Computed Tomography Simulation Scans Research Articles

Feasibility of simulation free abdominal stereotactic adaptive radiotherapy using an expedited pre-plan workflow

Background and PurposeImproved hounsfield-unit accuracy of on-board imaging may lead to direct-to-unit treatment approaches We aimed to demonstrate the feasibility of using only a diagnostic (dx) computed tomography (CT)-defined target pre-plan in an in silico study of simulation-free abdominal stereotactic adaptive radiotherapy (ART). Materials and MethodsEight patients with abdominal treatment sites (five pancreatic cancer, three oligometastases) were treated using an integrated adaptive O-Ring gantry system. Each patient’s target was delineated on a dxCT. The target only pre-plan served primarily to seed the ART process. During the ART session, all structures were delineated. All simulated cases were treated to 50 Gy in 5 fractions to a planning target optimization structure (PTV_OPT) to allow for dose escalation within the planning target volume. Timing of steps during this workflow was recorded. Plan quality was compared between ART treatment plans and a plan created on a CT simulation scan using the traditional planning workflow. ResultsThe workflow was feasible in all attempts, with organ-at-risk (OAR) constraints met in all fractions despite lack of initial OAR contours. Median absolute difference between the adapted plan and simulation CT plan for the PTV_Opt V95% was 2.0 %. Median absolute difference in the D0.5 cm3 between the adapted plan and simulation CT plan was −0.9 Gy for stomach, 1.2 Gy for duodenum, −5.3 Gy for small bowel, and 0.3 Gy for large bowel. Median end-to-end workflow time was 63 min. ConclusionThe workflow was feasible for a dxCT-defined target-only pre-plan approach to stereotactic abdominal ART.

DOSIMETRIC COMPARISON OF INTENSITY-MODULATED RADIATION THERAPY (IMRT) AND VOLUMETRIC-MODULATED ARC THERAPY (VMAT) IN RECTAL CANCER PATIENTS: A COMPREHENSIVE ANALYSIS

This study aimed to conduct a thorough dosimetric comparison between two advanced radiotherapy techniques, Intensity-Modulated Radiation Therapy (IMRT) and Volumetric-Modulated Arc Therapy (VMAT), to ascertain the superior modality in terms of dose distribution and organ-at-risk sparing for rectal cancer patients. Thirty-three patients with ECOG performance status 1, aged between 18 to 60 years, diagnosed with Stage II to III rectal cancer, were included in this prospective study. Each patient underwent treatment planning with IMRT and VMAT techniques using Varian system simulation computed tomography scans. The dosimetric analysis encompassed the evaluation of target volume dose homogeneity, conformity, and doses to at-risk organs. The dosimetric comparison was conducted utilizing seven-field IMRT and dual arc VMAT plans. Statistical analysis was performed using paired t-tests and SPSS 20 software. Treatment plan constraints adhered to the RTOG 9406 guidelines, ensuring adequate planning target volume (PTV) coverage, homogeneity index, conformity index, and dose limits for organs at risk, including small bowel V20 (<200cc), bladder V45 (<21Gy), and femoral head V50 (≤30 Gy). Volumetric arc therapy plans demonstrated superior dose homogeneity to fixed-field intensity-modulated plans (p-value = 0.04), particularly in femoral head dose-limiting toxicity (p-value = 0.00). However, intensity-modulated therapy exhibited equivalent or superior performance in other evaluated parameters. Our findings suggest that while VMAT offers advantages in PTV coverage homogeneity and femoral head dose-limiting toxicity, IMRT remains competitive and potentially preferable in certain dosimetric aspects. The choice between IMRT and VMAT should be carefully considered based on individual patient characteristics and treatment goals. This comprehensive dosimetric comparison contributes valuable insights into optimizing radiotherapy planning for rectal cancer patients, potentially guiding clinical decision-making and improving treatment outcomes.

Continuous Positive Airway Pressure–Assisted Breathing With Supine Tangential Left Breast Radiation Therapy When Deep Inspiration Breath-Hold Radiation Therapy Was Ineffective or Unsuitable: Clinical Implications for an Affordable Heart-Sparing Breast Radiation Therapy to Reduce the Health Care Disparities in Low-Resource Settings

Continuous Positive Airway Pressure–Assisted Breathing With Supine Tangential Left Breast Radiation Therapy When Deep Inspiration Breath-Hold Radiation Therapy Was Ineffective or Unsuitable: Clinical Implications for an Affordable Heart-Sparing Breast Radiation Therapy to Reduce the Health Care Disparities in Low-Resource Settings

Prompt Pain Relief From Bone Metastases: The Virtual Simulation Program

Rapid pain relief for patients with bone metastases can be a challenge due to the lengthy and complex radiation therapy 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 radiation therapy using standard anterior-posterior/posterior-anterior or opposed lateral fields. A recent (within 30 days) diagnostic computed tomography (CT) scan clearly visualizing the target volume was required for treatment planning. For comparison, a reference group of 40 consecutive patients with bone metastases who underwent in-person CT simulation before 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 the treatment center) and 22 patients were distant (≥50 miles from the treatment center). Average time from consultation to treatment for all patients undergoing virtual simulation was 3.7 days, compared with 7.5 days for patients undergoing in-person CT simulation (3.8 days sooner, on average; P ≤ .001). Before full implementation of the virtual simulation program, 5 eligible patients participated in a virtual simulation pilot from April 2021 to May 2021, in which each patient was contoured and planned on both a pre-existing diagnostic CT scan and a standard CT simulation scan. For virtual simulation-based plans, the average V90, V95, and V99 were 99.99%, 99.87%, and 96.70%. No significant planning target volume (PTV) coverage difference was found on subsequent in-person CT simulation scans. The virtual simulation program decreased the time from consultation to start of treatment by more than 50% for patients recommended palliative radiation therapy for painful bone metastases. 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 radiation therapy quickly or in underserved settings with limited transportation options to regional treatment centers.

Unsupervised deep learning for defect detection on CT parts using simulated data

Computed tomography (CT) is a prominent technology for nondestructive quality control and is already used in industry for defect detection. However, as quality control is shifting towards a full in-line inspection, automatic CT analysis is required to meet the tight production time. Nonetheless, in settings where a high amount of data is produced, a robust fully automatic defect detection is essential In the past years, deep learning (DL) has been extensively used to perform vision tasks in an automatic way, and given its promising results, has been successfully applied in CT settings. Most of the recent work is based on supervised DL often adapted from results in the medical field. Supervised DL, although extremely powerful, has the drawbacks of requiring a high amount of labeled data done by experts and is biased to the specific dataset used. Therefore, an unsupervised DL model is presented. A two stages network formed by an auto-encoder and an autoregressive model, originally implemented for image generation, is adapted for volume segmentation. The network is trained on the specific task of defect segmentation of cast aluminum parts. CAD models of such parts are gathered, and corresponding simulated CT scans are acquired. Results show that the architecture, although originally implemented for data generation, can be adapted for CT volume segmentation.

Dosimetric impact of bladder volume on organs at risk during external beam radiotherapy of cervical cancer.

This study aimed to investigate the effect of bladder volume on the dosimetry of pelvic organs at risk (OARs) in patients treated with external beam radiation therapy. Twenty patients with locally advanced cervical cancer were selected. Two computed tomography-simulation scans were obtained, one with an empty bladder followed by one with a full bladder. The acquired images were transferred to the treatment planning system. Target and OARs were contoured in both images, and treatment plans were performed for each computed tomography image. The delivered doses to target and OARs were determined using dose-volume histograms. The mean dose of the bowel bag in the empty and full bladder were 35.06±4.13 (Gy) and 31.59±3.86 (Gy), respectively. Furthermore, the V45 of the bowel bag in the empty bladder was 364.27±154.39 (cc) and in the full bladder, it was 240.84±129.66 (cc). The mean dose of the rectum in the empty and full bladder were 49.50±1.95 (Gy) and 49.18±1.03 (Gy), respectively. The rectal V50 (%) was 52.82±21.84 (%) in the empty bladder and 45.49±29.55 (%) in the full bladder. The mean dose and V45 of the bowel bag, also V50 of the rectum, had significantly decreased in the full bladder status (p-value<0.05). The results showed that the bladder volume significantly affected the delivered dose to the bowel bag and rectum. The average bowel bag V45 and rectum V50 in the full bladder were significantly decreased. Bladder distention is an effective method to improve the dosimetric parameters of pelvic OARs.

Endobronchially Implanted Real-Time Electromagnetic Transponder Beacon–Guided, Respiratory-Gated SABR for Moving Lung Tumors: A Prospective Phase 1/2 Cohort Study

Endobronchial electromagnetic transponder beacons (EMT) provide real-time, precise positional data of moving lung tumors. We report results of a phase 1/2, prospective, single-arm cohort study evaluating the treatment planning effects of EMT-guided SABR for moving lung tumors. Eligible patients were adults, Eastern Cooperative Oncology Group 0 to 2, with T1-T2N0 non-small cell lung cancer or pulmonary metastasis ≤4 cm with motion amplitude ≥5 mm. Three EMTs were endobronchially implanted using navigational bronchoscopy. Four-dimensional free-breathing computed tomography simulation scans were obtained, and end-exhalation phases were used to define the gating window internal target volume. A 3-mm expansion of gating window internal target volume defined the planning target volume (PTV). EMT-guided, respiratory-gated (RG) SABR was delivered (54 Gy/3 fractions or 48 Gy/4 fractions) using volumetric modulated arc therapy. For each RG-SABR plan, a 10-phase image-guided SABR plan was generated for dosimetric comparison. PTV/organ-at-risk (OAR) metrics were tabulated and analyzed using the Wilcoxon signed-rank pair test. Treatment outcomes were evaluated using RECIST (Response Evaluation Criteria in Solid Tumours; version 1.1). Of 41 patients screened, 17 were enrolled and 2 withdrew from the study. Median age was 73 years, with 7 women. Sixty percent had T1/T2 non-small cell lung cancer and 40% had M1 disease. Median tumor diameter was 1.9 cm with 73% of targets located peripherally. Mean respiratory tumor motion was 1.25 cm (range, 0.53-4.04 cm). Thirteen tumors were treated with EMT-guided SABR and 47% of patients received 48 Gy in 4 fractions while 53% received 54 Gy in 3 fractions. RG-SABR yielded an average PTV reduction of 46.9% (P < .005). Lung V5, V10, V20, and mean lung dose had mean relative reductions of 11.3%, 20.3%, 31.1%, and 20.3%, respectively (P < .005). Dose to OARs was significantly reduced (P < .05) except for spinal cord. At 6 months, mean radiographic tumor volume reduction was 53.5% (P < .005). EMT-guided RG-SABR significantly reduced PTVs of moving lung tumors compared with image-guided SABR. EMT-guided RG-SABR should be considered for tumors with large respiratory motion amplitudes or those located in close proximity to OARs.

Radiation Exposure of Cardiac Conduction Nodes During Breast Proton Therapy.

The exposition of cardiac conduction system during breast radiation therapy has never been studied, despite the increasing use of intensity-modulated radiation therapy, which exposes larger volume to low-dose bath. We evaluated conduction node exposure during breast irradiation with volumetric modulated arc therapy and estimated the potential dosimetric benefit with intensity-modulated proton therapy. Atrioventricular (AVN) and sinoatrial (SAN) nodes were retrospectively delineated according to published guidelines on the simulation computed tomography scans of 12 breast cancer patients having undergone conserving surgery and adjuvant locoregional volumetric modulated arc therapy. Intensity-modulated proton therapy treatment was replanned on the simulation computed tomography scans for all breast cancer patients. Mean and maximum doses delivered to the SAN and the AVN were retrieved and compared. Correlation coefficients were calculated between doses to the SAN or the AVN and the whole heart. Average mean doses delivered to the SAN and AVN were 2.8 and 2.3 Gy, respectively, for left-sided irradiation and 9.6 and 3.6 Gy, respectively, for right-sided irradiation. Average maximum doses to the SAN and AVN were 3.5 Gy and 2.8 Gy, respectively, for left-sided irradiation and 13.1 and 4.6 Gy, respectively, for right-sided irradiation. Intensity-modulated proton therapy significantly reduced mean and maximum doses to the SAN and AVN. Correlations between doses to the SAN or AVN and whole heart were usually significant. SAN and AVN can be substantially exposed during breast volumetric modulated arc therapy, especially for right-sided irradiation. Cardiotoxicity studies evaluating conduction node exposure might define dose constraints and criteria for additional cardiac-sparing techniques, such as respiratory techniques or proton therapy, which could benefit patients with underlying rhythmic or conduction disorders.

High-Risk Non-Small Cell Lung Cancer Treated With Active Scanning Proton Beam Radiation Therapy and Immunotherapy

PurposeNon-small cell lung cancer (NSCLC) is a deadly malignancy that is frequently diagnosed in patients with significant medical comorbidities. When delivering local and regional therapy, an exceedingly narrow therapeutic window is encountered, which often precludes patients from receiving aggressive curative therapy. Radiation therapy advances including particle therapy have been employed in an effort to expand this therapeutic window. Here we report outcomes with the use of proton therapy with curative intent and immunotherapy to treat patients diagnosed with high-risk NSCLC. Methods and MaterialsPatients were determined to be high risk if they had severe underlying cardiopulmonary dysfunction, history of prior thoracic radiation therapy, and/or large volume or unfavorable location of disease (eg, bilateral hilar involvement, supraclavicular involvement). As such, patients were determined to be ineligible for conventional x-ray–based radiation therapy and were treated with pencil beam scanning proton beam therapy (PBS-PBT). Patients who demonstrated excess respiratory motion (ie, greater than 1 cm in any dimension noted on the 4-dimensional computed tomography simulation scan) were deemed to be ineligible for PBT. Toxicity was reported using the Common Terminology Criteria for Adverse Events (CTCAE), version 5.0. Overall survival and progression-free survival were calculated using the Kaplan-Meier method. ResultsA total of 29 patients with high-risk NSCLC diagnoses were treated with PBS-PBT. The majority (55%) of patients were defined as high risk due to severe cardiopulmonary dysfunction. Most commonly, patients were treated definitively to a total dose of 6000 cGy (relative biological effectiveness) in 30 fractions with concurrent chemotherapy. Overall, there were a total of 6 acute grade 3 toxicities observed in our cohort. Acute high-grade toxicities included esophagitis (n = 4, 14%), dyspnea (n = 1, 3.5%), and cough (n = 1, 3.5%). No patients developed grade 4 or higher toxicity. The majority of patients went on to receive immunotherapy, and high-grade pneumonitis was rare. Two-year progression-free and overall survival was estimated to be 51% and 67%, respectively. COVID-19 was confirmed or suspected to be responsible for 2 patient deaths during the follow-up period. ConclusionsRadical PBS-PBT treatment delivered in a cohort of patients with high-risk lung cancer with immunotherapy is feasible with careful multidisciplinary evaluation and rigorous follow-up.

A comprehensive quality assurance procedure for 4D CT commissioning and periodic QA.

The 4D computed tomography (CT) simulation is an essential procedure for tumors exhibiting breathing-induced motion. However, to date there are no established guidelines to assess the characteristics of existing systems and to describe meaningful performance. We propose a commissioning quality assurance (QA) protocol consisting of measurements and acquisitions that assess the mechanical and computational operation for 4D CT with both phase and amplitude-based reconstructions, for regular and irregular respiratory patterns. The 4D CT scans of a QUASAR motion phantom were acquired for both regular and irregular breathing patterns. The hardware consisted of the Canon Aquilion Exceed LB CT scanner used in conjunction with the Anzai laser motion monitoring system. The nominal machine performance and reconstruction were demonstrated with measurements using regular breathing patterns. For irregular breathing patterns the performance was quantified through the analysis of the target motion in the superior and inferior directions, and the volume of the internal target volume (ITV). Acquisitions were performed using multiple pitches and the reconstructions were performed using both phase and amplitude-based binning. The target was accurately captured during regular breathing. For the irregular breathing, the measured ITV exceeded the nominal ITV parameters in all scenarios, but all deviations were less than the reconstructed slice thickness. The mismatch between the nominal pitch and the actual breathing rate did not affect markedly the size of the ITV. Phase and normalized amplitude binning performed similarly. We demonstrated a framework for measuring and quantifying the initial performance of 4D CT simulation scans that can also be applied during periodic QAs. The regular breathing provided confidence that the hardware and the software between the systems performs adequately. The irregular breathing data suggest that the system may be expected to capture in excess the target motion and geometry, but the deviation is expected to be within the slice thickness.

PIMA-CT: Physical Model-Aware Cyclic Simulation and Denoising for Ultra-Low-Dose CT Restoration.

A body of studies has proposed to obtain high-quality images from low-dose and noisy Computed Tomography (CT) scans for radiation reduction. However, these studies are designed for population-level data without considering the variation in CT devices and individuals, limiting the current approaches' performance, especially for ultra-low-dose CT imaging. Here, we proposed PIMA-CT, a physical anthropomorphic phantom model integrating an unsupervised learning framework, using a novel deep learning technique called Cyclic Simulation and Denoising (CSD), to address these limitations. We first acquired paired low-dose and standard-dose CT scans of the phantom and then developed two generative neural networks: noise simulator and denoiser. The simulator extracts real low-dose noise and tissue features from two separate image spaces (e.g., low-dose phantom model scans and standard-dose patient scans) into a unified feature space. Meanwhile, the denoiser provides feedback to the simulator on the quality of the generated noise. In this way, the simulator and denoiser cyclically interact to optimize network learning and ease the denoiser to simultaneously remove noise and restore tissue features. We thoroughly evaluate our method for removing both real low-dose noise and Gaussian simulated low-dose noise. The results show that CSD outperforms one of the state-of-the-art denoising algorithms without using any labeled data (actual patients' low-dose CT scans) nor simulated low-dose CT scans. This study may shed light on incorporating physical models in medical imaging, especially for ultra-low level dose CT scans restoration.

Simulation CT-based radiomics for prediction of response after neoadjuvant chemo-radiotherapy in patients with locally advanced rectal cancer

BackgroundTo report on the discriminative ability of a simulation Computed Tomography (CT)-based radiomics signature for predicting response to treatment in patients undergoing neoadjuvant chemo-radiation for locally advanced adenocarcinoma of the rectum.MethodsConsecutive patients treated at the Universities of Tübingen (from 1/1/07 to 31/12/10, explorative cohort) and Florence (from 1/1/11 to 31/12/17, external validation cohort) were considered in our dual-institution, retrospective analysis. Long-course neoadjuvant chemo-radiation was performed according to local policy. On simulation CT, the rectal Gross Tumor Volume was manually segmented. A feature selection process was performed yielding mineable data through an in-house developed software (written in Python 3.6). Model selection and hyper-parametrization of the model was performed using a fivefold cross validation approach. The main outcome measure of the study was the rate of pathologic good response, defined as the sum of Tumor regression grade (TRG) 3 and 4 according to Dworak’s classification.ResultsTwo-hundred and one patients were included in our analysis, of whom 126 (62.7%) and 75 (37.3%) cases represented the explorative and external validation cohorts, respectively. Patient characteristics were well balanced between the two groups. A similar rate of good response to neoadjuvant treatment was obtained in in both cohorts (46% and 54.7%, respectively; p = 0.247). A total of 1150 features were extracted from the planning scans. A 5-metafeature complex consisting of Principal component analysis (PCA)-clusters (whose main components are LHL Grey-Level-Size-Zone: Large Zone Emphasis, Elongation, HHH Intensity Histogram Mean, HLL Run-Length: Run Level Variance and HHH Co-occurence: Cluster Tendency) in combination with 5-nearest neighbour model was the most robust signature. When applied to the explorative cohort, the prediction of good response corresponded to an average Area under the curve (AUC) value of 0.65 ± 0.02. When the model was tested on the external validation cohort, it ensured a similar accuracy, with a slightly lower predictive ability (AUC of 0.63).ConclusionsRadiomics-based, data-mining from simulation CT scans was shown to be feasible and reproducible in two independent cohorts, yielding fair accuracy in the prediction of response to neoadjuvant chemo-radiation.

Adaptive carbon ion radiotherapy for locally advanced non-small cell lung cancer: Organ-sparing potential and target coverage.

BackgroundThe dose distribution of carbon ion radiotherapy (CIRT) for locally advanced non‐small cell lung cancer (LANSCLC) is highly sensitive to anatomical changes.PurposeTo demonstrate the dosimetric benefits of adaptive CIRT for LANSCLC and compare the differences between patients with and without adaptive plans based on dosimetry and clinical effect factors.Materials and methodsOf the 98 patients with LANSCLC receiving CIRT, 31 patients underwent replanning following re‐evaluations that revealed changes that would have compromised the dose coverage of the target volume or violated dose constraints. Dosimetric parameters and clinical factors were compared between patients with and without adaptive plans. Multivariate analysis identified factors influencing the adaptive planning.ResultsThe median number of fractions delivered using adaptive plans was eight (range: 2‐18). Adaptive plans ensured target coverage, and the maximum spinal cord dose was significantly decreased (p = 0.02). The median reduction in the maximum spinal cord dose was 10.4 Gy (relative biological effectiveness). Patients with adaptive plans had larger tumor volumes (p < 0.001); the median initial internal gross tumor volumes (iGTVs) of patients with adaptive and nonadaptive plans were 125.9 and 49.79 cm3, respectively. Tumor volumes of patients with adaptive plans were altered to a greater extent (p < 0.001); the median absolute percentage of volume changes in patients in the adaptive and in nonadaptive groups were 20.76% and 3.63%, respectively, while the median movements of iGTV centers were 5.75 and 2.44 mm, respectively. Binary logistic regression analysis revealed that the iGTV volume change and iGTV center movements were significantly different between the groups.ConclusionsAn adaptive plan can effectively ensure target area coverage and protect normal tissues, especially in patients with large tumor volumes and substantial changes. iGTV volume changes and iGTV center movements are the main factors influencing adaptive planning. Weekly simulation computed tomography scans are necessary for treatment evaluation in patients with LANSCLC treated with CIRT.

Comparison of defect detectability between Computed Tomography inspection and CT simulation using a calibrated defect phantom

The usage of metal Additive Manufactured (AM) components is growing in aerospace industry since the establishment of quality standards and due to the current maturity of the manufacturing systems, processing route, and inspection methodology. Moreover, possibilities of designing complex shapes by using 3D-printers enables design engineers to build lightweight structures and/or increase part functionality. However, the freedom in design is often a challenge for non-destructive testing (NDT), especially in parts with limited access, non-flat surfaces, etc. Therefore, there are few NDT methods which can be applied on such complex 3D geometries and capable of inspecting the whole part volume. Computed Tomography (CT) and Digital Xray methods are the most relevant ones offering rich information of inner defectology and the outer geometrical metrology. Simulation tools regarding manufacturing process and mechanical behavior are already considered as part of the definition phase of AM components and utilized as inputs in the design loop. However, inspectionability issues are mainly considered during the quality assurance phase. Therefore, inspection problems related with the detectability of defects above the part allowable can appear once the part have been designed, validated, and manufactured. On the other hand, tools that perform numerical simulations of X-ray imaging like SimCT could be a valuable source of additional information [1]. Firstly, for the users of XRay imaging devices in order to set up best inspection parameters and system configuration [2, 3]. Secondly, for producing CT simulations which can evaluate the inspectionability of a part, avoiding non-inspectionable regions limited by the inspection method. In this way, this information can be introduced for validating the part design for instance at PDR (Preliminary Design Review) and later at CDR (Critical Design Review) levels. In this work, an aluminum Image Quality Indicator (IQI) with calibrated defects has been analyzed with both real and simulated CT scans using different physical resolutions. In this way, a comprensive analysis for determining the limits of defect detectability by comparing both simultaion and test results has been developed. Defects of 100, 200 and 500µm diameter have been evaluated, corresponding to aerospace allowable for pores in AM hardware, depending on part criticality.

Extracting 3D Angiography Data from Simulated Computed Tomography Angiography Scans Using Low Iodine Contrast Agent

The purpose of this research is to investigate the feasibility of using low contrast agent concentration with X-ray computed tomography in visualizing and diagnosing the human vascular system while minimizing the risk of toxicity to the patient. This research investigated the effect of several iodine contrast agent concentrations on the ability to extract and visualize human vessels using simulated computed tomography scans. Monte Carlo simulation was used to perform these computed tomography acquisitions. The simulated patient was based on actual computed tomography angiography data, where a technique was developed to simulate brain vessels with contrast agents ranging from 0 mg to 20 mg of iodine. The simulation used segmented patient data along with basic image processing techniques to model the various levels of iodine concentrations. Cone beam computed tomography projections of a patient injected with and without iodine were acquired in the simulations. Subtraction of the corresponding projections was performed to generate images caused by the contrast agent. Then, histogram analysis of these differences was used to assess the validity of extracting and visualizing the human vessels. The smallest amount of iodine, 0.5 mg, helped better visualize the brain vessels and 2 mg of iodine was high enough to show almost 90% of the vessels. Additionally, the vessels were clearly visible in all the subtracted images. This research showed very promising outcomes in using low concentrations of iodine. Thus, this study proposes for the pharmaceutical companies and others interested to clinically investigate and evaluate the efficacy of using low concentrations of iodine and the associated side effects.

Deep Learning and Harmonization of Multi-Institutional Data for Automated Gross Tumor and Nodal Segmentation for Oropharyngeal Cancer

Automated tumor segmentation for oropharyngeal cancer (OPC) has the potential to improve treatment planning, response assessment, and clinical translation of imaging-based biomarkers. Deep learning has shown promise for cancer imaging segmentation, but performance for OPC tumors has been suboptimal with studies generally limited to small single-institution settings. In this study, we curated and harmonized multiple heterogeneous, multi-institutional datasets to develop and validate computed tomography (CT)-based, deep learning models for total gross tumor volume (GTV), primary (GTVp), and nodal (GTVn) segmentations.Data was obtained from The Cancer Imaging Archive (TCIA) and included 1228 CT simulation scans from OPC patients treated with definitive radiotherapy collected from 2003-2014 from four institutions that included original gross tumor volumes (GTV) as delineated by the treating radiation oncologist. GTVs were manually reviewed and ground-truth labels were harmonized to include distinct GTVp and GTVn labels. Cases were split randomly, such that 70% of cases were used for model training, 15% for tuning, and 15% for independent performance testing. Utilizing a modified 3D UNET-based architecture, models were trained and tuned to predict total GTV, GTVp and GTVn. Model performance was assessed by measuring precision, recall, and dice score coefficient (DSC) when comparing deep learning-generated to ground truth volumes in the independent test set. Patients without contrast-enhanced CT were excluded from the GTVp model a priori, given the importance of contrast in delineating primary tumor.Algorithms' median performance on test sets with 95% confidence intervals. GTVp test set limited to contrast-enhanced scans. Within the total dataset (n = 1228), median age was 59, cases were 82% male, and HPV status was 49% positive, 16% negative, and 35% unknown. Tumor staging was 45% T3-4, 54% T1-T2, and 1% T0/X. Nodal staging was 75% N2-N3 and 25% N0-1. Overall, the GTVn model had the highest performance (median DSC: 0.76; 95% CI: 0.73, 0.77), followed by total GTV (median DSC: 0.71; 95% CI: 0.68, 0.73), and GTVp (median DSC 0.68; 95% CI: 0.63, 0.71).Deep learning with multiple harmonized data sources can yield effective models for OPC primary and nodal segmentation using CT alone. The utility of these models will depend on the clinical use case and will be explored on further investigation, though current model performance metrics, particularly for nodal segmentation, are likely adequate for prospective testing in clinical and research applications.

Stereotactic Radiosurgery for Atrioventricular Node Ablation in Swine: A Study on Efficacy and Dosimetric Evaluation of Organs at Risk.

IntroductionStereotactic radiosurgery (SRS) delivered to arrhythmogenic foci within the heart is a promising treatment modality. We dosimetrically evaluated the radiation dose to the organs at risk of four swine that were successfully treated with linear-accelerator-based SRS for atrioventricular (AV) node ablation.Materials and methodsSingle‐chamber pacemakers were implanted in four large white breed swine. Cardiac computed tomography simulation scans were performed to localize the AV node and organs at risk. SRS (35-40 Gy) was delivered to the AV node, and the pigs were followed up with pacemaker interrogations. One-sample t-tests were used to evaluate Dmax of great vessels, esophagus, and chest wall as compared to known normal tissue constraints as per RTOG 0631 and AAPM Task Group 101.ResultsAll pigs had disturbances of AV conduction with progressive transition into complete heart block. Macroscopic and microscopic evaluation showed fibrosis in the AV node but did not reveal any changes in non-nodal cardiac tissue or vessels. The mean Dmax±SD (p-value) of the chest wall (14.7±3.3 (0.02)), esophagus (10.7±1.1 (<0.01)) superior vena cava (3.3±4.1 (<0.01)), right pulmonary artery (16.1±6.4 (<0.01)), right pulmonary vein (15.7± 5 (<0.01)), left pulmonary artery (11.1±1.7 (<0.01)) and left pulmonary vein (14.1±2.6 (<0.01)), and the inferior vena cava (33.68±1.6 (0.026)) were significantly below the normal tissue constraint cutoffs. Mean±SD (p-value) of the ascending aorta (19.4±16.1 (0.12)) was not significantly different than normal tissue constraint cutoffs. One swine model treated at 40 Gy had small area of hotspot in the ascending aorta (40.65 (0.4 cc)).ConclusionWe have demonstrated in our swine models that SRS using 35-40 Gy can be done without exceeding known human normal tissue constraints to the chest wall, esophagus, and great vessels.

Unmasking a Privacy Concern: Potential Identification of Patients in an Immobilization Mask from 3-Dimensional Reconstructions of Simulation Computed Tomography

AbstractPrevious studies have demonstrated that patients can be identified from 3-dimensional (3D) reconstructions of computed tomography (CT) or magnetic resonance imaging data of the brain or head and neck. This presents a privacy and security concern for scan data released to public data sets. It is unknown whether thermoplastic immobilization masks used for treatment planning in radiation therapy are sufficient to prevent facial recognition. Our study sought to evaluate whether patients with an immobilization mask could be identified on 3D reconstructions of scan data. Our study reconstructed 3D images from simulation CT (SIM-CT) scans of 35 patients and compared these to original patient photographs to test if the thermoplastic mask obfuscated facial features. Blind review from 4 facial recognition algorithms and a human (radiation oncologist) was evaluated for the ability to match 3D reconstructions of patients scans to patient images. The matching procedure was repeated against an expanded testing data set of the 35 patient photographs plus 13,233 facial photographs from the “Labeled Faces in the Wild” data set (13,268 photographs in total). Facial recognition algorithms were able to match a maximum of 83% (range, 60%-83%) of patients to the corresponding images. Radiation Oncologist blinded review correctly matched 80% of patients to the corresponding images. Ethnicity and facial hair were the most common reasons for patient mismatch. In the expanded testing data set, algorithms were also able to match a maximum of 83% (range, 57%-83%) of patients. The majority of patients were able to be identified through computer algorithm or human review even under a SIM-CT mask. These results suggest there is a potential privacy and security concern when SIM-CT data are released to publicly available data sets.

International Multi-institutional Patterns of Contouring Practice and Clinical Target Volume Recommendations for Stereotactic Body Radiation Therapy for Non-Spine Bone Metastases

Despite the increasing use of stereotactic body radiation therapy for non-spine bone metastases (NSBM), there is no established standard for target delineation. The objective of this study was to provide consensus recommendations on clinical target volume (CTV) delineation based on international expert contours. Eleven cases of NSBM were contoured by 9 international radiation oncologists. For each case, the gross tumor volume was provided on the simulation computed tomography scans with accompanying magnetic resonance imaging. Participants contoured the CTV and completed a clinical survey. Agreement between CTV contours were analyzed with simultaneous truth and performance level estimation using the kappa coefficient and the Dice similarity coefficient (DSC) and summarized to establish contouring recommendations. A direction-dependent analysis was applied to the consensus contours to quantify margins. All CTV contours were completed. Six participants used a single-dose level, whereas 3 used a 2-dose level simultaneous integrated boost (SIB) technique. For the SIB cases, the largest volume receiving a stereotactic body radiation therapy (SBRT) dose was used for contour analysis. There was substantial agreement between contours across cases with a mean kappa of 0.72 (mean sensitivity 0.85, mean specificity 0.97). The mean DSC value was 0.77 (range, 0.67-0.87). Consensus CTV contouring recommendations were (1) an intraosseous CTV margin of 5 to 10 mm should be strongly considered within contiguous bone; (2) an extraosseous margin of 5 to 10 mm should be strongly considered where there is soft tissue disease or cortical bone disruption; (3) CTVs should be manually cropped to respect anatomic barriers to spread (eg, peritoneal cavity, pleura, uninvolved joint space and cortical bone). CTV contouring recommendations for NSBM-SBRT were established based on analysis of international expert consensus contours with a high level of agreement. These principles may provide guidance to treating physicians and inform future study until prospective clinical data can provide further refinement.

Dosimetry Comparison of Palliative Radiation Plans Generated From Available Diagnostic CT Images Versus Dedicated CT Simulation for Inpatients.

IntroductionThe morbidity sequelae of advanced cancer are often irreversible. Early palliative radiation can prevent, delay, and even improve these consequences. Treatment may be delayed due to a packed computed tomography (CT) simulation schedule or other logistics, including the cost and burden of arranging ambulance transportation when radiation centers are off-site.ObjectivesThe primary objective was to determine the feasibility of using a recent diagnostic CT scan in lieu of a dedicated simulation CT to generate an adequate plan without sacrificing dosimetric goals and subsequent efficacy or tolerability. Secondary objectives included how much the lesion has grown, and how much earlier treatment could start if planned on a diagnostic CT scan.Materials/MethodsFor each inpatient treated with palliative radiation, a prior recent diagnostic CT scan was imported into the RayStation (RaySearch Laboratories, Stockholm, Sweden) planning system. From these diagnostic scans, planning treatment volumes (PTV) and organs at risk (OAR) were contoured using the same technique as the patient’s actual treatment. The primary outcome was to compare both the PTV coverage and OAR dose between the plan generated from the diagnostic CT compared to that from the simulation CT. Our secondary outcomes include the mean time between CT simulation and first treatment, change in tumor volume between diagnostic scan and CT simulation, and the hottest 1% of each plan (D1).ResultsBetween May and August 2019, a total of 22 inpatients were treated palliatively. Of those 22 patients, 10 patients (ages 32-92 years, median 64.5 years, 50% spine) met study criteria and had a diagnostic CT scan that was obtained within 14 days of simulation CT that was also compatible with our planning software. In the plans that were delivered, a mean of 98.8% (range 94.4-100%) of PTV was covered by at least 95% prescription dose. In the diagnostic CT plans, a mean of 95.4% (range 84.5-100%) of PTV was covered by at least 95% prescription dose. The difference between plans trended towards significance (p=0.061). When looking at patients receiving treatment to the spine or having a diagnostic CT within four days of the simulation CT, there was no statistically significant difference between the two plans (p=0.032 and 0.030, respectively). The OARs received, on average, 1.4% less mean radiation dose in the hypothetical plans (p=0.911). All OAR constraints were met in both groups. The mean time between diagnostic CT and CT simulation was 5.9 days and between CT simulation and first treatment was 1.9 days (range 0-5 days). The mean change in tumor volume was 22.64% smaller in the diagnostic CT scan plan. The D1 was an average 1% hotter in the hypothetical plans (p=0.16).ConclusionIn hospitalized patients with an indication for palliative radiation, treatment planning on a pre-existing recent diagnostic CT scan produces comparable dose distributions without increases in dose to OARs when compared to the use of CT simulation scans, particularly for the treatment of the spine or when a very recent diagnostic CT is available. Bypassing CT simulation in select cases allows for earlier delivery of radiation with less patient and logistical burden. In combination with daily image guidance, this may translate to more timely delivery of radiation, less cost and burden to critically ill patients, and improved palliative benefit.