CT Simulation Research Articles

Feasibility and Workflow Efficiency in MRI-Based HDR Brachytherapy for Cervical Cancer at a Community-Based Practice

Image-guided brachytherapy (IGBT) has seen widespread use in high-volume centers around the country in the last ten years. GEC-ESTRO recommendations stipulate that the dose be prescribed to the high-risk clinical target volume, which includes the residual tumor present at the time of brachytherapy. This practice has resulted in lesser normal tissue toxicity and improved local control. We introduced MRI volumetric planning at our center three years ago to confer this advantage to our patients. Based on a practice pattern survey by the American brachytherapy society in 2014, IGBT with MRI-based planning was grossly underutilized due to challenges associated with the work stream and availability of MRI. The combined mean procedure time for MR imaging and planning reported at a high-volume brachytherapy center was 63.2 minutes. Our study aimed to identify the time required for MRI-based IGBT for the first HDR fraction for cervical cancer patients at our institution. We aim to understand how we differ from a typical high-volume center and how we may improve our efficiency and workflow. Between 2020 and 2023, 14 patients were treated with HDR brachytherapy for a diagnosis of cervical cancer. Of these individuals, 7 patients were treated with MRI-guided IGBT during the first HDR fraction. The following time intervals were recorded: 1. Time from applicator insertion to completion of MRI imaging 2. Time from completion of MRI imaging to start of CT simulation 3. Time from start of CT simulation to completion of treatment delivery Materials and methods: Between 2020 and 2023, 14 patients were treated with HDR brachytherapy for a diagnosis of cervical cancer. Of these individuals, 7 patients were treated with MRI-guided IGBT during the First HDR fraction. The following time intervals were recorded: 1. Time from applicator insertion to completion of MRI imaging 2. Time from completion of MRI imaging to start of CT simulation 3. Time from start of CT simulation to completion of treatment delivery. The mean total procedure time was 582 minutes (Range 494- 665). The mean time and ranges for each phase were as follows. The mean time from the implant insertion to MRI completion was 157.7 minutes (Range 70-307), and the time from MRI completion to the start of CT simulation was 114.7 minutes (Range 17-173), and the time from the start of CT simulation to the treatment delivery was 310 minutes (Range 251-476). Our study demonstrates that MRI-based HDR brachytherapy with a skilled multidisciplinary team in a small community-based practice with an MRI imaging facility is a feasible approach similar to a high-volume center but differs in the total treatment time. Considering high treatment time in the community-based practice, the department is taking a team approach to improve efficiency. This includes establishing a dedicated brachytherapy suite closer to the MRI imaging and incorporating artificial intelligence to assist planning.

Assessment of the Spatial Bio-Distribution of NBTXR3 for Locally Advanced or Borderline-Resectable Pancreatic Ductal Adenocarcinoma (LAPC or BRPC) Patients Undergoing Radiation Therapy

NBTXR3 is a novel radioenhancer composed of functionalized hafnium oxide nanoparticles that are injected directly into the tumor. An ongoing phase I trial is assessing the application of NBTXR3 in the treatment of locally-advanced or borderline-resectable pancreatic ductal adenocarcinoma, with the primary goal of determining the recommended NBTXR3 dose for a subsequent phase II trial. Using patients from the ongoing phase I trial, we evaluated the spatial bio-distribution of NBTXR3 in the pancreas as function of time and assessed if there is migration of the compound during radiation therapy. Adult patients with LAPC or BRPC were enrolled. Analysis of 8 patients who underwent CT simulation at least 24 hours after receiving the NBTXR3 intratumoral injection in the pancreatic lesion under general anesthesia guided by an endoscopic ultrasound was conducted. Patients were simulated with and without iodinated intravenous contrast under breath hold, with 3mm CT slice thickness and 50-60cm FoV. A contrast-enhanced CT series was chosen for RT planning purposes. All patients were planned for a 15-fraction IMRT course using 6-MV beam energy, with 45Gy and 37.5Gy dose levels (using a simultaneous integrated boost technique) to gross primary tumor and microscopic disease/regional nodes, respectively. Daily image guidance included either cone-beam CT (CBCT, n = 1) or CT-on-Rails (CTOR, n = 7) and were fused to the planning CT images using Velocity, ver. 3.0.1. The NBTXR3 volume was determined by thresholding the images to 165 HU and 300 HU for CTOR and CBCT images, respectively. Bones, stents, and artifacts were manually removed from the NBTXR3 volume. The volumes for the NBTXR3 and the NBTXR3 overlapping with the GTV for each patient were individually fit to a linear model in R, ver. 4.1.3. One patient from the CTOR cohort was excluded from analysis due to the small volume of NBTXR3 (0.02 cm3) compared to the rest of the cohort (0.716 - 6.917 cm3). The total volume of NBTXR3 in the CBCT images could not be analyzed due the substantial image artifacts, only the volume overlapping the GTV was calculated. There were no statistically significant changes (p > 0.1 Adj-R2 = 0.99) in the raw volume of NBTXR3 for the duration of the treatment in all CTOR patients. For the NBTXR3 volume overlapping with the GTV, five cases did not present with a volume change during the treatment (p > 0.1 Adj-R2 = 0.99), while one CTOR case had a reduction in volume of about 1.5%/day (p = 0.7), and the CBCT case had a reduction of 3%/day (p < 0.001). The raw volume of NBTXR3 injected in the pancreas did not significantly change over the duration of the three weeks of treatment. We also did not observe significant changes in the NBTXR3 volume overlapping the GTV in most cases. The stability of NBTXR3 was demonstrated during RT.

Creation and Implementation of an Interdisciplinary Workflow for CBCT-Based Online Adaptive Radiotherapy

CBCT-based online adaptive radiotherapy (OART) is an emerging treatment strategy to replan based on the anatomy of the day while the patient remains on the couch. OART is not just an add-on to the current workflow; it necessitates a new approach across the patient's path of care, from CT simulation to treatment delivery. OART requires the addition of duties to clinical personnel, strategies to create auto-plan templates, and monitoring the "black box" adaptation process. Studies have shown that OART implementation is limited by its resource-intensive nature and the risks associated with the treatment approach. We hypothesized that the implementation of an interdisciplinary, streamlined workflow and checklists would enhance the OART treatment efficiency, prevent medical errors from the adaptation, and minimize the burden on clinicians. An interdisciplinary OART working group comprising radiation oncologists, medical physicists, dosimetrists, and therapists was created to enable weekly knowledge sharing, workflow design, implementation, and continuous process improvement. 213 adaptive sessions from 5 treatment sites (pancreas, bladder, prostate, rectum, anus) were treated on a CBCT-based OART platform in a single institutional study. An evaluation of the treatment safety and workflow time was performed for each adaptive session. The OART workflow was divided into four sub-workflows: 1) pre-treatment site-specific template preparation, 2) pre-treatment initial planning and verification, 3) on-treatment procedure, and 4) post-treatment evaluation. The sub-processes involved 4, 8, 13, and 4 separate, sequentially tasks, respectively, and a total of 11 task checklists. The template preparation is a new process developed for site-specific, standardized physician template directives, automated planning template development, and testing for its accuracy and robustness. The planning templates generated high-quality initial plans automatically within minutes once structures were segmented on the planning CT. This process was replicated during treatment using the CBCT. The median (interquartile range) online procedure time, defined as the time from initial CBCT to plan approval, of the five treatment sites (pancreas, bladder, prostate, rectum, anus) was 22.1 (19.2-24.8) min, 16.5 (15.3-17.5) min, 14.7 (13.9-17.4) min, 17 (15.3-19.7) min, and 24 (21.4-25.8) min, respectively. Safety assessment determined that no treatment deviations were observed. Creating an interdisciplinary, standardized workflow and checklists allowed the safe delivery of OART with clinically feasible online procedure time and significantly reduced initial planning time compared with traditional EBRT. The unique workflow is essential to minimize the burden on the care team, increase patient safety, and access to OART.

Comparison of Four Software Packages for Automated Contouring

We are an academic radiation oncology center and treat 1500-2000 patients annually. The majority of target volume definition and organ at risk (OAR) contouring is performed by radiation oncologists (RO) and is estimated to require 10-15 hours per week for each RO. Artificial intelligence (AI) solutions for contouring are a possible method of reducing the time required. Several packages are commercially available. The purpose of this exercise was to evaluate 4 packages to identify one which would meet our department's need. The primary objective was to calculate the potential time savings associated with each of the 4 packages. The second objective was to perform a qualitative evaluation and comparison. Approval from the hospital's Research Ethics Committee was obtained. CT Simulation datasets for 15 patients were anonymized for use as test cases for AI contouring. This included 5 datasets from each of 3 sites - head and neck (H+N), breast/thorax, and prostate/pelvis. 4 potential vendors provided access to their product for automated contouring of OARs +/- target volumes of each of the 15 datasets. Analysis was as follows: 1. One RO used a stopwatch to assess the time taken for them to contour each of the OARs +/- targets on each dataset 2. The AI contours from each of the vendors for each of the datasets was reviewed. 3. The time taken to correct each auto-contoured organ to make it clinically useful was timed. These were summed to give a time for each vendor for each dataset. 4. The time saving was calculated by subtracting the result of step (3) from step (1) 5. For the purpose of qualitative assessment, 2 ROs reviewed each dataset and assigned an objective score of 1-5 to each auto-contoured organ (e.g., 5 = no, or almost no, edits required; clinically useful as is) 6. The scores for each organ in each of the 15 datasets were averaged to give a mean value for the software package. Baseline contouring time was 32.8 minutes for H+N, 23.0 minutes for breast/thorax, and 64.8 minutes for prostate/pelvis. One package was removed from consideration after assessment of H+N only as it was deemed to be of insufficient standard to be of clinical use. The average time saving for each of the remaining 3 software packages ranged were 15.7, 17.9, and 12.8 minutes for H+N, 3.1, 13.1, and 11.0 minutes for breast/thorax, and 32.6, 35.2, and 36.2 minutes for prostate/pelvis. Percentage time savings ranged from 39-55% for H+N, 13-57% for breast/thorax, and 50-56% for prostate/pelvis. The overall qualitative assessments on a 5-point scale were 3.5, 4.0, and 3.6. The AI contouring solutions were associated with meaningful savings in the time taken to contour. The introduction of one of these products into clinical practice within our department will permit ROs to focus more of their time on other clinical activities.

Failure Mode and Effects Analysis Prior to the Introduction of AI Generated GTVs for Brain Metastases in the Clinical Workflow

AI autosegmentation of organs-at-risk (OARs) is common practice at many radiotherapy clinics. Despite the abundance of gross tumor volume (GTV) autosegmentation algorithms, adoption in clinical care has been slow due to the high risk associated with errors in GTV delineation. Here we present a failure mode and effects analysis (FMEA) to evaluate the risk associated with introducing AI derived GTVs in patients treated with stereotactic radiosurgery (SRS). An AI GTV autosegmentation algorithm for brain metastases was developed in-house based on a V-Net 3D CNN. Registered CT and MR images and a contour of the brain are input into the software and all identified lesions are returned in a DICOM-RT structure set. Following algorithm evaluation, a workflow was developed to enable AI GTV autosegmentation to be introduced clinically for every SRS patient. The following steps were added to existing procedures: 1) workflow to send CT/MR and brain structure to external server, 2) autosegmentation run on the server, 3) AI GTV structures with a standard nomenclature added to existing OAR structure set, and 4) MD review, editing, and approval of AI GTVs. After successfully completing the physics evaluation testing of the new process, we formed a team of 10 faculty and staff including physicists, residents, physicians, and planners to perform the FMEA prior to clinical implementation. The team met to map the process, identify potential failure modes, and score their frequency of occurrence, severity, and detectability. A 3-point scale (1, 3, or 5) was used to simplify the scoring process. Occurrence was defined as rare, sometimes, or often; severity as low, medium, or high; and detectability as obvious, possible, or challenging. The risk probability numbers (RPNs) were calculated and the steps in the process with the highest RPNs were flagged for further discussion. The FMEA team completed their process map and analysis primarily in 4 meetings. The process map began with acquisition of the patients CT simulation scan and ended with physician approval of final volumes for treatment planning. We identified 17 process steps and 72 possible failure modes, of which 26 were associated with the new workflow. Eighteen failure modes had an RPN greater than 30 (highest risk score in at least one category) and were flagged to assess mitigation strategies. Five were unique to the new AI GTV workflow and mitigation strategies will be designed prior to clinical use. Those involved risks related to inaccurate AI GTV contours, false positives, and an incomplete review stemming from over-reliance by team members on AI. AI is increasingly being employed at every step of radiotherapy to automate and streamline processes. The FMEA analysis resulted in the identification of the riskiest parts of using AI GTV autosegmentation. This can be an effective tool in the development of checks to ensure that GTV autosegmentation methods can be safely introduced in support of patient care.

Stereotactic Body Radiotherapy (SBRT) for Adrenal Metastases: Practice Patterns at Community-Based Radiotherapy Centers

To analyze SBRT of adrenal metastases at free-standing centers within a large network in the USA, in order to guide institutional recommendations. We interrogated records of patients treated with SBRT to adrenal metastases from January 2019 to June 2022 from a database of 13,363 patients who were treated with SBRT at 21 community-based centers clustered into five regions within a network. We reported patient characteristics, frequency, survival rate, and biologically effective dose (BED10) prescribed, respiratory motion capture according to region and treatment year. Survival was calculated by Kaplan-Meier estimates. A total of 271 patients were treated to metastatic adrenal lesions (2.0% of all SBRT patients). Median age was 69.8 years (range 32-96). Frequency of treatment increased by an average of 4.5% per year over the period. Table 1 gives the frequency of primary tumor sites, histological types, and dose fractionations. It also shows the range of BED10 used. 91% received five fractions, and 6% received 3 fractions. Median BED10 was 72 Gy (mean 68.0 Gy SD = 18.7). The most common regimen was 8 Gy x 5 (n = 75, 28%), but ranged from 5 Gy x 5 (n = 20) to 10 Gy x 5 (n = 34). BED10 varied according to geographic region ranging from 61.2 Gy to 73.0 Gy (p = 0.01). Median BED was 72 or above in 2 of 5 regions and less than 72 in 3 regions. Respiratory motion capture simulation was done in 65.1% of cases and frequency varied across the 5 locations as shown in Table1 from 93.3% in region 1 to 41.7% in region 5 (p<.0001). 4D CT simulation was used in 46.2% of cases and use was relatively stable over time: 52.8% in July to December 2020 and 40.9% from January to June 2022 with no trend over time: p = .7635 At median follow-up 6.6 months (range 0.3-48.5), 203 (74.9%) of patients were alive with a median overall survival of 24.3 months. 1-year and 2-year survival was 72.7% and 51.5% respectively. Use of respiratory motion simulation was not significantly associated with survival p = 0.47. Despite variation in BED10 and respiratory motion management, 2-year overall survival was over 50% in patients treated with SBRT for adrenal metastases in a large community network. Further study will address refinement of recommendations regarding dose and other treatment characteristics and expansion of clinical research in this setting.

Prompt Pain Relief from Bone Metastases: Mature Results from the Virtual Simulation Program

Rapidpain relief for patients with bone metastases can be a challenge due to the lengthy and complex radiotherapy workflow. The purpose of this study was to evaluate the time (in days) between initial radiation oncology consultation and start of palliative radiation treatment after implementing an alternative virtual simulation palliative workflow. Patients meeting strict criteria were selected for virtual simulation, which included only those with painful bone metastases who were recommended palliative radiotherapy using standard AP/PA or opposed lateral fields. A recent (within 30 days) diagnostic CT scan clearly visualizing the target volume was required for treatment planning. For comparison, a reference group of 40 consecutive patients with bone metastases that underwent in-person CT simulation prior to virtual simulation implementation was reviewed. Forty-five patients were treated for painful bone metastases as part of the virtual simulation program from May 2021 to October 2022. Regarding travel distance, 23 patients lived locally (<50 miles from treatment center) and 22 patients were distant (≥50 miles from treatment center). Average time from consult to treatment for all virtual simulation patients was 3.7 days compared to 7.5 days for in-person CT simulation patients (3.8 days sooner on average, p = <0.001). For outpatient treatments, average time from consult to treatment for distant virtual simulation patients was 4.0 days compared to 8.9 days for distant in-person CT simulation patients (4.9 days sooner on average, p = 0.003). The virtual simulation program decreased the time from consult to start of treatment for patients recommended palliative radiotherapy for painful bone metastases by over 50%. This benefit was most significant for outpatients traveling ≥ 50 miles for treatment. Virtual simulation-based planning can be considered for patients anxious to proceed with radiotherapy quickly, or in underserved settings with limited transportation options to regional treatment centers.

ARTIA-Bladder: Daily Online Adaptive Short-Course Radiation Therapy (RT) and Concurrent Chemotherapy for Muscle-Invasive Bladder Cancer (MIBC): A Prospective Trial of an Individualized Approach for Reducing Bowel and Bladder Toxicity

Concurrent chemo-radiotherapy is commonly prescribed for muscle-invasive bladder cancer (MIBC). Post hoc analysis of two large, randomized trials found that hypofractionation improves loco-regional control (LRC) vs. standard fractionation in this population. A challenge in traditional image-guided radiotherapy of the bladder is that daily changes in bladder position and size requires large margins to ensure target coverage. This makes it difficult to spare uninvolved bladder from high-dose treatment, increases the risk of bowel toxicity, and results in historical rates of acute G3+ toxicity exceeding 20-30%. Daily online adaptive RT (ART) may enable reduced, personalized margins that maintain target coverage while reducing dose to OARs. This prospective clinical trial will test whether: 1) participants undergoing ART for MIBC have a lower rate of acute G3+ GI/GU toxicity compared with the 31% historical control rate (Stage III BC2001 trial), and 2) 2-year LRC with ART will be non-inferior to historical controls (75%). This multi-national trial will enroll 165 adult subjects with stage cT2-T4aN0M0 urothelial MIBC. Subjects will have undergone an attempt at maximal transurethral resection of bladder tumor. Patients with clinically involved nodes or G2+ GI or G3+ GU symptoms/conditions at baseline are ineligible. Concurrent with chemotherapy, participants will receive (at the discretion of the investigator) either 55 Gy in 20 fx to whole-bladder or 46 Gy in 20 fx to whole-bladder plus simultaneous in-field boost of 55 Gy in 20 fx to tumor bed. A personalized ITV will be derived for each subject based on bladder expansion, as assessed on two CT simulations separated by 30 min. Daily ART will be attempted for all subjects. The primary endpoint is acute G3+ GI/GU toxicity. Secondary endpoints are LRC; quality of life (EORTC QLQ-BLM30, EPIC 26 bowel and urinary); global function (EQ-5D-5L ); 2-year disease-free, bladder intact event-free, and overall survival; 2-year bladder cancer-specific mortality; NTCP model of acute GI toxicity for hypofractionated bladder RT; workflow feasibility of ART; improved target coverage ± reduced dose to critical OARs vs. non-ART dosimetry; acute G3+ GI/GU toxicity rate in subjects with ≥75% of their treatments as ART; and acute G3+ GI/GU toxicity in the cohort treated with partial bladder boost. Exploratory translational and correlative endpoints will also be examined. This trial opened to enrollment on Feb 2, 2023; the study duration is expected to be 4-5 years. This prospective clinical trial will provide robust clinical data to inform healthcare providers' decisions on the use of daily online ART and hypofractionation as a bladder preservation strategy for this population.

Dosimetric Analysis of Proton Beam Therapy vs. Photon Radiotherapy for Cardiac Tumors with or without Deep Inspiratory Breath Holding: A Case Report

Proton beam therapy (PBT) has been demonstrated to deliver equivalent dosimetric radiation with the benefit of improved sparing of organs at risk (OAR). Deep inspiration breath holding (DIBH) is a commonly used method for reducing the radiation dose to the heart and lungs. However, few studies have ever reported the usage of DIBH combined with proton beam therapy in cardiac tumors. The purpose of this case report is to compare the dosimetric differences between photon radiotherapy and proton radiation therapy (PBT) with or without deep inspiration breath holding. A 66-year-old female patient with cardiac tumors was recruited, and the prescribed dose of radiotherapy for cardiac tumors was 95%PGTV 50Gy/2.5Gy/20f. Two simulation CT scans were collected during free breath (FB) and DIBH. And the target area was delineated on deep inspiratory breath holding image (DIBH-CT) and free breathing image (FB-CT). The target area of FB-CT was modified by referring to the ten-time phases of 4D-CT. Finally, IMRT, VMAT and PBT plans (DIBH-IMRT, DIBH-VAMT, DIBH-PBT, FB-IMRT, FB-VAMT, FB-PBT) were generated on the above images, and the organs at risk were limited as follows: lungs V20 ≤20%, lungs mean ≤11 Gy, heart V30 ≤40%, coronary artery mean ≤26 Gy, spinal cord ≤30 Gy, and left breast mean ≤5 Gy. All of the six plans satisfied most of the treatment planning goals. DIBH resulted in a dose reduction in all organs at risk including the heart, lungs, coronary artery (CA), spinal cord and breasts, when compared with FB using IMRT, VMAT, or PBT. Compared with the FB, DIBH provided a significant reduction in the mean dose of coronary artery (CA mean for DIBH-IMRT vs FB-IMRT = 28.32 Gy vs 42.66 Gy, CA mean for DIBH-VMAT vs FB-VAMT = 26.44Gy vs 40.85Gy, CA mean for DIBH-PBT vs FB-PBT = 27.71Gy vs 39.51Gy). Similarly, when compared with IMRT or VMAT in either FB or DIBH, PBT reduced radiation doses for all of the OAR. In comparison, the difference was less significant between IMRT and VMAT technique. Pitmen compared with IMRT and VMAT, reduced significantly the max dose of spinal cord, lungs V5, breast-L/R mean. Totally, DIBH-PBT was observed sufficient dose coverage and better sparing of organs at risk. PBT combined with DIBH technique gained an advantage in the sparing of OAR for cardiac tumors, especially in coronary protection. The possibility of broader application of PBT with DIBH in clinical practice is currently being evaluated and further studies are needed.

Effect of Dose to the Heart and Cardiac Substructures on Cardiac Toxicity after Breast Cancer Radiation

Pre-existing cardiovascular disease, chemotherapy, and higher mean heart dose are known risk factors for cardiac toxicity after breast radiation therapy. However, the relationship between cardiac substructure radiation exposure and toxicity is not well understood. We hypothesized that mean heart dose is a surrogate for global cardiac radiation exposure but that more specific dosimetric thresholds for the heart and its substructures could be identified, which could be used to guide radiation planning for breast cancer patients in the future. In this cohort study, all breast cancer patients who received curative intent breast or chest wall radiotherapy at a single high-volume institution in 2014 and 2017 were included (n = 841). Baseline characteristics included hormone therapy, chemotherapy, menopausal status, diabetes, dyslipidemia, pre-existing cardiac toxicity, and age at diagnosis. Outcomes included any cardiac toxicity, arrhythmia, cardiomyopathy, ischemia, valvular, pericardial disease, and death. The heart and substructures, including left ventricle, right ventricle, left atrium, right atrium, aortic valve, pulmonic valve, mitral valve, tricuspid valve, and left anterior descending artery, were delineated on the simulation CT for each patient. Dosimetric variables, including mean dose, max dose, and V1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, and 70 Gy for the heart and substructures (in cc) were extracted. For each dosimetric variable, multivariable logistic regression was performed using baseline covariates in addition to the single dosimetric variable. Patients with missing data values were excluded. Results were combined and False Discovery Rate p-value correction was performed. Multiple cardiac substructure dosimetric variables were associated with increased risk of mortality on multivariable analysis (p < 0.05). For left atrium and right atrium, V2, 3, 4, and 5 Gy were all significant. For right ventricle, mean dose, V1, 2, 3, 4, 5, 10, 15, 20, 25, 30, and 35 Gy were significant. For mitral valve, mean dose, max dose, V3, 4, and 5 Gy were significant. For tricuspid valve, mean dose was significant. For aortic valve, max dose, mean dose, V4, and V5 Gy were significant. For the whole heart, V1, 2, 3, 4, 5, 10, 15, 20, 25, 30, and 35 Gy were significant. We have identified multiple dosimetric variables for the heart and its substructures which were associated with increased risk of mortality after breast cancer radiation. In fact, for certain structures, there were multiple exposure thresholds which showed increased risk of toxicity, highlighting the complex relationship between substructure dose and outcomes. Further study into these relationships will identify the most critical cardiac substructure constraints that could be used in radiation treatment planning.

Study on the Clinical Use of a Respiratory Navigator Combined with Breath-Hold for MRI- Guided Liver SBRT

Respiratory movement strongly affects the accuracy of stereotactic body radiation therapy (SBRT) of liver malignancies treated without the use of a respiratory gating system. This study investigates the feasibility and advantages of using a respiratory navigator-guided combined with patient breath-hold for liver SBRT in an adaptive magnetic-resonance guided workflow. Clinical datasets of 10 liver cancer patients treated with 1.5T MR-Linac with respiratory navigator-guided SBRT combined with patient breath-hold were retrospectively analyzed. All patients underwent simulation CT with and without contrast, and 4D-CT and 3D-T2w MRI without contrast. Patients received a prescription dose ranging from 36 to 50 Gy in 5 to 8 fractions and followed the adapt to shape (ATS) workflow including contours adjustment and a subsequent MR-based synthetic CT (sCT) calculation on the online MRI acquired. The reference treatment plan was optimized on the expiratory phase of the 4D-CT, and during the online session the contours and the adapted plans were performed using the 3D-T2w navigator MRI of the patient's end-expiratory signal; 2D-T2w real-time monitoring MRI was also used as support for the contour's definition. The radiation therapist instructed the patients to hold their breath at the end of the breathing cycle for the time of the beam on. A total of 59 fractions were analyzed. For each fraction the dosimetric parameters of the target and normal liver of the adaptive and reference plans were compared; particularly the volume, the conformity index (CI) and gradient index (GI) for the target, and V5, V10 and Dmean for the normal liver. T-student statistical analysis was performed; a p-value less than 0.05 was considered statistically significant. In the free breathing state, the 3D-T2w navigator MRI images enable a clear visualization of the tumor and its boundaries. The average target CI of the adaptive and reference plans is not significantly different (p = 0.448), while the GI is significantly higher (p = 0.043). Normal liver V10 and Dmean are lower and V5 is slightly increased, but without statistical differences. The mean values and standard deviation of the dosimetric parameters of the reference and adapted plans are shown in the Table below. The use of a respiratory navigator combined with the breath-hold for MRI- guided liver SBRT allows clear visualization of the tumor, ensures the accuracy of the delivered dose and may be considered an alternative when the respiratory gating system is not available during MRgART sessions.

TT-TG distance decreases after open wedge distal femoral varization osteotomy in patients with genu valgum & patellar instability. A pilot 3D computed tomography simulation study

BackgroundGenu valgum is considered to be a risk factor for patellar instability. Correction of valgus alone or combined with a tibial tuberosity medialization has been described. However, changes occurring in the tibial tuberosity-trochlear groove (TT-TG) distance after a lateral distal femoral opening wedge osteotomy (LDF-OWO) are not known.PurposeThis study aimed to define changes in TT-TG distance with increasing amounts of valgus correction after LDF-OWO.MethodsThree-Dimensional Computed Tomography (3D-CT) scans of six patients (two females and four males) aged between 19 and 35 years with genu valgum and patellar instability were analyzed. 3D models of femoral, tibial, and patellar bones were created with the advanced segment option of Mimics 21® software. An oblique lateral opening wedge osteotomy of the distal femur was simulated in 2-degree increments up to 12 degrees of varus opening. Change in TT-TG distance was measured in mm on 3D models of the knee.ResultsCompared to baseline without osteotomy, the TT-TG distance decreased significantly (p < 0.05) for all corrections from 2 to 12 degrees in 2° steps. The TT-TG distance decreased by an average of 1.7 mm for every 2 degrees of varus opening.ConclusionLateral opening wedge distal femoral osteotomy causes a decrease in TT-TG distance. The surgeon should be aware of the magnitude of this change (1.7 mm decrease for every 2° increment of valgus opening).

Computed tomography simulation projection acquisition method of artistic relics based on voxel model

Computed tomography simulation projection acquisition method of artistic relics based on voxel model

MRI-guided Real-time Online Gated Stereotactic Body Radiation Therapy for Liver Tumors.

Liver tumors are commonly encountered in oncology. The study aimed to assess the impact of magnetic resonance imaging (MRI)-guided stereotactic body radiation therapy (SBRT) (MRgSBRT) on disease-related outcomes and the toxicity profile. Patients who received MRgSBRT from 2019 to 2021 for primary and metastatic liver tumors were included in this analysis. The protocol for treatment simulation included Gadoxetate disodium injection followed by a single-dimensional post-exhale MRI (0.35-T MRI linear accelerator) and computed tomography simulation. The patient demographics and treatment-related outcomes were assessed. The time-to-event curves were analyzed for freedom from local progression (FFLP) and overall survival (OS). A total of 35 patients were eligible for analysis with a median age of 70 years (range 25 to 95). The median follow-up was 19.4 months (range 1 to 37mo). The one-year OS was 77.7%, with an estimated 3 years of 47.9%. Patients with the locally controlled disease had a better median OS of 27.8 months (95% CI [23.8-31.6]) compared with 13.5 months (95% CI [5.6-21.3], P =0.007) in patients with local disease progression. The 1-year FFLP was 95.6%, and 3-year estimated FFLP was 87.1%. Patients who received a radiation dose of biologically equivalent dose≥100 Gy had FFLP of 30.9 months (95% CI [28.7-33.1]) compared with 13.3 months (95% CI [5.3-21.3], P =0.004) in patients who received <100 Gy biologically equivalent dose. MRI-guided SBRT provides optimal local control, associated with improved OS in a heavily morbid, pretreated older cohort of patients with reasonable safety profiles.

Predicting successful clinical candidates for fiducial-free lung tumor tracking with a deep learning binary classification model.

The CyberKnife system is a robotic radiosurgery platform that allows the delivery of lung SBRT treatments using fiducial-free soft-tissue tracking. However, not all lung cancer patients are eligible for lung tumor tracking. Tumor size, density, and location impact the ability to successfully detect and track a lung lesion in 2D orthogonal X-ray images. The standard workflow to identify successful candidates for lung tumor tracking is called Lung Optimized Treatment (LOT) simulation, and involves multiple steps from CT acquisition to the execution of the simulation plan on CyberKnife. The aim of the study is to develop a deep learning classification model to predict which patients can be successfully treated with lung tumor tracking, thus circumventing the LOT simulationprocess. Target tracking is achieved by matching orthogonal X-ray images with a library of digital radiographs reconstructed from the simulation CT scan (DRRs). We developed a deep learning model to create a binary classification of lung lesions as being trackable or untrackable based on tumor template DRR extracted from the CyberKnife system, and tested five different network architectures. The study included a total of 271 images (230 trackable, 41 untrackable) from 129 patients with one or multiple lung lesions. Eighty percent of the images were used for training, 10% for validation, and the remaining 10% for testing. For all five convolutional neural networks, the binary classification accuracy reached 100% after training, both in the validation and the test set, without any falseclassifications. A deep learning model can distinguish features of trackable and untrackable lesions in DRR images, and can predict successful candidates for fiducial-free lung tumortracking.

In silico trial of simulation-free hippocampal-avoidance whole brain adaptive radiotherapy

Background and PurposeHippocampal-avoidance whole brain radiotherapy (HA-WBRT) can be a time-consuming process compared to conventional whole brain techniques, thus potentially limiting widespread utilization. Therefore, we evaluated the in silico clinical feasibility, via dose-volume metrics and timing, by leveraging a computed tomography (CT)-based commercial adaptive radiotherapy (ART) platform and workflow in order to create and deliver patient-specific, simulation-free HA-WBRT. Materials and methodsTen patients previously treated for central nervous system cancers with cone-beam computed tomography (CBCT) imaging were included in this study. The CBCT was the adaptive image-of-the-day to simulate first fraction on-board imaging. Initial contours defined on the MRI were rigidly matched to the CBCT. Online ART was used to create treatment plans at first fraction. Dose-volume metrics of these simulation-free plans were compared to standard-workflow HA-WBRT plans on each patient CT simulation dataset. Timing data for the adaptive planning sessions were recorded. ResultsFor all ten patients, simulation-free HA-WBRT plans were successfully created utilizing the online ART workflow and met all constraints. The median hippocampi D100% was 7.8 Gy (6.6–8.8 Gy) in the adaptive plan vs 8.1 Gy (7.7–8.4 Gy) in the standard workflow plan. All plans required adaptation at first fraction due to both a failing hippocampal constraint (6/10 adaptive fractions) and sub-optimal target coverage (6/10 adaptive fractions). Median time for the adaptive session was 45.2 min (34.0–53.8 min). ConclusionsSimulation-free HA-WBRT, with commercially available systems, was clinically feasible via plan-quality metrics and timing, in silico.

P17.17.A WHAT IS THE BETTER THERAPEUTIC STRATEGY FOR DISTANT RECURRENT GLIOMA? OUTCOME AND PROGNOSTIC FACTORS EVALUATION IN A RETROSPECTIVE SERIES

Abstract BACKGROUND Distant recurrent glioma is a rare condition. A possible correlation has been postulate with original tumor location in close proximity to the subventricular zone. Nevertheless, to date no data exist about treatment strategy, neither retrospective nor prospective studies, and a standard of care is missing. Several treatment options are available: surgery, systemic treatment (CHT), and radiation therapy (RT) alone or with concomitant chemotherapy. Aim of this retrospective analysis was to investigate outcome of patients (pts) with distant recurrent glioma treated with RT, with or without concomitant and/or adjuvant temozolomide chemotherapy. MATERIAL AND METHODS Adult pts with distant recurrent glioma have been evaluated. Histological diagnosis has been reclassified according to the 2021 WHO Classification of Central Nervous System tumors, and original tumor proximity to subventricular zone has been registered. Distant progression treatment strategies included RT alone, RT concurrent with CHT (RT-CHT), RT followed by CHT. For RT planning, target delineation was defined on simulation CT scan and brain MRI co-registered into the image workstation of Brainlab. RESULTS From 2015 to 2023, 35 distant recurrent glioma pts have been evaluated. Histological diagnosis was: glioblastoma (GBM) in 27 pts, astrocytoma grade 3 IDH-mutant in 6 pts, and astrocytoma grade 2 IDH-mutant in 2 pts. Location of primary tumor was in subventricular zone in all cases: 16 pts developed only distant recurrence, 19 pts both local and distant recurrence. Median interval time (IT) between diagnosis and distant recurrence was 22 months (range 5-49); 8 cases had distant recurrence within 12 months, 17 cases between 13 and 24 months, and 10 cases ≥ 24 months. Regarding treatment strategy, 10 pts (29%) received RT alone, 6 pts (17%) received RT-CHT, 11 (31%) received RT followed by sequential CHT, 8 (23%) received concurrent RT-CHT followed by sequential CHT. Median RT dose was 45 Gy in 15 fractions (range 30-50 Gy in 5-20fr). Median follow up time was 36months (range3-97). Median overall survival (OS) time, 6 months,1,2-year OS rates from diagnosis were 33 months (95% CI 30-36), 100%, 94.3%±3.92, 70.0%±7.9, respectively. Median OS time, 6 months,1,2-year OS rates from distant recurrence were 10 months (95% CI 7-12), 75.4%±7.6, 26.2%±7.9, 0.%. On univariate, and multivariate analysis, prognostic factors impacting OS were IT between diagnosis and first recurrence, and treatment performed. Indeed, median OS time were 6 months (95% CI 2-10) for IT ≤ 1year (P= 0.0363), 17 months (95% CI 8-12), 10 months (95% CI 7-12), 6 months (95% CI 8-12) for RT alone, RT and concurrent RT-CHT followed by sequential CHT, concurrent RT-CHT without sequential CHT, respectively (P &amp;lt; 0.0001). CONCLUSION Our results suggest that RT alone could be a feasible and effective treatment option for distant recurrent glioma.

Dosimetric characterization of foam padding with posterior fields in palliative radiation therapy

Patients undergoing external beam radiation therapy for the palliative treatment of painful bony metastases may have difficulty maintaining a still position on a rigid uncovered couch top, both during CT simulation as well as during patient setup, image guidance, and treatment on the linear accelerator. For these patients, a thin foam pad or mattress is sometimes used to mitigate patient discomfort. It was desired to quantify the effect of the padding in cases in which the patient is to be treated supine with posterior beams when the majority of the beam weighting traverses both the couch and the pad. Ion chamber measurements in-phantom were acquired with 6 MV, 10 MV, and 15 MV photon beams. At depths of maximum dose, the pad resulted in a difference of signal collected ≤1%. At the phantom surface, the pad resulted in an increase in signal ranging from 1% to 6.5% for the measured beams. CT data of the pad, both with and without applied pressure, indicated that the pad had average HU values close to air.

Development, validation, and simplification of a scanner-specific CT simulator.

Simulated computed tomography (CT) images allow for knowledge of the underlying ground truth and for easy variation of imaging conditions, making them ideal for testing and optimization of new applications or algorithms. However, simulating all processes that affect CT images can result in simulations that are demanding in terms of processing time and computer memory. Therefore, it is of interest to determine how much the simulation can be simplified while still achieving realistic results. To develop a scanner-specific CT simulation using physics-based simulations for the position-dependent effects and shift-invariant image corruption methods for the detector effects. And to investigate the impact on image realism of introducing simplifications in the simulation process that lead to faster and less memory-demanding simulations. To make the simulator realistic and scanner-specific, the spatial resolution and noise characteristics, and the exposure-to-detector output relationship of a clinical CT system were determined. The simulator includes a finite focal spot size, raytracing of the digital phantom, gantry rotation during projection acquisition, and finite detector element size. Previously published spectral models were used to model the spectrum for the given tube voltage. The integrated energy at each element of the detector was calculated using the Beer-Lambert law. The resulting angular projections were subsequently corrupted by the detector modulation transfer function (MTF), and by addition of noise according to the noise power spectrum (NPS) and signal mean-variance relationship, which were measured for different scanner settings. The simulated sinograms were reconstructed on the clinical CT system and compared to real CT images in terms of CT numbers, noise magnitude using the standard deviation, noise frequency content using the NPS, and spatial resolution using the MTF throughout the field of view (FOV). The CT numbers were validated using a multi-energy CT phantom, the noise magnitude and frequency were validated with a water phantom, and the spatial resolution was validated with a tungsten wire. These metrics were compared at multiple scanner settings, and locations in the FOV. Once validated, the simulation was simplified by reducing the level of subsampling of the focal spot area, rotation and of detector pixel size, and the changes in MTFs were analyzed. The average relative errors for spatial resolution within and across image slices, noise magnitude, and noise frequency content within and across slices were 3.4%, 3.3%, 4.9%, 3.9%, and 6.2%, respectively. The average absolute difference in CT numbers was 10.2HU and the maximum was 22.5HU. The simulation simplification showed that all subsampling can be avoided, except for angular, while the error in frequency at 10% MTF would be maximum 16.3%. The simulation of a scanner-specific CT allows for the generation of realistic CT images by combining physics-based simulations for the position-dependent effects and image-corruption methods for the shift-invariant ones. Together with the available ground truth of the digital phantom, it results in a useful tool to perform quantitative analysis of reconstruction or post-processing algorithms. Some simulation simplifications allow for reduced time and computer power requirements with minimal loss of realism.

Low-Dose CT Image Synthesis for Domain Adaptation Imaging Using a Generative Adversarial Network With Noise Encoding Transfer Learning.

Deep learning (DL) based image processing methods have been successfully applied to low-dose x-ray images based on the assumption that the feature distribution of the training data is consistent with that of the test data. However, low-dose computed tomography (LDCT) images from different commercial scanners may contain different amounts and types of image noise, violating this assumption. Moreover, in the application of DL based image processing methods to LDCT, the feature distributions of LDCT images from simulation and clinical CT examination can be quite different. Therefore, the network models trained with simulated image data or LDCT images from one specific scanner may not work well for another CT scanner and image processing task. To solve such domain adaptation problem, in this study, a novel generative adversarial network (GAN) with noise encoding transfer learning (NETL), or GAN-NETL, is proposed to generate a paired dataset with a different noise style. Specifically, we proposed a method to perform noise encoding operator and incorporate it into the generator to extract a noise style. Meanwhile, with a transfer learning (TL) approach, the image noise encoding operator transformed the noise type of the source domain to that of the target domain for realistic noise generation. One public and two private datasets are used to evaluate the proposed method. Experiment results demonstrated the feasibility and effectiveness of our proposed GAN-NETL model in LDCT image synthesis. In addition, we conduct additional image denoising study using the synthesized clinical LDCT data, which verified the merit of the proposed synthesis in improving the performance of the DL based LDCT processing method.