Patient Computed Tomography Data Research Articles

Effect of the application of digital technology-assisted optimization in the process of adjusting jaw position.

The aim of this study was to demonstrate a novel jaw position adjustment technique derived from digital twins and evaluate the application effect of digital technology-assisted optimization in the process of adjusting jaw position on patients with temporomandibular disorders (TMD). A total of 74 patients with TMD who attended the Department of Temporomandibular Joint, West China Hospital of Stomatology, Si-chuan University, between June 2022 and May 2023 were selected. The patient's initial computed tomography (CT) and bilateral temporomandibular joint data obtained by magnetic resonance imaging (MRI) were collected. The 148 joints were divided into the normal disc-condyle relationship (N) group, disc displacement with reduction (DDWR) group, and disc displacement without reduction (DDWoR) group. Assisted by digital technology, the patient's CT data were reconstructed, and a personalized reference plane was established to adjust the jaw position. A three-point bite guiding splint was designed by the adjusted occlusal space and then fabricated by 3D printing technology. It was worn by the patients and then reviewed by MRI. Before and after the adjustment of jaw position, the amount and direction of condyle and disc displacement and the angle between condyle and disc were measured as the evaluation indexes of the effect of the adjustment. The correlation with condylar displacement was evaluated. In the N group, the disc moved backward and downward along the X and Z axes by (-0.60±0.62) and (0.51±0.71) mm, respectively. In the DDWR group, the disc moved backward and upward along the X and Z axes by (-1.33±1.38) and (-0.09±1.31) mm, respectively. In the DDWoR group, the disc moved forward and downward along the X and Z axes by (0.49±1.76) and (1.35±1.76) mm, respectively. The angle between the condyle and the disc decreased after adjustment of the jaw position in all three groups. All patients showed improvement in symptoms after adjustment. Digital technology-assisted jaw position adjustment can simplify the process, reduce the sensitivity of the technique, and improve patients' disc-condyle structure and symptoms. Therefore, its application in the treatment of patients with TMD is of great clinical significance.

In silico study of the electronic portal imaging devices sensitivity to parotid glands shrinkage during radiotherapy for nasopharyngeal cases

This computational investigation examines the capability of electronic portal imaging devices (EPIDs) in detecting anatomical changes during radiation therapy. A photon beam for 6 MV was simulated using compliant phase-space files via BEAMnrc (an EGSnrc user code). Initially, the dose calculation was carried out using DOSXYZnrc, an EGSnrc user code, on a water-based calibration phantom made of water. Phantoms, constructed utilizing patient computed tomography data, were subsequently used to simulate clinically relevant parotid gland (PG) shrinkage through image deformation using ImSimQA software. Two EPID geometries were implemented; a simple water slab, and a comprehensive multi-layer representation of the EPID (referred to as the 17-slab model). Using a 1%/1 mm gamma index the 17-slab geometry was sensitive to volume reductions in the PG ≥ − 26% from its original volume, while the water slab detected volume reduction of ≥ − 28.5%. In a clinical setting, replanning of head and neck patients is initiated when a reduction of 24–30% in PG volume is detected through cone-beam computed tomography. The sensitivity index, indicative of the signal to error ratio between the reference and evaluated images, showed an increase as the gland volume decreased for both models. Notably, the 17-slab model consistently more sensitive than the water slab. This computational study highlights the prospective use of EPIDs in monitoring and detecting clinically significant volume changes in the PG during radiation treatment. Advances in knowledge: While EPIDs are frequently employed for patient setup and alignment, this computational work constructed a virtual EPID to assess its detection abilities for the anatomical changes in the PG due to radiation exposure in head and neck cancer cases.

CT Findings of Stage IA Ovarian Cancer: Comparison between Borderline Tumor and Stage IC Ovarian Cancer.

Ovarian cancer is a common gynecological malignant tumor in women. Most patients have reached the advanced stage when they visit the hospital. In order to diagnose ovarian cancer at an early stage, treat it at an early stage, and improve the survival rate of patients, this study has used the imaging computed tomography (CT) method to diagnose stage IA ovarian cancer. The purpose of this work was to study CT features of stage IA ovarian cancer, and compare the borderline tumor and stage IC ovarian cancer at the same time so as to improve the CT diagnosis of early ovarian cancer. We retrospectively collected clinical and CT data of patients with stage I ovarian cancer and borderline ovarian tumor admitted to Nantong Tumor Hospital from 2013 to 2021. Altogether, 23 cases of patients (borderline ovarian tumor, 9 cases; stage IA ovarian cancer, 5 cases; stage IC ovarian cancer, 9 cases) were involved. CT characteristics of these patients were analyzed in terms of the tumor diameter, cystic solid structure, solid component, septation, enhancement, peritoneal thickening, ascites, and abdominal lymph nodes. CT features of stage IA ovarian cancer included large tumor size (average diameter: 15 cm), cystic solid structure (4/5; 80%), septation (4/5; 80%), and enhanced cystic wall, septum, or solid components of the tumor on contrast-enhanced CT (5/5; 100%) no peritoneal thickening (0/5; 0%), no ascites (0/5; 0%), and no abdominal lymph node enlargement (0/5; 0%). The tumor structure did not differ significantly between stage IA and IC ovarian cancers (p > 0.05), while intraperitoneal ascites did (χ2 = 0.031; p < 0.05). Stage IA and borderline ovarian tumors did not differ significantly in ovarian tumor structure (p > 0.05). CT features of stage IA ovarian cancer included large tumor size, cystic solid structure, septation, and enhanced cystic wall and solid parts in the tumors. No pelvic or abdominal metastasis was observed.

Simulation of consequences of using nonideal detectors during beam data commissioning measurements.

Beam data commissioning is a core task of radiotherapy physicists. Despite multiple detectors available, a feasible measurement program compromises between detector properties and time constraints. Therefore, it is important to understand how nonideal measurement data propagates into patient dose calculation. We simulated the effects of realistic errors, due to beam commissioning with presumably nonoptimal detectors, on the resulting patient dose distributions. Additionally, the detectability of such beam commissioning errors during patient plan quality assurance (QA) was evaluated. A clinically used beam model was re-commissioned introducing changes to depth dose curves, output factors, profiles or combinations of those. Seventeen altered beam models with incremental changes of the modelling parameters were created to analyze dose changes on simplified anatomical phantoms. Additionally, fourteen altered models incorporate changes in the order of signal differences reported for typically used detectors. Eighteen treatment plans of different types were recalculated on patient CT data sets using the altered beam models. For the majority of clinical plans, dose distributions in the target volume recalculated on the patient computed tomography data were similar between the original and the modified beam models, yielding global 2%/2mm gamma pass rates above 98.9%. Larger changes were observed for certain combinations of beam modelling errors and anatomical sites, most extreme for output factor changes in a small target volume plan with a pass rate of 80.6%. Modelling an enlarged penumbra as if measured with a 0.125cm3 ion chamber had the largest effect on the dose distribution (average pass rate of 96.5%, lowest 85.4%). On different QA phantom geometries, dose distributions between calculations with modified and unmodified models typically changed too little to be detected in actual measurements. While the simulated errors during beam modelling had little effect on most plans, in some cases changes were considerable. High-quality penumbra and small field output factor should be a main focus of commissioning measurements. Detecting modelling issues using standard patient QA phantoms is unlikely. Verification of a beam model should be performed especially for plans with high modulation and in different depths or geometries representing the variety of situations expected clinically.

Computed Tomography Is Predictive of Significant Neurologic Injury in Children Supported on Extracorporeal Membrane Oxygenation.

Computed Tomography Is Predictive of Significant Neurologic Injury in Children Supported on Extracorporeal Membrane Oxygenation.

Design and biomechanical analysis of patientspecific porous tantalum prostheses for knee joint revision surgery.

Artificial joint revision surgery, as an increasingly common surgery in orthopedics, often requires patient-specific prostheses to repair the bone defect. Porous tantalum is a good candidate due to its excellent abrasion and corrosion resistance and good osteointegration. Combination of 3D printing technology and numerical simulation is a promising strategy to design and prepare patient-specific porous prostheses. However, clinical design cases have rarely been reported, especially from the viewpoint of biomechanical matching with the patient's weight and motion and specific bone tissue. This work reports a clinical case on the design and mechanical analysis of 3D-printed porous tantalum prostheses for the knee revision of an 84-year-old male patient. Particularly, standard cylinders of 3D-printed porous tantalum with different pore size and wire diameters were first fabricated and their compressive mechanical properties were measured for following numerical simulation. Subsequently, patientspecific finite element models for the knee prosthesis and the tibia were constructed from the patient's computed tomography data. The maximum von Mises stress and displacement of the prostheses and tibia and the maximum compressive strain of the tibia were numerically simulated under two loading conditions by using finite element analysis software ABAQUS. Finally, by comparing the simulated data to the biomechanical requirements for the prosthesis and the tibia, a patient-specific porous tantalum knee joint prosthesis with a pore diameter of 600 μm and a wire diameter of 900 μm was determined. The Young's modulus (5719.32 ± 100.61 MPa) and yield strength (172.71 ± 1.67 MPa) of the prosthesis can produce both sufficient mechanical support and biomechanical stimulation to the tibia. This work provides a useful guidance for designing and evaluating a patient-specific porous tantalum prosthesis.

The effect of exacerbations on lung density in α1-antitrypsin deficiency.

Acute exacerbations of COPD (AECOPD) have unclear impacts on emphysema measurement using computed tomography (CT)-derived 15th percentile lung density (PD15). The aim of this study was to assess the influence of AECOPD on PD15 lung density in α1-antitrypsin deficiency. In a post hoc analysis of the RAPID (Randomised Trial of Augmentation Therapy in α1-Proteinase Inhibitor Deficiency) trial, raw marginal residuals of PD15 (measured - predicted) were determined by fitting a regression line to individual patient CT data. These deviations from the expected slope were compared by age, sex, baseline forced expiratory volume in 1 s, diffusing capacity of the lungs for carbon monoxide % predicted and PD15, inhaled corticosteroid use and treatment group. Positive and negative residuals (reflecting higher or lower lung density than predicted from regression) were observed, which declined in magnitude over time following AECOPD events. Logistic regression confirmed a limited effect of patient characteristics on the absolute size of residuals, whereas AECOPD within 6 weeks of CT had a notable effect versus no AECOPD within 6 weeks (OR 5.707, 95% CI 3.375-9.652; p<0.0001). AECOPD result in higher or lower CT lung density estimates; the effect is greatest in the 2 weeks immediately after an AECOPD and persists for <6 weeks. Patient characteristics were less relevant than AECOPD within 6 weeks, supporting the reliability of PD15 as a measure of lung density. An exacerbation-free period prior to CT scan is advisable to reduce signal-to-noise ratio in future clinical trials.

Prediction model based on preoperative CT findings for carotid artery invasion in patients with head and neck masses.

ObjectivesTo investigate the performance of a model in predicting carotid artery (CA) invasion in patients with head and neck masses using computed tomography (CT).MethodsThis retrospective study included patients with head and neck masses who underwent CT and surgery between January 2013 and July 2021. Patient characteristics and ten CT features were assessed by two radiologists. The patients were randomly allocated to a training cohort (n=106) and a validation cohort (n=109). Independent risk factors for CA invasion were assessed by univariate and multivariate logistic regression analyses. The predictive model was established as a nomogram using the training cohort. In addition, the calibration, discrimination, reclassification, and clinical application of the model were assessed in the validation cohort.ResultsA total of 215 patients were evaluated, including 54 patients with CA invasion. Vascular wall deformation (odds ratio [OR], 7.17; p=0.02) and the extent of encasement to the CA (OR, 1.02; p<0.001) were independent predictors of CA invasion in the multivariable analysis in the training cohort. The performance of the model was similar between the training and validation cohort, with an area under the receiver operating characteristic curve of 0.93 (95% confidence intervals [CI], 0.88-0.98) and 0.88 (95% CI, 0.80-0.96) (p=0.07), respectively. The calibration curve showed a good agreement between the predicted and actual probabilities.ConclusionA predictive model for carotid artery invasion can be defined based on features that come from patient characteristics and CT data to help in improve surgical planning and invasion evaluation.

Remote results of the course of non-acute viral pneumonia COVID-19 according to computed tomography

Objective — to study the dynamics of changes in the structure of the lungs in patients with a complicated course of non-hospital viral pneumonia COVID-19 by programmatic processing of computed tomography (CT) scan data and to determine the features of changes to predict its course.&#x0D; Materials and methods. The dynamics of these CT scan 70 patients for non-hospital pneumonia of viral etiology (COVID-19) was analyzed. The diagnosis of COVID-19 was established in accordance with the current protocols for the treatment of coronavirus disease. CT scan was performed on the scanner Aquilion TSX-101A Tochiba (Japan) with the recording of the results on digital media with their subsequent software processing. &#x0D; Results and discussion. Of the 70 patients for non-hospital pneumonia of viral etiology (COVID-19), 21 patients (15 men and 6 women) were diagnosed with «disappearing lung syndrome», which accounted for 30 % of all those examined. Cases of the formation of massive fibrous post-inflammatory changes were not detected.We performed software processing of CT data of patients’ OGC in dynamics during 6—12 months. On average, control CT scans of the OGC were performed every 2—3 months. At the same time we determined the peculiarities of densitometric indexes dynamics, according to which we could predict the course of non-hospital pneumonia of viral etiology (COVID-19).&#x0D; Conclusions. Given the high percentage of diagnosis of «disappearing lung syndrome» caused by the autoimmune process in patients with complicated non-hospital viral pneumonia COVID-19, it is necessary to develop methods for its treatment. In order to diagnose complications, prevent them and predict the course of severe forms of pneumonia pneumonia COVID-19, it is necessary to conduct a CT scan in dynamics with densitometric examinations of the pulmonary parenchyma.

Framework of metal insertion in the projection domain for image quality optimization in interventional computed tomography.

Purpose: This work aims to develop a framework to accurately and efficiently simulate metallic objects used during interventional oncology (IO) procedures and their artifacts in computed tomography (CT) images of different body regions. Approach: A metal insertion framework based on an existing lesion insertion tool was developed. Noise and beam hardening models were incorporated into the model and validated by comparing images of real and artificially inserted metallic rods of known material composition and dimensions. The framework was further validated by inserting ablation probes into a water phantom and comparing image appearance to scans of real probes at matching locations in the phantom. Finally, a comprehensive library of metallic probes used in our IO practice was generated and a graphical user interface was built to efficiently insert any number of probes at arbitrary positions in patient CT data, including projection and image domain insertions. Results: Metallic rod experiments demonstrated that noise and beam hardening were properly modeled. Phantom and patient data with virtually inserted probes demonstrated similar artifact appearance and magnitude compared with real probes. The developed user interface resulted in accurately co-registered virtual probes both with and without accompanying artifacts from projection and image domain insertions, respectively. Conclusions: The developed metal insertion framework successfully replicates metallic object and artifact appearance with projection domain insertions and provides corresponding artifact-free images with the metallic object in the identical location through image domain insertion. This framework has potential to generate robust training libraries for deep learning algorithms and facilitate image quality optimization in interventional CT.

MS-ResNet: disease-specific survival prediction using longitudinal CT images and clinical data.

PurposeMedical imaging data of lung cancer in different stages contain a large amount of time information related to its evolution (emergence, development, or extinction). We try to explore the evolution process of lung images in time dimension to improve the prediction of lung cancer survival by using longitudinal CT images and clinical data jointly.MethodsIn this paper, we propose an innovative multi-branch spatiotemporal residual network (MS-ResNet) for disease-specific survival (DSS) prediction by integrating the longitudinal computed tomography (CT) images at different times and clinical data. Specifically, we first extract the deep features from the multi-period CT images by an improved residual network. Then, the feature selection algorithm is used to select the most relevant feature subset from the clinical data. Finally, we integrate the deep features and feature subsets to take full advantage of the complementarity between the two types of data to generate the final prediction results.ResultsThe experimental results demonstrate that our MS-ResNet model is superior to other methods, achieving a promising 86.78% accuracy in the classification of short-survivor, med-survivor, and long-survivor.ConclusionIn computer-aided prognostic analysis of cancer, the time dimension features of the course of disease and the integration of patient clinical data and CT data can effectively improve the prediction accuracy.

Clinical and baseline computed tomography features of patients infected with the B.1.617.2 (Delta) variant of severe acute respiratory syndrome coronavirus 2

PURPOSE: The purpose of this study was to investigate the clinical and baseline computed tomography (CT) features and their correlation in patients infected with the B.1.617.2 (Delta) variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).MATERIALS AND METHODS: Clinical and chest baseline CT data of patients infected with the Delta variant of SARS-CoV-2 from July to August 2021 were collected. First, the correlation between the clinical data and baseline CT results was analyzed according to CT positivity or negativity. Then, subgroup analysis was performed between different age distributions and clinical characteristics. Next, the CT characteristics and clinical data of all baseline CT-positive patients were collected, and the correlations between CT characteristics and age, vaccination status, and chronic disease were analyzed. Lesions in patients with baseline CT positivity were evaluated by semi-quantitative scoring to analyze the correlations between the semi-quantitative scores and vaccination status and age distribution.RESULTS: A total of 221 nucleic acid-positive patients with the SARS-CoV-2 Delta variant were included, of whom 107 patients were baseline CT positive and 114 were baseline CT negative. Baseline CT positivity was associated with age distribution, and baseline CT positivity was most common in patients aged >60 years (P < 0.001), but not with vaccination status or gender. The results of the subgroup analysis according to age distribution indicated that different age distribution subgroups had different vaccination statuses, and the majority of patients aged <18 years and >60 years were unvaccinated (90.5%, 19/21, and 57.3%, 63/110, respectively). In contrast, most patients aged 18–60 years had received two doses of the vaccine (61.1%, 55/90) (P < 0.001). Different age distribution subgroups had different clinical infection types. Asymptomatic and mild cases were most common in patients aged ≤60 years, and moderate and severe or critical cases were most common in patients aged >60 years. For baseline CT-positive patients, the extent of lung involvement was associated with age, vaccination status, and chronic disease. The number of involved lobes was higher in patients who were unvaccinated or who had received one injection, who were aged >60 years or had chronic disease. There was a statistical difference in CT semi-quantitative scores between the different age subgroups. Compared with patients aged < 60 years, patients aged >60 years had higher semi-quantitative scores (P < 0.001). However, there was no statistical difference between the different vaccination groups.CONCLUSIONS: Age had a large effect on baseline CT positivity, CT characteristics, and semi-quantitative CT scores in patients infected with the Delta variant.

Mathematical postprocessing analysis of contrast-enhanced computed tomography data of the kidneys in evaluation of split renal function in patients with kidney stone disease

Introduction. An original research work was performed to assess split kidney function by glomerular filtration rate (GFR) with mathematical analysis of the kidneys computed tomography (CT) data in patients with kidney stone disease (KSD). Objective was to evaluate the GFR and the parenchyma structure of each kidney and identify the possible patterns of contrast medium intrarenal transport with mathematical analysis of the kidneys CT data in patients with KSD.Materials and methods. Data of 27 patients of both genders with KSD were retrospectively analyzed. To evaluate GFR separately for each kidney we analyzed the data of contrast-enhanced CT (GFR reference values are 0.55 % of contrast medium per second). Inclusion criteria are as follows: 1) newly diagnosed SKD; 2) stone size ≤1,5-2,0 cm, no obstruction of the urine flow registered; 3) no kidney or upper urinary tract surgical history; 4) age - ≤45 years; 5) no severe chronic diseases. All of these allowed to minimize influence of any other disorders on split renal function except for SKD and conduct per se research.Results. The mathematic analysis of the contrast-enhanced CT data revealed GFR changes in 26 (96.3 %) out of 27 patients. Hyperfiltration was found in 12 (44.4 %) patients: right kidney GFR - 0.6-0.77 %, mean value - 0.65 %; left kidney GFR - 0.59-0.79 %, mean value - 0.67 %. Hypofiltration was found in 13 (48.1 %) patients: right kidney GFR - 0.2-0.54 %, mean value - 0.37 %; left kidney GFR - 0.2-0.53 %, mean value - 0.4 %. The GFR values significantly differed between the groups both for the right (p = 0.000014) and left (p = 0.000045) kidneys. We found no significant age-related difference between the groups (p = 0.895). As well as that no significant differences in Resistance Index both in magistral (right kidney: p = 0.221; left kidney: p = 0.850) and segmental (right kidney: p = 0.306; left kidney: p = 0.957) arteries between the groups with hyperfiltration and hypofiltration were observed. One patient demonstrated no changes in GFR, and the other one had hyperfiltration (0,62 %) in one kidney and hypofiltration (0,48 %) in another.Conclusion. Most of the patients (92.6 %) with SKD demonstrate GFR changes (either hyperfiltration or hypofiltration) that may indicate the disturbed intrarenal blood and urine flow through the kidney.

Clinical and Computed Tomography (CT) Characteristics of Pulmonary Nodules Caused by Cryptococcal Infection.

PurposeTo investigate the clinical and computed tomography (CT) characteristics of pulmonary nodules caused by cryptococcal infection and gain a greater insight and understanding that will reduce misdiagnosis.Materials and MethodsA total of 47 patients with confirmed pulmonary cryptococcosis (PC) manifested as nodules were retrospectively enrolled from January 2013 to December 2020. The clinical and CT data of patients with single and multiple nodules were analyzed and compared with emphasis on exploring the characteristics of the solitary ones.ResultsAmong the 47 patients, single and multiple nodules were detected in 25 (53.2%) and 22 (46.8%) patients, respectively, with similar clinical characteristics. The diameter of solitary nodules was smaller than that of the largest ones of the multiple nodules (P = 0.000). Compared with multiple nodules, less solitary nodules were located in the subpleural zone (P = 0.031) and had a cavity or vacuole (P = 0.049). Regarding the solitary nodules, 22 (88.0%) and 3 (12.0%) were solid and subsolid, respectively. Eighteen (81.8%) solid solitary nodules exhibited either one of air bronchogram or vacuole, satellite lesions, and halo sign. Twenty-three (92.0%) of 25 patients with solitary nodules had follow-up CT data, and most of them had no changes (12, 52.1%) or increased in size and/or density (8, 34.8%), but only a few (3, 13.0%) decreased with or without anti-inflammatory therapy.ConclusionThe clinical characteristics of patients with solitary and multiple nodules caused by PC are similar. The solitary nodules are smaller, more scattered, but less necrotic than multiple ones. For solitary nodules with air bronchogram or vacuole, satellite lesions, or halo sign, PC should be considered as one of the differential diagnosis.

Hemodynamic Analysis on the Anastomosis Angle in Arteriovenous Graft Using Multiphase Blood Model

Numerical analysis was performed for the effect of the venous anastomosis angle in a forearm arteriovenous graft for hemodialysis using a multiphase blood model. The geometry of the blood vessel was generated based on the patient-computed tomography data. The anastomosis angles were set at 15°, 30°, and 45°. The hematocrit was set at 34%, 45%, and 58%. The larger anastomosis angle, high wall shear stress area &gt;11 Pa, increases to the side of the vein wall away from the anastomosis site. Further, the relatively low wall shear stress area, &lt;3 Pa, occurs near the anastomosis site in larger anastomosis angles. Therefore, the effect of high wall shear stress has advantages in the vicinity of the anastomosis, as the anastomosis angle is larger, but disadvantages as the distance from the anastomosis increases. Moreover, patients with low hematocrit are advantageous for WSS area.

Development of clinical application program for radiotherapy induced cancer risk calculation using Monte Carlo engine in volumetric-modulated arc therapy

BackgroundThe purpose of this study is to develop a clinical application program that automatically calculates the effect for secondary cancer risk (SCR) of individual patient. The program was designed based on accurate dose calculations using patient computed tomography (CT) data and Monte Carlo engine. Automated patient-specific evaluation program was configured to calculate SCR.MethodsThe application program is designed to re-calculate the beam sequence of treatment plan using the Monte Carlo engine and patient CT data, so it is possible to accurately calculate and evaluate scatter and leakage radiation, difficult to calculate in TPS. The Monte Carlo dose calculation system was performed through stoichiometric calibration using patient CT data. The automatic SCR evaluation program in application program created with a MATLAB was set to analyze the results to calculate SCR. The SCR for organ of patient was calculated based on Biological Effects of Ionizing Radiation (BEIR) VII models. The program is designed to sequentially calculate organ equivalent dose (OED), excess absolute risk (EAR), excess relative risk (ERR), and the lifetime attributable risk (LAR) in consideration of 3D dose distribution analysis. In order to confirm the usefulness of the developed clinical application program, the result values from clinical application program were compared with the manual calculation method used in the previous study.ResultsThe OED values calculated in program were calculated to be at most approximately 13.3% higher than results in TPS. The SCR result calculated by the developed clinical application program showed a maximum difference of 1.24% compared to the result of the conventional manual calculation method. And it was confirmed that EAR, ERR and LAR values can be easily calculated by changing the biological parameters.ConclusionsWe have developed a patient-specific SCR evaluation program that can be used conveniently in the clinic. The program consists of a Monte Carlo dose calculation system for accurate calculation of scatter and leakage radiation and a patient-specific automatic SCR evaluation program using 3D dose distribution. The clinical application program that improved the disadvantages of the existing process can be used as an index for evaluating a patient treatment plan.

Evaluation of a portable assembly catheter simulator using a 3D-printed heart model for percutaneous transvenous mitral commissurotomy in developing countries: Catheter simulator for PTMC.

We developed a catheter simulator for percutaneous transvenous mitral commissurotomy (PTMC) based on the data from a patient with mitral valve stenosis. The simulator has the following characteristics: 1) the simulator is portable and easy to assemble and disassemble, 2) the cardiac portion is created using a 3D-printer, based on patient computed tomography data, 3) the simulator uses a foot-operated water pump to create pulsatile flow, and 4) the fossa ovalis in the atrial septum of the heart model is made of a thin polyurethane membrane and is interchangeable. We aimed to assess the effectiveness of this novel simulator for training in PTMC using the Inoue balloon in developing countries. We used this simulator for training in the National Institute of Cardiovascular Diseases in Bangladesh (13 physicians), and in Kenyatta National Hospital in Kenya (11 physicians). The effectiveness of training was evaluated by questionnaire and the procedure time in simulation. The questionnaire obtained from the trainees showed that the model scored 4.7±0.5 for realism, utility of pulsatile flow scored 4.7±0.5, simulator utility scored 4.9±0.3, and the effect of training on PTMC performance scored 4.9±0.5. The procedure time in simulation was shortened from 30.0±12.6 min (first time), to 23.4±11.9 min (second time) and to 20.4 ± 11.1 min (third time) (p<0.01). The novel portable assembly catheter simulator using a 3D-printed heart model for PTMC received positive comments and improved the skills of trainees.

Classification of the source of treatment deviation in proton therapy using prompt-gamma imaging information.

Prompt-gamma imaging (PGI)-based range verification has been successfully implemented in clinical proton therapy recently and its sensitivity to detect treatment deviations is currently investigated. The cause of treatment deviations can be multiple - for example, computed tomography (CT)-based range prediction, patient setup, and anatomical changes. Hence, it would be beneficial, if PGI-based verification would not only detect a treatment deviation but would also be able to directly identify its most probable source. Here, we propose a heuristically derived decision tree approach to automatically classify the sources of range deviation in proton pencil-beam scanning (PBS) treatments of head and neck tumors based on range information obtained with a PGI slit camera. The decision tree model was iteratively generated on a training dataset of different anatomical complexities, for an anthropomorphic head phantom and patient CT data (planning and control CTs) from five patients. Different range prediction errors, setup changes and relevant and nonrelevant anatomical changes were introduced or derived from control CTs, summing up to a total of 98 training scenarios. Independent validation was performed for another 98 scenarios, derived solely from patient CT data of another seven patients. PBS head and neck treatment plans were generated for the nominal scenario. For all PBS spots in the investigated field, PGI profiles were simulated using a dedicated analytical model of the slit camera for the nominal as well as the different error scenarios. From comparison of PGI profiles for nominal and error scenarios, a spot-wise range shift after spot aggregation with a kernel of 7mm sigma was determined for each error scenario. The heuristic approach includes a prefiltering of the most suitable PBS spots for PGI treatment verification. From the validation, the accuracy, sensitivity, and specificity of the model were determined. A five-step consecutive filter was developed to preselect PBS spots. On average, 25% of spots (1044 spots) remained as input for the classification model. The derived heuristic decision tree model is based on five parameters: The coefficient of determination (R2 ), the slope and intercept of the linear regression between PGI-derived range shifts and the respectively predicted proton ranges for the investigated PBS spots, as well as the average and standard deviation of the PGI-derived shifts. With this approach, 94 of 98 error scenarios could be classified correctly in validation (accuracy of 96%). A sensitivity and specificity of 100% and 86% were reached. In this simulation study it was demonstrated that the source of a treatment deviation can be identified from simulated noiseless PGI information in head and neck tumor treatments with high sensitivity and specificity. The application, refinement, and evaluation of the approach on measured PGI data will be the next step to show the clinical feasibility of PGI-based error source classification.

Convolutional-neural-network-based diagnosis of appendicitis via CT scans in patients with acute abdominal pain presenting in the emergency department

Acute appendicitis is one of the most common causes of abdominal emergencies. We investigated the feasibility of a neural-network-based diagnosis algorithm of appendicitis by using computed tomography (CT) for patients with acute abdominal pain visiting the emergency room (ER). A neural-network-based diagnostic algorithm of appendicitis was developed and validated using CT data from three institutions who visited the ER with abdominal pain and underwent abdominopelvic CT. For input data, 3D isotropic cubes including the appendix were manually extracted and labeled as appendicitis or a normal appendix. A 3D convolutional neural network (CNN) was trained to binary classification on the input. For model development and testing, 8-fold cross validation was conducted for internal validation and an ensemble model was used for external validation. Diagnostic performance was excellent in both the internal and external validation with an accuracy larger than 90%. The CNN-based diagnosis algorithm may be feasible in diagnosing acute appendicitis using the CT data of patients visiting the ER with acute abdominal pain.

Computed tomography imaging characteristics of clear cell papillary renal cell carcinoma.

ABSTRACTPurpose:Clear cell papillary (CCP) renal cell carcinoma (RCC) is a new subtype of RCC that was formally recognized by the International Society of Urological Pathology Vancouver Classification of Renal Neoplasia in 2013. Subsequently, CCP RCC was added to the 2016 World Health Organization Classification of Tumors of the Urinary System and Male Genital Organs. In this study, we retrospectively investigated the computed tomography (CT) findings of pathologically diagnosed CCP RCC.Materials and Methods:This study included 12 patients pathologically diagnosed with CCP RCC at our institution between 2015 and 2017. We reviewed the patient's CT data and analyzed the characteristics.Results:Nine solid masses and 3 cystic masses with a mean tumor size of 22.7±9.2mm were included. Solid masses exhibited slight hyper-density on unenhanced CT with a mean value of 34±6 Hounsfield units (HU), good enhancement in the corticomedullary phase with a mean of 195±34HU, and washout in the nephrogenic phase with a mean of 133±29HU. The walls of cystic masses enhanced gradually during the corticomedullary and nephrogenic phases. Solid and cystic masses were preoperatively diagnosed as clear cell RCC and cystic RCC, respectively.Conclusions:The CT imaging characteristics of CCP RCCs could be categorized into either the solid or cystic type. These masses were diagnosed radiologically as clear cell RCC and cystic RCC, respectively.