4.9mm lower as compared to LI and FAS. Sixteen percent of the predicted contour from MAI resulted in a DSC<0.8, as compared to 46% and 60% for FAS and LI, respectively. All comparisons between MAI and the baseline methods were found to be statistically significant (p-value<0.001). The proposed MAI algorithm significantly outperformed LI in terms of accuracy and robustness for both stomach segmentation from low-field MRIs and bowel segmentation from CT images. At this time, FAS methods for HDOs still require significant manual editing. Therefore, we believe that the MAI algorithm has the potential to expedite the process of HDO delineation within the radiation therapy workflow.

Toward AI-Driven Proton Dose Calculation: Development and Evaluation of 3D and 2D Sequential Neural Network Design

AI-driven proton dose calculation may provide both the accuracy and computational speed needed during memory-intensive operations (e.g., robust treatment planning) and could ultimately pave the way to realizing a seamless adaptive proton therapy workflow. Recently, multiple approaches have been devised to effectively exploit the capabilities of the neural networks in simulating the proton transport in matter. Primarily, the three-dimensional (3D) proton beam dose distribution estimation was carried out by either training a 3D Convolutional Neural Network (3D-CNN) or by transforming the problem into a sequence of two-dimensional (2D) slices in the beam's eye view. In this study, we compare the performance of the two approaches for estimating the dose for single pencil beams (PB) and we discuss the strength and weaknesses of each approach, primarily in terms of the underlying inherent model characteristics.The two models were trained on two distinct datasets created via gold standard Monte Carlo (MC) simulation. A dataset of 10000 Topas MC simulations on artificial water phantoms exhibiting sizable cuboid inhomogeneities of varying dimensions, densities, and alignment along the beam trajectory, and a dataset of 12000 MC simulations on real-world lung patient case with different impinging proton energies, gantry angles, and isocenter shifts. While the latter dataset is meant to demonstrate the performance of each model in challenging real-life scenarios, the former is designed to monitor the behavior of each model in extreme heterogeneous scenarios. The 3D-CNN is constituted of a U-net model with a state-of-the-art ResNet encoder. The sequenced approach is incorporating a Long Short-Term Memory (LSTM) model as the frontend to transmit relevant information along the beam, and a fully connected neural network as a backend to generate the 2D dose distribution in each step along the beam trajectory.The response of the two models to simple and extreme clinical scenarios was investigated extensively in terms of range, lateral spread, and the profile deformation of the Bragg peaked region. Both models demonstrate comparable accuracy in comparison to the gold standard MC simulations resulting in 96.5 % (3D-CNN) and 97.7 % (LSTM) mean gamma index pass rate ([1%, 3mm]) when executed on the set-aside test set of the lung dataset. The 3D-CNN proves to be capable of extracting spatio-temporal features between the heterogeneities and the consequent dose deformations, however, the LSTM network exhibits a better propagation of the heterogeneities for higher ranged PBs.Both approaches to AI-driven dose calculation in proton therapy were shown to be viable candidates to estimate dose distributions at the PB level; however, in extreme scenarios of highly heterogeneous tissues and high-energy protons, advanced procedures are warranted for the design of the 3D model neural network.

Experimental validation of daily adaptive proton therapy

Anatomical changes during proton therapy require rapid treatment plan adaption to mitigate the associated dosimetric impact. This in turn requires a highly efficient workflow that minimizes the time between imaging and delivery. At the Paul Scherrer Institute, we have developed an online adaptive workflow, which is specifically designed for treatments in the skull-base/cranium, with the focus set on simplicity and minimizing changes to the conventional workflow. The dosimetric and timing performance of this daily adaptive proton therapy (DAPT) workflow has been experimentally investigated using an in-house developed DAPT software and specifically developed anthropomorphic phantom. After a standard treatment preparation, which includes the generation of a template plan, the treatment can then be adapted each day, based on daily imaging acquired on an in-room CT. The template structures are then rigidly propagated to this CT and the daily plan is fully re-optimized using the same field arrangement, DVH constraints and optimization settings of the template plan. After a dedicated plan QA, the daily plan is delivered. To minimize the time between imaging and delivery, clinically integrated software for efficient execution of all online adaption steps, as well as tools for comprehensive and automated QA checks, have been developed. Film measurements of an end-to-end validation of a multi-fraction DAPT treatment showed high agreement to the calculated doses. Gamma pass rates with a 3%/3 mm criteria were >92% when comparing the measured dose to the template plan. Additionally, a gamma pass rate >99% was found comparing measurements to the Monte Carlo dose of the daily plans reconstructed from the logfile, accumulated over the delivered fractions. With this, we experimentally demonstrate that the described adaptive workflow can be delivered accurately in a timescale similar to a standard delivery.

Increasing Demand on Human Capital and Resource Utilization in Radiation Therapy: The Past Decade

To quantify the change resource utilization in radiation therapy in the context of advancing technologies and techniques over the last decade. Prospectively, the time to complete radiation therapy workflow tasks was captured between January 1, 2020, and December 31, 2020. The institutional task workflows are specific to each technique and broadly organized into 4 categories: 3-dimenstional conformal radiation therapy, intensity modulated radiation therapy, volumetric modulated arc therapy simple, and volumetric modulated arc therapy complex. These discipline-specific task times were used to quantify a resource utilization factor, which is the median time taken to complete all tasks for each category divided by the median time for 3-dimensional conformal radiation therapy treatments. Retrospectively, all plans treated between January 1, 2012, and December 31, 2019, were quantified and categorized. The resource factor was applied to determine resource utilization. For context, institutional staffing levels were captured across the same decade for medical dosimetrists, medical physicists, and radiation oncologists. This analysis includes 30,229 patient plans in the retrospective data set and 4747 patient plans in the prospective data set. This analysis demonstrates that over this period, patient numbers increased by approximately 45%, whereas time-based human resources increased by almost 150%. The resource allocation factors for 3-dimenstional conformal radiation therapy, intensity modulated radiation therapy, volumetric modulated arc therapy simple, and volumetric arc therapy complex were 1.0, 2.4, 2.9, and 4.3, respectively. Across the 3 disciplines, staffing levels increased from 15 to 17 (13%) for medical dosimetrists, from 10 to 13 (30%) for medical physicists, and from 16 to 23 (44%) for radiation oncologists. This work demonstrates the increase in resource utilization due to the introduction of advanced technologies and changes in radiation therapy techniques over the past decade. Human resource utilization is the predominant factor and should be considered with increasing patient volume for operational planning.

Prospects for daily online adaptive radiotherapy via ethos for prostate cancer patients without nodal involvement using unedited CBCT auto-segmentation.

Purpose Implementing new online adaptive radiation therapy technologies is challenging because extra clinical resources are required particularly expert contour review. Here, we provide the first assessment of Varian's Ethos™ adaptive platform for prostate cancer using no manual edits after auto‐segmentation to minimize this impact on clinical efficiency.MethodsTwenty‐five prostate patients previously treated at our clinic were re‐planned using an Ethos™ emulator. Clinical target volumes (CTV) included intact prostate and proximal seminal vesicles. The following clinical margins were used: 3 mm posterior, 5 mm left/right/anterior, and 7 mm superior/inferior. Adapted plans were calculated for 10 fractions per patient using Ethos's auto‐segmentation and auto‐planning workflow without manual contouring edits. Doses and auto‐segmented structures were exported to our clinical treatment planning system where contours were modified as needed for all 250 CTVs and organs‐at‐risk. Dose metrics from adapted plans were compared to unadapted plans to evaluate CTV and OAR dose changes.ResultsOverall 96% of fractions required auto‐segmentation edits, although corrections were generally minor (<10% of the volume for 70% of CTVs, 88% of bladders, and 90% of rectums). However, for one patient the auto‐segmented CTV failed to include the superior portion of prostate that extended into the bladder at all 10 fractions resulting in under‐contouring of the CTV by 31.3% ± 6.7%. For the 24 patients with minor auto‐segmentation corrections, adaptation improved CTV D98% by 2.9% ± 5.3%. For non‐adapted fractions where bladder or rectum V90% exceeded clinical thresholds, adaptation reduced them by 13.1% ± 1.0% and 6.5% ± 7.3%, respectively.Conclusion For most patients, Ethos's online adaptive radiation therapy workflow improved CTV D98% and reduced normal tissue dose when structures would otherwise exceed clinical thresholds, even without time‐consuming manual edits. However, for one in 25 patients, large contour edits were required and thus scrutiny of the daily auto‐segmentation is necessary and not all patients will be good candidates for adaptation.

Multi-parametric MRI (mpMRI) for treatment response assessment of radiation therapy.

Magnetic resonance imaging (MRI) plays an important role in the modern radiation therapy (RT) workflow. In comparison with computed tomography (CT) imaging, which is the dominant imaging modality in RT, MRI possesses excellent soft-tissue contrast for radiographic evaluation. Based on quantitative models, MRI can be used to assess tissue functional and physiological information. With the developments of scanner design, acquisition strategy, advanced data analysis, and modeling, multiparametric MRI (mpMRI), a combination of morphologic and functional imaging modalities, has been increasingly adopted for disease detection, localization, and characterization. Integration of mpMRI techniques into RT enriches the opportunities to individualize RT. In particular, RT response assessment using mpMRI allows for accurate characterization of both tissue anatomical and biochemical changes to support decision-making in monotherapy of radiation treatment and/or systematic cancer management. In recent years, accumulating evidence have, indeed, demonstrated the potentials of mpMRI in RT response assessment regarding patient stratification, trial benchmarking, early treatment intervention, and outcome modeling. Clinical application of mpMRI for treatment response assessment in routine radiation oncology workflow, however, is more complex than implementing an additional imaging protocol; mpMRI requires additional focus on optimal study design, practice standardization, and unified statistical reporting strategy to realize its full potential in the context of RT. In this article, the mpMRI theories, including image mechanism, protocol design, and data analysis, will be reviewed with a focus on the radiation oncology field. Representative works will be discussed to demonstrate how mpMRI can be used for RT response assessment. Additionally, issues and limits of current works, as well as challenges and potential future research directions, will also be discussed.

PO-1643 End-2-End testing of PBS proton therapy workflow with the PTW RUBY phantom

PO-1643 End-2-End testing of PBS proton therapy workflow with the PTW RUBY phantom

Range probing as a quality control tool for CBCT-based synthetic CTs: In vivo application for head and neck cancer patients.

PurposeCone‐beam CT (CBCT)‐based synthetic CTs (sCT) produced with a deep convolutional neural network (DCNN) show high image quality, suggesting their potential usability in adaptive proton therapy workflows. However, the nature of such workflows involving DCNNs prevents the user from having direct control over their output. Therefore, quality control (QC) tools that monitor the sCTs and detect failures or outliers in the generated images are needed.This work evaluates the potential of using a range‐probing (RP)‐based QC tool to verify sCTs generated by a DCNN. Such a RP QC tool experimentally assesses the CT number accuracy in sCTs.MethodsA RP QC dataset consisting of repeat CTs (rCT), CBCTs, and RP acquisitions of seven head and neck cancer patients was retrospectively assessed. CBCT‐based sCTs were generated using a DCNN. The CT number accuracy in the sCTs was evaluated by computing relative range errors between measured RP fields and RP field simulations based on rCT and sCT images.ResultsMean relative range errors showed agreement between measured and simulated RP fields, ranging from −1.2% to 1.5% in rCTs, and from −0.7% to 2.7% in sCTs.ConclusionsThe agreement between measured and simulated RP fields suggests the suitability of sCTs for proton dose calculations. This outcome brings sCTs generated by DCNNs closer toward clinical implementation within adaptive proton therapy treatment workflows. The proposed RP QC tool allows for CT number accuracy assessment in sCTs and can provide means of in vivo range verification.

Head and neck multi-organ segmentation on dual-energy CT using dual pyramid convolutional neural networks

Organ delineation is crucial to diagnosis and therapy, while it is also labor-intensive and observer-dependent. Dual energy CT (DECT) provides additional image contrast than conventional single energy CT (SECT), which may facilitate automatic organ segmentation. This work aims to develop an automatic multi-organ segmentation approach using deep learning for head-and-neck region on DECT. We proposed a mask scoring regional convolutional neural network (R-CNN) where comprehensive features are firstly learnt from two independent pyramid networks and are then combined via deep attention strategy to highlight the informative ones extracted from both two channels of low and high energy CT. To perform multi-organ segmentation and avoid misclassification, a mask scoring subnetwork was integrated into the Mask R-CNN framework to build the correlation between the class of potential detected organ’s region-of-interest (ROI) and the shape of that organ’s segmentation within that ROI. We evaluated our model on DECT images from 127 head-and-neck cancer patients (66 training, 61 testing) with manual contours of 19 organs as training target and ground truth. For large- and mid-sized organs such as brain and parotid, the proposed method successfully achieved average Dice similarity coefficient (DSC) larger than 0.8. For small-sized organs with very low contrast such as chiasm, cochlea, lens and optic nerves, the DSCs ranged between around 0.5 and 0.8. With the proposed method, using DECT images outperforms using SECT in almost all 19 organs with statistical significance in DSC (p<0.05). Meanwhile, by using the DECT, the proposed method is also significantly superior to a recently developed FCN-based method in most of organs in terms of DSC and the 95th percentile Hausdorff distance. Quantitative results demonstrated the feasibility of the proposed method, the superiority of using DECT to SECT, and the advantage of the proposed R-CNN over FCN on the head-and-neck patient study. The proposed method has the potential to facilitate the current head-and-neck cancer radiation therapy workflow in treatment planning.