Changes In Patient Anatomy Research Articles

Rapid in vivo EPID image prediction using a combination of analytically calculated attenuation and AI predicted scatter.

The electronic portal imaging device (EPID) can be used in vivo, to detect on-treatment errors by evaluating radiation exiting a patient. To detect deviations from the planning intent, image predictions need to be modeled based on the patient's anatomy and plan information. To date in vivo transit images have been predicted using Monte Carlo (MC) algorithms. A deep learning approach can make predictions faster than MC and only requires patient information for training. To test the feasibility and reliability of creating a deep-learning model with patient data for predicting in vivo EPID images for IMRT treatments. In our approach, the in vivo EPID image was separated into contributions from primary and scattered photons. A primary photon attenuation function was determined by measuring attenuation factors for various thicknesses of solid water. The scatter component ofin vivoEPID images was estimated using a convolutional neural network (CNN). The CNN input was a 3-channel image comprised of the non-transit EPID image and ray tracing projections through a pretreatment CBCT. The predicted scatter component was added to the primary attenuation component to give the full predictedin vivo EPID image. We acquired 193 IMRT fields/images from 93 patients treated on the Varian Halcyon. Model training:validation:test dataset ratios were 133:20:40 images. Additional patient plans were delivered to anthropomorphic phantoms, yielding 75 images for further validation. We assessed model accuracy by comparing model-calculated and measuredin vivoimages with a gamma comparison. Comparing the model-calculated and measured in vivo images gives a mean gamma pass rate for the training:validation:test datasets of 95.4%:94.1%:92.9% for 3%/3 mm and 98.4%:98.4%:96.8% for 5%/3mm. For images delivered to phantom data sets the average gamma pass rate was 96.4% (3%/3 mm criteria). In all data sets, the lower passing rates of some images were due to CBCT artifacts and patient motion that occurred between the time of CBCT and treatment. CONCLUSIONS: The developed deep-learning-based model can generatein vivoEPID images with a mean gamma pass rate greater than 92% (3%/3 mm criteria).This approach provides an alternative to MC prediction algorithms. Image predictions can be made in 30 ms on a standard GPU. In future work, image predictions from this model can be used to detectin vivo treatment errors and on-treatment changes in patient anatomy, providing an additional layer of patient-specific quality assurance.

Optimizing conventional radiotherapy: A synergistic approach with generative artificial intelligence and computational sustainability

Conventional radiotherapy (CR) stands at a critical juncture, poised for transformation through the integration of cutting-edge technologies. This article explores the transformative potential of integrating generative artificial intelligence (GAI) and computational sustainability (CS) principles into CR. The convergence of GAI techniques, such as generative adversarial networks, with CS offers novel approaches for optimizing treatment planning, enhancing precision, and ensuring long-term sustainability in radiotherapy practices. We delve deeper into the personalized medicine strategy facilitated by generative models, taking into account patient-specific anatomical variations and dose optimization. The article highlights the role of GAI in adaptive radiotherapy, enabling real-time adjustments to treatment plans based on dynamic changes in patient anatomy. CS principles contribute to resource optimization and energy efficiency, addressing the environmental impact of CR practices. The synergy between GAI and CS fosters innovations in treatment techniques, data-driven decision-making, and ethical considerations, promoting equitable access and minimizing disparities. This article provides a comprehensive overview of the potential benefits and challenges associated with the integration of GAI and CS in CR, shaping the future of precision, efficiency, and sustainable radiotherapy practices.

Total Ankle Replacement with Built-in Antibiotic Spacer: A Paradigm Shift in the Management of Infected Ankles

Introduction/Purpose: Infection of an ankle fracture is a devastating complication that can lead to chronic pain, limited motion or amputation. Traditional treatment strategies after infection involve aggressive surgical debridement, implant removal and prolonged antibiotic therapy. Non-anatomic cement spacers for the tibiotalar joint have previously been described with mixed results. Articulating spacers have shown improved outcomes and may be used as definitive treatment. Currently, there are no prefabricated TAR spacers on the market. The use of 3D printing to create custom implants is emerging, however, there is a paucity of literature regarding their use. We present a case of post-infectious ankle arthritis in 14 year old patient treated with a 3D printed total ankle replacement with built in antibiotic spacer (TAR-AS). Methods: A CT scan was performed in accordance with computer aided design (CAD) parameters. Bilateral lower extremities were scanned to allow the unaffected side to be mirrored and be the basis for implant design. Slice spacing less than 1.25mm with pixel size of 0.5mm. The studies were in DICOM files and within a timeframe where no significant change in patient anatomy had occurred. The implants were fabricated by selective laser melting (SLM) of cobalt chrome alloy (CoCrMo) by Restor3d (Durham, NC). Our design incorporated a stacked gyroid component to facilitate antibiotic cement impregnation. In terms of surgical technique, the custom TAR-AS followed a similar approach to a patient specific TAR procedure. Prior to implantation, the gyroid component of the TAR-AS was filled with Simplex bone cement with tobramycin (Stryker). Results: At six months postoperatively, our patient reported no limitation in activities. AOFAS scores improved from 46/100 preoperatively to 83/100 at six months postoperatively. Radiographic parameters showed no signs of implant failure, loosening or change in alignment. Intraoperative cultures remained negative. Conclusion: We present a case demonstrating the utilization of 3D generated prostheses for treatment of post-infection ankle arthritis. The TAR-AS represents a significant advancement in the management of ankle infections. This innovative approach combines the benefits of joint replacement and continued antibiotic elution. With further research and continued technological advancements, the TAR-AS has the potential to become the gold standard for the treatment of infected TAR. Despite the promising results, challenges remain in the implementation of TAR-AS. Long term follow up studies are needed to evaluate the durability and longevity of the implant. Total ankle with built in antibiotic spacer Superior view of tibial tray with antibiotic cement packed in gyroid

What We Know About Facial Volume Restoration with Autogenous Fat.

Facial rejuvenation involves a careful analysis of a patient's anatomic changes that are secondary to aging and then the application of several methods, tools, and technologies to reverse those changes. A central component of facial aging is the changes seen in facial soft tissue volume that occurs with atrophy and malpositioning of normal facial volume through several underlying aging processes. Although many surgical and nonsurgical interventions are available to remedy many of the sequela of aging, the restoration of volume is one of the most important goals that has to be engaged. Over the years, autogenous fat has emerged as one of the safer and reliable methods to restore the diminished volume of the aging face. The purpose of this manuscript is to relate some of the history, clinical practices, research, and current literature supporting the use of autologous fat in facial rejuvenation.

EPIDEEP: Using a Deep Learning Model to Predict In Vivo Electronic Portal Imaging Device (EPID) Transit Images

To create a deep-learning model to predict in-vivo electronic portal imaging device (EPID) transit images for IMRT treatments. This model was created to predict in-vivo images to identify machine and patient-related errors that occur during beam delivery and are undetectable with current QA approaches. The deep-learning model can make image predictions much faster than Monte Carlo approaches, making image prediction feasible for application in online adaptive radiotherapy. Additionally, the model does not rely on any proprietary information and can be easily utilized by other clinics. Our approach separates the primary and scatter components of in-vivo transit images. The attenuation of primary radiation reaching the EPID panel is modeled analytically, using attenuation measurements from phantoms of known thicknesses. The scatter component is estimated using a convolutional neural network (CNN). The CNN training uses information from the on-treatment cone-beam CTs (CBCTs), and a pretreatment EPID image with no patient in the beam. We acquired 193 IMRT fields/images from 118 patients previously treated on the Varian Halcyon. Treatment sites included the pelvis, abdomen, lungs, and extremities. CBCTs were collected immediately before treatment, to provide an accurate representation of the anatomy. A 3-channel input image was used, consisting of the pretreatment EPID image, a ray tracing projection through the CBCT to the EPID panel, and a projection to isocenter. Model training:validation:test set ratios were 133:20:40 images. The primary and scatter components are added together to give the predicted transit image. Prediction accuracy was assessed by comparing model-predicted and measured in-vivo EPID images with a 3%/3mm and 5%/3mm gamma pass rate. The gamma pass rate for the patients in the training:validation:test was 91.5%:90.4%:92.1% for 3%/3mm and 96.7%:96.6%:97.0% for 5%/3mm. The model can make image predictions in 20 milliseconds. The poor passing rates of some images may be due to CBCT artifacts and patient motion that occurs between the time of CBCT and treatment. This model can predict in-vivo EPID images with an average gamma pass rate greater than 90%. Image predictions from this model can be used to detect in-vivo treatment errors and changes in patient anatomy, providing an additional layer of patient-specific quality assurance. The speed of image predictions is 20 milliseconds, making use feasible for online adaptive treatments, which currently do not utilize patient-specific measurements of the delivered radiation. Upcoming studies will assess the model's ability in detecting clinically relevant errors and changes in patient anatomy that can adversely affect treatment. Future goals include acquiring more data to further improve the model and extending the model to make predictions for VMAT treatments.

Comparison and Breakdown of Cost in Head and Neck Cancer Radiation Therapy and Adaptive Replanning

Head and neck cancers pose a significant challenge to radiation oncologists due to several factors, including rapid shrinkage during radiation and patient weight loss. As the target and patient anatomy change, re-simulation and re-planning are often necessary, which precipitates additional time, resources, and cost. However, the true added cost of providing adaptive replanning for head and neck cancer patients receiving radiation therapy (RT) is often unknown. We hypothesize that adaptive replanning adds significant cost to treatment, primarily driven by the additional personnel time in the replanning process. The total costs of providing standard radiation therapy and treatment with adaptive replanning for head and neck cancer were calculated using time-driven activity-based costing. We created process maps for each step from consultation to end of treatment, utilizing data that included the space, personnel involved, equipment and supplies used, and time spent. Capacity cost rates were calculated for all resources using a combination of publicly accessible cost data, financial data from our institution, and time averages for each activity obtained from personnel interviews and clinical measurements. We included the following domains in our process maps: consultation, CT simulation, treatment planning, new treatment start, daily beam treatments, weekly on-treatment visits, CT Re-simulation, treatment replanning, and new start for replanned treatment. We found that the total cost for providing standard therapy was $12,216.39 US Dollars (USD), while the total cost of adaptive replanning was $14,705.14 USD, for a relative increase in cost of 20.4%. The most expensive factor for both treatment arms was the cost of daily treatments on the linear accelerator, representing 66.1% and 53.3% of the standard and adaptive replanning, respectively. The second largest driver of cost for both was weekly on-treatment visits with a physician, representing 11.37% and 9.45% of the total cost, respectively. Of the total cost difference between standard therapy and adaptive replanning, 64% was due to personnel costs, 27.9% was due to space/equipment costs, and 8.1% was attributable to material costs. We found that adaptive replanning increased the overall cost of radiotherapy for head and neck cancers by a significant amount. The additional personnel time with resim and replanning, as well as machine time on the CT simulator are the primary reasons for this increase. In addition, use of these resources represents an opportunity cost in the clinic. Opportunities to make adaptive replanning more cost-effective include incorporation of AI tools for volume delineation and/or treatment planning to reduce personnel time, as well as using daily cone beam CT images instead of a new CT simulation, when feasible.

Estimating Potential Benefits of Online Adaptive Proton Therapy for Head-and-Neck Cancer: A Retrospective Study

Proton therapy is highly sensitive to anatomical changes and setup variations in head-and-neck (HN) treatments. To address this issue, proton centers often acquire patient CT images weekly to monitor patient anatomical changes during the treatment course and perform offline plan adaptation when needed. However, offline adaptation cannot fully account for daily setup variations or the anatomical changes occurring with high frequency. There are a few groups endeavoring to develop advanced technologies to enable online adaptive proton therapy (APT). However, the necessity of online APT remains controversial, as it is unknown that whether online APT will significantly improve treatment quality and outcomes compared to offline APT. The purpose of this study is to estimate the clinical potential of online APT in the management of HN cancers in relation to the current offline APT. Our retrospective study was conducted with four HN patients (35 fractions per patient), who had been treated with intensity modulated proton therapy and had offline adaptation once or twice during their treatment courses. Synthetic CT (sCT) images were generated from 140 daily CBCT images for us to recalculate the dose of the treatment plan in patient's actual treatment anatomy for each treatment fraction and adapt the plan when warranted. These adaptations were assumed to be performed online before treatment delivery to mimic an online APT course. Accumulative doses were calculated for both courses using the CBCT-based sCT images of every fraction for us to compare the target coverage, organ at risk (OAR) sparing, tumor control probability (TCP) and normal tissue complication probability (NTCP). An in-house script was developed to semi-automate this process in a commercial treatment planning system to facilitate our study. All patients would benefit from online APT to different extents. For the first patient, with OAR doses comparable to the actual offline course, the retrospective online APT course improved dose coverages of the three CTVs from 95.2%, 98.64% and 89.53% to 98.88%, 99.81%, 98.97%, which would lead to a 4.52% improvement in TCP. Similarly, online APT would yield a 2.66% improvement in TCP for the second patient. For the third patient, with comparable CTV dose coverages, the mean doses of right parotid and oral cavity were decreased from 29.52 Gy relative biological effectiveness (RBE) and 41.89 Gy RBE to 22.16 Gy RBE and 34.61 Gy RBE, leading to a reduce of 1.67% and 3.40% in NTCP. The mean dose of right parotid was decreased from 21.71 Gy RBE to 19.37 Gy RBE for the last patient, leading to a reduce of 0.73% in NTCP. Our results showed that online APT could better maintain the treatment plan quality than offline APT for all the four patients, despite their significant anatomical changes. Future investigation will focus on collecting more patient data to obtain statistically significant results and help identify the patients to whom the online APT will be of most benefit.

Adaptive Radiation Therapy: A Review of CT-based Techniques.

Adaptive radiation therapy is a feedback process by which imaging information acquired over the course of treatment, such as changes in patient anatomy, can be used to reoptimize the treatment plan, with the end goal of improving target coverage and reducing treatment toxicity. This review describes different types of adaptive radiation therapy and their clinical implementation with a focus on CT-guided online adaptive radiation therapy. Depending on local anatomic changes and clinical context, different anatomic sites and/or disease stages and presentations benefit from different adaptation strategies. Online adaptive radiation therapy, where images acquired in-room before each fraction are used to adjust the treatment plan while the patient remains on the treatment table, has emerged to address unpredictable anatomic changes between treatment fractions. Online treatment adaptation places unique pressures on the radiation therapy workflow, requiring high-quality daily imaging and rapid recontouring, replanning, plan review, and quality assurance. Generating a new plan with every fraction is resource intensive and time sensitive, emphasizing the need for workflow efficiency and clinical resource allocation. Cone-beam CT is widely used for image-guided radiation therapy, so implementing cone-beam CT-guided online adaptive radiation therapy can be easily integrated into the radiation therapy workflow and potentially allow for rapid imaging and replanning. The major challenge of this approach is the reduced image quality due to poor resolution, scatter, and artifacts. Keywords: Adaptive Radiation Therapy, Cone-Beam CT, Organs at Risk, Oncology © RSNA, 2023.

CBCT-guided adaptive radiotherapy using self-supervised sequential domain adaptation with uncertainty estimation.

Adaptive radiotherapy (ART) is an advanced technology in modern cancer treatment that incorporates progressive changes in patient anatomy into active plan/dose adaption during the fractionated treatment. However, the clinical application relies on the accurate segmentation of cancer tumors on low-quality on-board images, which has posed challenges for both manual delineation and deep learning-based models. In this paper, we propose a novel sequence transduction deep neural network with an attention mechanism to learn the shrinkage of the cancer tumor based on patients' weekly cone-beam computed tomography (CBCT). We design a self-supervised domain adaption (SDA) method to learn and adapt the rich textural and spatial features from pre-treatment high-quality computed tomography (CT) to CBCT modality in order to address the poor image quality and lack of labels. We also provide uncertainty estimation for sequential segmentation, which aids not only in the risk management of treatment planning but also in the calibration and reliability of the model. Our experimental results based on a clinical non-small cell lung cancer (NSCLC) dataset with sixteen patients and ninety-six longitudinal CBCTs show that our model correctly learns weekly deformation of the tumor over time with an average dice score of 0.92 on the immediate next step, and is able to predict multiple steps (up to 5 weeks) for future patient treatments with an average dice score reduction of 0.05. By incorporating the tumor shrinkage predictions into a weekly re-planning strategy, our proposed method demonstrates a significant decrease in the risk of radiation-induced pneumonitis up to 35% while maintaining the high tumor control probability.

The application of gradient dose segmented analysis of in-vivo EPID images for patients undergoing VMAT in a resource-constrained environment.

The gamma analysis metric is a commonly used metric for VMAT plan evaluation. The major drawback of this is the lack of correlation between gamma passing rates and DVH values. The novel GDSAmean metric was developed by Steers etal. to quantify changes in the PTV mean dose (Dmean ) for VMAT patients. The aim of this work is to apply the GDSA retrospectively on head-and-neck cancer patients treated on the newly acquired Varian Halcyon, to assess changes in GDSAmean , and to evaluate the cause of day-to-day changes in the time-plot series. In-vivo EPID transmission images of head-and-neck cancer patients treated between August 2019 and July 2020 were analyzed retrospectively. The GDSAmean was determined for all patients treated. The changes in patient anatomy and rotational errors were quantified using the daily CBCT images and added to a time-plot with the daily change in GDSAmean . Over 97% of the delivered treatment fractions had a GDSAmean <3%. Thirteen of the patients received at least one treatment fraction where the GDSAmean >3%. Most of these deviations occurred for the later fractions of radiotherapy treatment. Additionally, 92% of these patients were treated for malignancies involving the larynx and oropharynx. Notable deviations in the effective separation diameters were observed for 62% of the patients where the change in GDSAmean >3%. For the other five cases with GDSAmean <3%, the mean pitch, roll, and yaw rotational errors were 0.90°, 0.45°, and 0.43°, respectively. A GDSAmean >3% was more likely due to a change in separation, whereas a GDSAmean <3% was likely caused by rotational errors. Pitch errors were shown to be the most dominant. The GDSAmean is easily implementable and can aid in scheduling new CT scans for patients before significant deviations in dose delivery occur.

Error detection using EPID-based 3D in vivo dose verification for lung stereotactic body radiotherapy

PurposeTo investigate the error detectability limitations of an EPID-based 3D in vivo dosimetry verification system for lung stereotactic body radiation therapy (SBRT). MethodsThirty errors were intentionally introduced, consisting of dynamic and constant machine errors, to simulate the possible errors that may occur during delivery. The dynamic errors included errors in the output, gantry angle and MLC positions related to gantry inertial and gravitational effects, while the constant errors included errors in the collimator angle, jaw positions, central leaf positions, setup shift and thickness to simulate patient weight loss. These error plans were delivered to a CIRS phantom using the SBRT technique for lung cancer. Following irradiation of these error plans, the dose distribution was reconstructed using iViewDose™ and compared with the no error plan. ResultsAll errors caused by the central leaf positions, dynamic MLC errors, Jaw inwards movements, setup shifts and patient anatomical changes were successfully detected. However, dynamic gantry angle and collimator angle errors were not detected in the lung case due to the rotation-symmetric target shape. The results showed that the γmean and γpassrate indicators can detect 13 (81.3%) and 14 (87.5%) of the 16 errors respectively without including the gantry angle error, collimator angle error and output error. ConclusionsIn summary, iViewDose™ is an appropriate approach for detecting most types of clinical errors for lung SBRT. However, the phantom results also showed some detectability limitations of the system in terms of dynamic gantry angle and constant collimator angle errors.

Online Adaptive Radiation Therapy and Opportunity Cost

Changes in patient anatomy and tumor geometry pose a challenge to ensuring consistent target coverage and organ-at-risk sparing; online adaptive radiation therapy (ART) accounts for these interfractional changes by facilitating replanning before each treatment. This project explored the opportunity cost of computed tomography (CT)-based online ART by evaluating time and human resource requirements. Time-driven activity-based costing (TDABC) was employed to determine the cost of this time to assess if the dosimetric benefit is worthwhile. CT-based online ART was recently employed at our institution and has been used to treat pelvic disease sites (prostate, prostate bed, prostate with nodal coverage, bladder, rectum); data points from all adaptively treated patients (415 fractions) were used. Time taken for each adaptive fraction before treatment, which at our facility is best represented by the duration between 2 cone beam CT scans, was used as a broadly applicable and transferable metric, representing the additional time required for ART on top of standard image guided radiation therapy. Dosimetric effect was also considered by taking the difference of planning target volume V100% for the scheduled and adapted plans. Using recently validated TDABC at this facility, the per fraction cost of ART was determined, reflecting the added cost of ART on top of image guided radiation therapy. A median time of 15.97 (interquartile range, 13.23-18.83) additional minutes was required for each adaptive fraction. TDABC demonstrated an average minimum cost per adapted fraction of $103.58. Dosimetric differences between V100% of the scheduled versus adapted plan showed a mean dosimetric difference of 15.8%. Although online ART decreases the uncertainty of anatomic shifts, each adaptive fraction requires more staff time, delaying completion of other tasks and increasing resource utilization. Although toxicity benefits require further studies, the implementation of progressively complex radiation therapy technologies, like ART, requires consideration of the time and human resource requirements and subsequent opportunity cost.

Density Overrides that Best Predict Bowel Changes during Pencil Beam Scanning Proton Radiotherapy for Prostate Cancer

<h3>Purpose/Objective(s)</h3> Changes in patient anatomy during pencil beam scanning proton radiotherapy (PBSPT) can result in substantial differences in dose distribution. In patients with prostate cancer receiving elective nodal irradiation (ENI), interfraction variability in bowel position or bowel filling can highlight this uncertainty. Such changes are often managed by assessing the plan on CT dataset copies with the bowel contour overridden to other densities. The ideal Hounsfield Unit (HU) overrides remain unclear, but extremes (HU=0 water and HU=-1000 air) are often utilized. The aim of the present study is to determine the bowel density overrides that are most predictive of bowel changes during a course of PBSPT. <h3>Materials/Methods</h3> Consecutive patients, from a single institution, receiving PBSPT with ENI for intact prostate cancer were retrospectively reviewed for the period between 10/2020-12/2021. Proton radiotherapy consisted of 50.4 Gy in 28 fractions to the pelvic lymph node stations with a simultaneous integrated boost to 70 Gy in 28 fractions to the prostate. Individually contoured loops of small bowel, large bowel, and rectum were overridden to HU of 0 to -1000 in 100 HU intervals. A bowel evaluation structure, consisting of a bowel bag cropped 3 cm away from the high-risk volume, was created; its dose volume histogram was reviewed; and values for the maximum point dose (Dmax), V105%, V45Gy, V20Gy were recorded for each plan. The dosimetry of bowel override plans were compared to the patient's Quality Assurance CT (QACT) scans. The bowel override plan which most closely matched the dosimetry of each QACT for each dosimetric parameter was determined, and the prevalence of closest prediction for each override value was tabulated. <h3>Results</h3> A total of 32 QACTs from 12 patients were included. For Dmax, HU=0, HU=-100, and HU=-700 predicted most accurately with 10 (31.3%), 5 (15.6%), and 4 (12.5%) closest predictions, respectively. For V105%, HU=-700 and HU=-1000 performed best with 6 (18.8%) and 7 (21.9%), respectively. For V45, HU=0 and HU=-500 predicted best with 13 (40.6%) and 5 (15.6%), respectively. For V20, HU=0, HU=-200, and HU=-600 predicted best with 18 (56.3%), 4 (12.5%), and 3 (9.4%), respectively. Overall, HU=0, -100, -600, -700, and -1000 predicted best with 42 (32.8%), 11 (8.6%), 10 (7.8%), 12 (9.4%), and 12 (9.4%) out of the 128 total dose metrics, respectively. This created a relative bimodal distribution with peaks in the 0 to -100 and -600 to -700 ranges. The nominal plan predicted best in 13 scenarios. <h3>Conclusion</h3> Bowel density override plans remain an important tool for assessment of PBSPT robustness to bowel filling changes throughout the course of therapy as the nominal CT relatively poorly predicts QACT dosimetry. The relatively bimodal distribution of accurate predictions in this study would suggest that the most predictive overrides would be HU∼0 and HU∼-650. Future investigation into multidataset robust optimization with these overrides to improve plan robustness is planned.

Online Adaptive Radiotherapy and Opportunity Cost

<h3>Purpose/Objective(s)</h3> Changes in patient anatomy and tumor geometry pose a challenge to ensuring consistent target coverage and organs-at-risk (OAR) sparing; online adaptive radiotherapy (ART) has been used to account for these inter-fractional changes by facilitating replanning prior to each treatment. This project explores the opportunity cost of CT-based online ART by evaluating the time and human resource requirements for adaptive versus conventional image-guided radiation therapy (IGRT). This study seeks to quantify time requirements for ART and, using time driven activity-based costing (TDABC), determine the cost of this time to determine if the dosimetric benefit is worthwhile. <h3>Materials/Methods</h3> Online ART for pelvic disease sites was evaluated using Ethos based planning technology. Time, which at our facility is best represented by the duration between two cone beam CT (CBCT) scans taken for each adaptive fraction, was used as a broadly applicable and transferable metric, representing the additional time required for adaptive treatment on top of standard IGRT. Dosimetric impact was also considered. All parties (dosimetrist, therapists, physicist, physician) need to be present for the duration of treatment planning given the rapid response time required. Using recently validated TDABC at this facility, the per fraction cost of ART was determined, reflecting the added cost of ART on top of IGRT. <h3>Results</h3> Preliminary data suggests a time difference of approximately 7 versus 24 minutes for daily IGRT and ART, respectively. Each adaptive fraction requires more staffing for the duration of this time delaying completion of other tasks and functionally increasing resource utilization. <h3>Conclusion</h3> While online ART decreases the uncertainty of anatomical shifts, the added human resources required per fraction complicate the workflow of daily treatment and increase expense. This has an even greater impact on the implementation of ART in the context of the alternative payment model. Moreover, online ART removes physicians from clinical time. This opportunity cost is likely to be similar across different disease sites, unlike toxicity benefits which may vary more depending on anatomic location and require further longitudinal studies. Regardless, the implementation of progressively complex radiotherapy technologies, like ART, requires consideration of the time and human resource requirements and subsequent opportunity cost.

Assessment of dose gradient index variation during simultaneously integrated boost intensity‐modulated radiation therapy for head and neck cancer patients

AbstractObjectiveDose gradient index (DGI) is a tool used to evaluate radiation dose gradient outside the target. This study aimed to analyze the consistency of this tool through the long course of radiotherapy due to patient anatomical changes, such as body weight loss and tumor shrinkage.MethodsA total of 30 patients diagnosed with different head and neck cancer were treated with the simultaneous integrated boost intensity‐modulated radiation therapy technique; the patients underwent new computed tomography (CT) simulations after 10 and 20 treatment sessions. The gradient index was compared for the initial, reconstructed, and adaptive plans.ResultsAll patients showed a significant decrease (p &lt; 0.001) in weight at reconstructed CT1 (RCT1) and reconstructed CT2 compared with original CT. Also, primary gross tumor volume was significantly decreased (p &lt; 0.001) at reconstructed CT1 and reconstructed CT2. In the dosimetric part, all organs showed a significant increase in dose delivery at reconstructed plans (Rplans) compared with the original plan (Oplan). Meanwhile, at adaptive plans (Aplans), all organs showed a significant decrease in dose delivery compared with Oplan. The DGI was significantly increased at Rplan1 and Rplan2 compared with Oplan, with a median value of 29 (15.5–41.3) and 30 (15.1–38) at Rplan1 and Rplan2, respectively, and 25.8 (14.9–37.6) at Oplan. Whereas DGI value decreased significantly at Aplan1 compared with Oplan, and then insignificantly increased at Aplan2 compared with Aplan1.ConclusionDGI must be evaluated during the intensity‐modulated radiation therapy course in the treatment of head and neck cancers, which clearly varies significantly as a result of a patient's anatomical changes during the radiotherapy course, and it can be improved or maintained to its original value by using adaptive planning strategy.

Analysis of the Rate of Re-planning in Spot-Scanning Proton Therapy.

PurposeFinite proton range affords improved dose conformality of radiation therapy when patient regions-of-interest geometries are well characterized. Substantial changes in patient anatomy necessitate re-planning (RP) to maintain effective, safe treatment. Regularly planned verification scanning (VS) is performed to ensure consistent treatment quality. Substantial resources, however, are required to conduct an effective proton plan verification program, which includes but is not limited to, additional computed tomography (CT) scanner time and dedicated personnel: radiation therapists, medical physicists, physicians, and medical dosimetrists.Materials and MethodsVerification scans (VSs) and re-plans (RPs) of 711 patients treated with proton therapy between June 2015 and June 2018 were studied. All treatment RP was performed with the intent to maintain original plan integrity and coverage. The treatments were classified by anatomic site: brain, craniospinal, bone, spine, head and neck (H&N), lung or chest, breast, prostate, rectum, anus, pelvis, esophagus, liver, abdomen, and extremity. Within each group, the dates of initial simulation scan, number of VSs, number of fractions completed at the time of VS, and the frequency of RP were collected. Data were analyzed in terms of rate of RP and individual likelihood of RP.ResultsA total of 2196 VSs and 201 RPs were performed across all treatment sites. H&N and lung or chest disease sites represented the largest proportion of plan modifications in terms of rate of re-plan (RoR: 54% and 58%, respectively) and individual likelihood of RP on a per patient basis (likelihood of RP [RP%]: 46% and 39%, respectively). These sites required RP beyond 4 weeks of treatment, suggesting continued benefit for frequent, periodic VS. Disease sites in the lower pelvis demonstrated a low yield for RP per VS (0.01-0.02), suggesting that decreasing VS frequency, particularly late in treatment, may be reasonable.ConclusionsA large degree of variation in RoR and individual RP% was observed between anatomic treatment sites. The present retrospective analysis provides data to help develop anatomic site–based VS protocols.

The dose accumulation and the impact of deformable image registration on dose reporting parameters in a moving patient undergoing proton radiotherapy.

IntroductionPotential changes in patient anatomy during proton radiotherapy may lead to a deviation of the delivered dose. A dose estimate can be computed through a deformable image registration (DIR) driven dose accumulation. The present study evaluates the accumulated dose uncertainties in a patient subject to an inadvertent breathing associated motion.Materials and methodsA virtual lung tumour was inserted into a pair of single participant landmark annotated computed tomography images depicting opposite breathing phases, with the deep inspiration breath-hold the planning reference and the exhale the off-reference geometry. A novel Monte Carlo N-Particle, Version 6 (MCNP6) dose engine was developed, validated and used in treatment plan optimization. Three DIR methods were compared and used to transfer the exhale simulated dose to the reference geometry. Dose conformity and homogeneity measures from International Committee on Radioactivity Units and Measurements (ICRU) reports 78 and 83 were evaluated on simulated dose distributions registered with different DIR algorithms.ResultsThe MCNP6 dose engine handled patient-like geometries in reasonable dose calculation times. All registration methods were able to align image associated landmarks to distances, comparable to voxel sizes. A moderate deterioration of ICRU measures was encountered in comparing doses in on and off-reference anatomy. There were statistically significant DIR driven differences in ICRU measures, particularly a 10% difference in the relative D98% for planning tumour volume and in the 3 mm/3% gamma passing rate.ConclusionsT he dose accumulation over two anatomies resulted in a DIR driven uncertainty, important in reporting the associated ICRU measures for quality assurance.

Development of a Monte Carlo based robustness calculation and evaluation tool.

BackgroundEvaluating plan robustness is a key step in radiotherapy.PurposeTo develop a flexible Monte Carlo (MC)‐based robustness calculation and evaluation tool to assess and quantify dosimetric robustness of intensity‐modulated radiotherapy (IMRT) treatment plans by exploring the impact of systematic and random uncertainties resulting from patient setup, patient anatomy changes, and mechanical limitations of machine components.MethodsThe robustness tool consists of two parts: the first part includes automated MC dose calculation of multiple user‐defined uncertainty scenarios to populate a robustness space. An uncertainty scenario is defined by a certain combination of uncertainties in patient setup, rigid intrafraction motion and in mechanical steering of the following machine components: angles of gantry, collimator, table‐yaw, table‐pitch, table‐roll, translational positions of jaws, multileaf‐collimator (MLC) banks, and single MLC leaves. The Swiss Monte Carlo Plan (SMCP) is integrated in this tool to serve as the backbone for the MC dose calculations incorporating the uncertainties. The calculated dose distributions serve as input for the second part of the tool, handling the quantitative evaluation of the dosimetric impact of the uncertainties. A graphical user interface (GUI) is developed to simultaneously evaluate the uncertainty scenarios according to user‐specified conditions based on dose‐volume histogram (DVH) parameters, fast and exact gamma analysis, and dose differences. Additionally, a robustness index (RI) is introduced with the aim to simultaneously evaluate and condense dosimetric robustness against multiple uncertainties into one number. The RI is defined as the ratio of scenarios passing the conditions on the dose distributions. Weighting of the scenarios in the robustness space is possible to consider their likelihood of occurrence. The robustness tool is applied on IMRT, a volumetric modulated arc therapy (VMAT), a dynamic trajectory radiotherapy (DTRT), and a dynamic mixed beam radiotherapy (DYMBER) plan for a brain case to evaluate the robustness to uncertainties of gantry‐, table‐, collimator angle, MLC, and intrafraction motion. Additionally, the robustness of the IMRT, VMAT, and DTRT plan against patient setup uncertainties are compared. The robustness tool is validated by Delta4 measurements for scenarios including all uncertainty types available.ResultsThe robustness tool performs simultaneous calculation of uncertainty scenarios, and the GUI enables their fast evaluation. For all evaluated plans and uncertainties, the planning target volume (PTV) margin prevented major clinical target volume (CTV) coverage deterioration (maximum observed standard deviation of D98%CTV was 1.3 Gy). OARs close to the PTV experienced larger dosimetric deviations (maximum observed standard deviation of D2%chiasma was 14.5 Gy). Robustness comparison by RI evaluation against patient setup uncertainties revealed better dosimetric robustness of the VMAT and DTRT plans as compared to the IMRT plan. Delta4 validation measurements agreed with calculations by >96% gamma‐passing rate (3% global/2 mm).ConclusionsThe robustness tool was successfully implemented. Calculation and evaluation of uncertainty scenarios with the robustness tool were demonstrated on a brain case. Effects of patient and machine‐specific uncertainties and the combination thereof on the dose distribution are evaluated in a user‐friendly GUI to quantitatively assess and compare treatment plans and their robustness.

Using eclipse scripting to fully automate in-vivo image analysis to improve treatment quality and safety.

PurposeAn automated, in‐vivo system to detect patient anatomy changes and machine output was developed using novel analysis of in‐vivo electronic portal imaging device (EPID) images for every fraction of treatment on a Varian Halcyon. In‐vivo approach identifies errors that go undetected by routine quality assurance (QA) to compliment daily machine performance check (MPC), with minimal physicist workload.MethodsImages for all fractions treated on a Halcyon were automatically downloaded and analyzed at the end of treatment day. For image analysis, compared to first fraction, the mean difference of high‐dose region of interest is calculated. This metric has shown to predict changes in planning treatment volume (PTV) mean dose. Flags are raised for: (Type‐A) treatment fraction whose mean difference exceeds 10%, to protect against large errors, and (Type‐B) patients with three consecutive fractions with mean exceeding ±3%, to protect against systematic trends. If a threshold is exceeded, a physicist is e‐mailed, a report for flagged patients, for investigation. To track machine output changes, for all patients treated on a day, the average and standard deviations are uploaded to a QA portal, along with the reviewed MPC, ensuring comprehensive QA for the Halcyon. To guide clinical implementation, a retrospective study from November 2017 till December 2020 was conducted, which grouped errors by treatment site. This framework has been used prospectively since January 2021.ResultsFrom retrospective data of 1633 patients (35 759 fractions), no Type‐A errors were found and only 45 patients (2.76%) had Type‐B errors. These Type‐B deviations were due to head‐and‐neck weight loss. For 6 months of prospective use (345 patients), 13 patients (3.7%) had Type‐B errors and no Type‐A errors.ConclusionsThis automated system protects against errors that can occur in vivo to provide a more comprehensive QA. This fully automated framework can be implemented in other centers with a Halcyon, requiring a desktop computer and analysis scripts.

OD42 - Changes in patient anatomy or setup: replanning predictive power of tomotherapy sinograms gamma analysis by using Delivery Analysis

OD42 - Changes in patient anatomy or setup: replanning predictive power of tomotherapy sinograms gamma analysis by using Delivery Analysis