Evaluation of GPT-4's Chest X-Ray Impression Generation: A Reader Study on Performance and Perception.

Exploring the generative capabilities of the multimodal GPT-4, our study uncovered significant differences between radiological assessments and automatic evaluation metrics for chest x-ray impression generation and revealed radiological bias.

Immunohistochemistry Reveals TRPC Channels in the Human Hearing Organ-A Novel CT-Guided Approach to the Cochlea.

TRPC channels are critical players in cochlear hair cells and sensory neurons, as demonstrated in animal experiments. However, evidence for TRPC expression in the human cochlea is still lacking. This reflects the logistic and practical difficulties in obtaining human cochleae. The purpose of this study was to detect TRPC6, TRPC5 and TRPC3 in the human cochlea. Temporal bone pairs were excised from ten body donors, and the inner ear was first assessed based on computed tomography scans. Decalcification was then performed using 20% EDTA solutions. Immunohistochemistry with knockout-tested antibodies followed. The organ of Corti, the stria vascularis, the spiral lamina, spiral ganglion neurons and cochlear nerves were specifically stained. This unique report of TRPC channels in the human cochlea supports the hypothesis of the potentially critical role of TRPC channels in human cochlear health and disease which has been suggested in previous rodent experiments.

Multiple breath washout (MBW) testing using sulfur hexafluoride: reference values and influence of anthropometric parameters.

BACKGROUND: Multiple breath washout (MBW) using sulfur hexafluoride (SF6) has the potential to reveal ventilation heterogeneity which is frequent in patients with obstructive lung disease and associated small airway dysfunction. However, reference data are scarce for this technique and mostly restricted to younger cohorts. We therefore set out to evaluate the influence of anthropometric parameters on SF6-MBW reference values in pulmonary healthy adults. METHODS: We evaluated cross-sectional data from 100 pulmonary healthy never-smokers and smokers (mean 51 (SD 20), range 20-88 years). Lung clearance index (LCI), acinar (Sacin) and conductive (Scond) ventilation heterogeneity were derived from triplicate SF6-MBW measurements. Global ventilation heterogeneity was calculated for the 2.5% (LCI2.5) and 5% (LCI5) stopping points. Upper limit of normal (ULN) was defined as the 95th percentile. RESULTS: Age was the only meaningful parameter influencing SF6-MBW parameters, explaining 47% (CI 33% to 59%) of the variance in LCI, 32% (CI 18% to 47%) in Sacin and 10% (CI 2% to 22%) in Scond. Mean LCI increases from 6.3 (ULN 7.4) to 8.8 (ULN 9.9) in subjects between 20 and 90 years. Smoking accounted for 2% (CI 0% to 8%) of the variability in LCI, 4% (CI 0% to 13%) in Sacin and 3% (CI 0% to 13%) in Scond. CONCLUSION: SF6-MBW outcome parameters showed an age-dependent increase from early adulthood to old age. The effect was most pronounced for global and acinar ventilation heterogeneity and smaller for conductive ventilation heterogeneity. No influence of height, weight and sex was seen. Reference values can now be provided for all important SF6-MBW outcome parameters over the whole age range. TRIAL REGISTRATION NUMBER: NCT04099225.

Less is more-the best test for anastomotic leaks in rectal cancer patients prior to ileostomy reversal.

PURPOSE: No clear consensus exists on how to routinely assess the integrity of the colorectal anastomosis prior to ileostomy reversal. The objective of this study was to evaluate the accuracy of contrast enema, endoscopic procedures, and digital rectal examination in rectal cancer patients in this setting. METHODS: A systematic literature search was performed. Studies assessing at least one index test for which a 2 × 2 table was calculable were included. Hierarchical summary receiver operating characteristic curves were calculated and used for test comparison. Paired data were used where parameters could not be calculated. Methodological quality was assessed with the QUADAS-2 tool. RESULTS: Two prospective and 11 retrospective studies comprising 1903 patients were eligible for inclusion. Paired data analysis showed equal or better results for sensitivity and specificity of both endoscopic procedures and digital rectal examination compared to contrast enema. Subgroup analysis of contrast enema according to methodological quality revealed that studies with higher methodological quality reported poorer sensitivity for equal specificity and vice versa. No case was described where a contrast enema revealed an anastomotic leak that was overseen in digital rectal examination or endoscopic procedures. CONCLUSIONS: Endoscopy and digital rectal examination appear to be the best diagnostic tests to assess the integrity of the colorectal anastomosis prior to ileostomy reversal. Accuracy measures of contrast enema are overestimated by studies with lower methodological quality. Synopsis of existing evidence and risk-benefit considerations justifies omission of contrast enema in favor of endoscopic and clinical assessment. TRIAL REGISTRATION: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42019107771.

Radiation dose of chaperones during common pediatric computed tomography examinations.

BACKGROUND: One main challenge in pediatric imaging is to reduce motion artifacts by calming young patients. To that end, the Radiological Society of North America (RSNA) as early as 1997 stated the necessity of adults accompanying their child during the child's examination. Nonetheless, current research lacks data regarding radiation dose to these chaperones. OBJECTIVE: The aim of this study was to measure the radiation dose of accompanying adults during state-of-the-art pediatric CT protocols. MATERIALS AND METHODS: In addition to a 100-kV non-contrast-enhanced chest CT (Protocol 1), we performed a 70-kV contrast-enhanced chest protocol (Protocol 2) using a third-generation dual-source CT. We acquired data on the radiation dose around the scanner using digital dosimetry placed right at the gantry, 1 m away, as well as beside the gantry. We acquired the CT-surrounding radiation dose during scanning of a pediatric phantom as well as 12 pediatric patients. RESULTS: After conducting 10 consecutive phantom scans using Protocol 1, we found the location with the highest cumulative dose acquired was right next to the gantry opening, at 3 µSv. Protocol 2 showed highest cumulative dose of 2 µSv at the same location. For Protocol 1, the location with the highest radiation doses during pediatric scans was right next to the gantry opening, with doses of 0.75±0.70 µSv. For Protocol 2, the highest radiation was measured 1 m away at 0.50±0.60 µSv. No radiation dose was measured at any time beside the gantry. CONCLUSION: Our results provide proof that chaperones receive low radiation doses during state-of-the-art CT examinations. Given knowledge of these values as well as the optimal spots with the lowest radiation doses, parents as well as patients might be more relaxed during the examination.

Evaluating Treatment Response in GEJ Adenocarcinoma: The Role of Pretherapeutic and Posttherapeutic Iodine Mapping.

BACKGROUND: Neoadjuvant therapy regimens have significantly improved the prognosis of GEJ (gastroesophageal junction) cancer; however, there are a significant percentage of patients who benefit from earlier resection or adapted therapy regimens, and the true response rate can only be determined histopathologically. Methods that allow preoperative assessment of response are lacking. PURPOSE: The purpose of this retrospective study is to assess the potential of pretherapeutic and posttherapeutic spectral CT iodine density (IoD) in predicting histopathological response to neoadjuvant chemotherapy in patients diagnosed with adenocarcinoma of the GEJ. METHODS: In this retrospective cohort study, a total of 62 patients with GEJ carcinoma were studied. Patients received a multiphasic CT scan at diagnosis and preoperatively. Iodine-density maps were generated based on spectral CT data. All tumors were histopathologically analyzed, and the tumor regression grade (TRG) according to Becker et al ( Cancer . 2003;98:1521-1530) was determined. Two experienced radiologists blindly placed 5 defined ROIs in the tumor region of highest density, and the maximum value was used for further analysis. Iodine density was normalized to the aortic iodine uptake. In addition, tumor response was assessed according to standard RECIST measurement. After assessing interrater reliability, the correlation of IoD values with treatment response and with histopathologic TRG was evaluated. RESULTS: The normalized ΔIoD (IoD at diagnosis - IoD after neoadjuvant treatment) and the normalized IoD after neoadjuvant treatment correlated significantly with the TRG. For the detection of responders and nonresponders, the receiver operating characteristic (ROC) curve for normalized ΔIoD yielded the highest area under the curve of 0.95 and achieved a sensitivity and specificity of 92.3% and 92.1%, respectively. Iodine density after neoadjuvant treatment achieved an area under the curve of 0.88 and a sensitivity and specificity of 86.8% and 84.6%, respectively (cutoff, 0.266). Iodine density at diagnosis and RECIST did not provide information to distinguish responders from nonresponders. Using the cutoff value for IoD after neoadjuvant treatment, a reliable classification of responders and nonresponders was achieved for both readers in a test set of 11 patients. Intraclass correlation coefficient revealed excellent interrater reliability (intraclass correlation coefficient, >0.9). Lastly, using the cutoff value for normalized ΔIoD as a definition for treatment response, a significantly longer survival of responders was shown. CONCLUSIONS: Changes in IoD after neoadjuvant treatment of GEJ cancer may be a potential surrogate for therapy response.

Comparison of Virtual Non-Contrast and True Non-Contrast CT Images Obtained by Dual-Layer Spectral CT in COPD Patients.

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death. Recent studies have underlined the importance of non-contrast-enhanced chest CT scans not only for emphysema progression quantification, but for correlation with clinical outcomes as well. As about 40 percent of the 300 million CT scans per year are contrast-enhanced, no proper emphysema quantification is available in a one-stop-shop approach for patients with known or newly diagnosed COPD. Since the introduction of spectral imaging (e.g., dual-energy CT scanners), it has been possible to create virtual non-contrast-enhanced images (VNC) from contrast-enhanced images, making it theoretically possible to offer proper COPD imaging despite contrast enhancing. This study is aimed towards investigating whether these VNC images are comparable to true non-contrast-enhanced images (TNC), thereby reducing the radiation exposure of patients and usage of resources in hospitals. In total, 100 COPD patients with two scans, one with (VNC) and one without contrast media (TNC), within 8 weeks or less obtained by a spectral CT using dual-layer technology, were included in this retrospective study. TNC and VNC were compared according to their voxel-density histograms. While the comparison showed significant differences in the low attenuated volumes (LAVs) of TNC and VNC regarding the emphysema threshold of -950 Houndsfield Units (HU), the 15th and 10th percentiles of the LAVs used as a proxy for pre-emphysema were comparable. Upon further investigation, the threshold-based LAVs (-950 HU) of TNC and VNC were comparable in patients with a water equivalent diameter (DW) below 270 mm. The study concludes that VNC imaging may be a viable option for assessing emphysema progression in COPD patients, particularly those with a normal body mass index (BMI). Further, pre-emphysema was generally comparable between TNC and VNC. This approach could potentially reduce radiation exposure and hospital resources by making additional TNC scans obsolete.

Machine learning assisted feature identification and prediction of hemodynamic endpoints using computed tomography in patients with CTEPH.

Chronic thromboembolic pulmonary hypertension (CTEPH) is a rare but potentially curable cause of pulmonary hypertension (PH). Currently PH is diagnosed by right heart catheterisation. Computed tomography (CT) is used for ruling out other causes and operative planning. This study aims to evaluate importance of different quantitative/qualitative imaging features and develop a supervised machine learning (ML) model to predict hemodynamic risk groups. 127 Patients with diagnosed CTEPH who received preoperative right heart catheterization and thoracic CTA examinations (39 ECG-gated; 88 non-ECG gated) were included. 19 qualitative/quantitative imaging features and 3 hemodynamic parameters [mean pulmonary artery pressure, right atrial pressure (RAP), pulmonary artery oxygen saturation (PA SaO2)] were gathered. Diameter-based CT features were measured in axial and adjusted multiplane reconstructions (MPR). Univariate analysis was performed for qualitative and quantitative features. A random forest algorithm was trained on imaging features to predict hemodynamic risk groups. Feature importance was calculated for all models. Qualitative and quantitative parameters showed no significant differences between ECG and non-ECG gated CTs. Depending on reconstruction plane, five quantitative features were significantly different, but mean absolute difference between parameters (MPR vs. axial) was 0.3 mm with no difference in correlation with hemodynamic parameters. Univariate analysis showed moderate to strong correlation for multiple imaging features with hemodynamic parameters. The model achieved an AUC score of 0.82 for the mPAP based risk stratification and 0.74 for the PA SaO2 risk stratification. Contrast agent retention in hepatic vein, mosaic attenuation pattern and the ratio right atrium/left ventricle were the most important features among other parameters. Quantitative and qualitative imaging features of reconstructions correlate with hemodynamic parameters in preoperative CTEPH patients-regardless of MPR adaption. Machine learning based analysis of preoperative imaging features can be used for non-invasive risk stratification. Qualitative features seem to be more important than previously anticipated.

Development and Validation of a Deep Learning Algorithm to Differentiate Colon Carcinoma From Acute Diverticulitis in Computed Tomography Images.

Importance: Differentiating between malignant and benign etiology in large-bowel wall thickening on computed tomography (CT) images can be a challenging task. Artificial intelligence (AI) support systems can improve the diagnostic accuracy of radiologists, as shown for a variety of imaging tasks. Improvements in diagnostic performance, in particular the reduction of false-negative findings, may be useful in patient care. Objective: To develop and evaluate a deep learning algorithm able to differentiate colon carcinoma (CC) and acute diverticulitis (AD) on CT images and analyze the impact of the AI-support system in a reader study. Design, Setting, and Participants: In this diagnostic study, patients who underwent surgery between July 1, 2005, and October 1, 2020, for CC or AD were included. Three-dimensional (3-D) bounding boxes including the diseased bowel segment and surrounding mesentery were manually delineated and used to develop a 3-D convolutional neural network (CNN). A reader study with 10 observers of different experience levels was conducted. Readers were asked to classify the testing cohort under reading room conditions, first without and then with algorithmic support. Main Outcomes and Measures: To evaluate the diagnostic performance, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for all readers and reader groups with and without AI support. Metrics were compared using the McNemar test and relative and absolute predictive value comparisons. Results: A total of 585 patients (AD: n = 267, CC: n = 318; mean [SD] age, 63.2 [13.4] years; 341 men [58.3%]) were included. The 3-D CNN reached a sensitivity of 83.3% (95% CI, 70.0%-96.6%) and specificity of 86.6% (95% CI, 74.5%-98.8%) for the test set, compared with the mean reader sensitivity of 77.6% (95% CI, 72.9%-82.3%) and specificity of 81.6% (95% CI, 77.2%-86.1%). The combined group of readers improved significantly with AI support from a sensitivity of 77.6% to 85.6% (95% CI, 81.3%-89.3%; P < .001) and a specificity of 81.6% to 91.3% (95% CI, 88.1%-94.5%; P < .001). Artificial intelligence support significantly reduced the number of false-negative and false-positive findings (NPV from 78.5% to 86.4% and PPV from 80.9% to 90.8%; P < .001). Conclusions and Relevance: The findings of this study suggest that a deep learning model able to distinguish CC and AD in CT images as a support system may significantly improve the diagnostic performance of radiologists, which may improve patient care.

CT Attenuation of Hepatic Pancreatic Cancer Metastases Correlates with Prognostically Detrimental Metastatic Necrosis.

Percutaneous CT-guided biopsy is a frequently performed procedure for the confirmation and molecular workup of hepatic metastases of pancreatic ductal adenocarcinoma (PDAC). Tumor necrosis of primary PDAC has shown a negative prognostic impact in recent studies. This study aims to examine predictability in CT scans and the prognostic impact of necrosis in hepatic metastases of PDAC. In this tertiary-center retrospective cohort study, we included 36 patients with hepatic metastases of PDAC who underwent CT-guided hepatic biopsies. Normalized attenuation of the biopsied metastasis was determined in venous phase contrast-enhanced planning scans obtained prior to biopsy by automatic, threshold-based 3D segmentation and manual, blinded 2D segmentation. A board-certified pathologist specialized in hepatic pathology histologically quantified the tumor necrosis and cellularity of the biopsy cylinders. We found a significant inverse-linear correlation between normalized attenuation and the fraction of necrosis (Pearson's r = 0.51, p < 0.001 for automatic 3D segmentation or Pearson's r = 0.52, p < 0.001 for manual 2D segmentation), whereas no correlation was found with tumor cellularity. Additionally, we discovered that patients with a fraction of necrosis ≥ 20% in metastases had a significantly shorter overall survival (p < 0.035). In summary, tumor necrosis of PDAC metastases can be estimated from contrast-enhanced CT scans, which could help to improve biopsy sample pattern planning. In addition, liver metastatic necrosis may serve as a prognostic biomarker in PDAC.

Cost-Effectiveness of Artificial Intelligence Support in Computed Tomography-Based Lung Cancer Screening.

BACKGROUND: Lung cancer screening is already implemented in the USA and strongly recommended by European Radiological and Thoracic societies as well. Upon implementation, the total number of thoracic computed tomographies (CT) is likely to rise significantly. As shown in previous studies, modern artificial intelligence-based algorithms are on-par or even exceed radiologist's performance in lung nodule detection and classification. Therefore, the aim of this study was to evaluate the cost-effectiveness of an AI-based system in the context of baseline lung cancer screening. METHODS: In this retrospective study, a decision model based on Markov simulation was developed to estimate the quality-adjusted life-years (QALYs) and lifetime costs of the diagnostic modalities. Literature research was performed to determine model input parameters. Model uncertainty and possible costs of the AI-system were assessed using deterministic and probabilistic sensitivity analysis. RESULTS: In the base case scenario CT + AI resulted in a negative incremental cost-effectiveness ratio (ICER) as compared to CT only, showing lower costs and higher effectiveness. Threshold analysis showed that the ICER remained negative up to a threshold of USD 68 for the AI support. The willingness-to-pay of USD 100,000 was crossed at a value of USD 1240. Deterministic and probabilistic sensitivity analysis showed model robustness for varying input parameters. CONCLUSION: Based on our results, the use of an AI-based system in the initial low-dose CT scan of lung cancer screening is a feasible diagnostic strategy from a cost-effectiveness perspective.

Feature Robustness and Diagnostic Capabilities of Convolutional Neural Networks Against Radiomics Features in Computed Tomography Imaging.

MATERIALS AND METHODS: Imaging phantoms were scanned twice on 3 computed tomography scanners from 2 different manufactures with varying tube voltages and currents. Phantoms were segmented, and features were extracted using PyRadiomics and a pretrained CNN. After standardization the concordance correlation coefficient (CCC), mean feature variance, feature range, and the coefficient of variant were calculated to assess feature robustness. In addition, the cosine similarity was calculated for the vectorized activation maps for an exemplary phantom. For the in vivo comparison, the radiomics and CNN features of 30 patients with hepatocellular carcinoma (HCC) and 30 patients with hepatic colon carcinoma metastasis were compared. RESULTS: In total, 851 radiomics features and 256 CNN features were extracted for each phantom. For all phantoms, the global CCC of the CNN features was above 98%, whereas the highest CCC for the radiomics features was 36%. The mean feature variance and feature range was significantly lower for the CNN features. Using a coefficient of variant ≤0.2 as a threshold to define robust features and averaging across all phantoms 346 of 851 (41%) radiomics features and 196 of 256 (77%) CNN features were found to be robust. The cosine similarity was greater than 0.98 for all scanner and parameter variations. In the retrospective analysis, 122 of the 256 CNN (49%) features showed significant differences between HCC and hepatic colon metastasis. DISCUSSION: Convolutional neural network features were more stable compared with radiomics features against technical variations. Moreover, the possibility of tumor entity differentiation based on CNN features was shown. Combined with visualization methods, CNN features are expected to increase reproducibility of quantitative image representations. Further studies are warranted to investigate the impact of feature stability on radiological image-based prediction of clinical outcomes.

High-Resolution, High b-Value Computed Diffusion-Weighted Imaging Improves Detection of Pancreatic Ductal Adenocarcinoma.

BACKGROUND: Our purpose was to investigate the potential of high-resolution, high b-value computed DWI (cDWI) in pancreatic ductal adenocarcinoma (PDAC) detection. MATERIALS AND METHODS: We retrospectively enrolled 44 patients with confirmed PDAC. Respiratory-triggered, diffusion-weighted, single-shot echo-planar imaging (ss-EPI) with both conventional (i.e., full field-of-view, 3 × 3 × 4 mm voxel size, b = 0, 50, 300, 600 s/mm2) and high-resolution (i.e., reduced field-of-view, 2.5 × 2.5 × 3 mm voxel size, b = 0, 50, 300, 600, 1000 s/mm2) imaging was performed for suspected PDAC. cDWI datasets at b = 1000 s/mm2 were generated for the conventional and high-resolution datasets. Three radiologists were asked to subjectively rate (on a Likert scale of 1-4) the following metrics: image quality, lesion detection and delineation, and lesion-to-pancreas intensity relation. Furthermore, the following quantitative image parameters were assessed: apparent signal-to-noise ratio (aSNR), contrast-to-noise ratio (aCNR), and lesion-to-pancreas contrast ratio (CR). RESULTS: High-resolution, high b-value computed DWI (r-cDWI1000) enabled significant improvement in lesion detection and a higher incidence of a high lesion-to-pancreas intensity relation (type 1, clear hyperintense) compared to conventional high b-value computed and high-resolution high b-value acquired DWI (f-cDWI1000 and r-aDWI1000, respectively). Image quality was rated inferior in the r-cDWI1000 datasets compared to r-aDWI1000. Furthermore, the aCNR and CR were higher in the r-cDWI1000 datasets than in f-cDWI1000 and r-aDWI1000. CONCLUSION: High-resolution, high b-value computed DWI provides significantly better visualization of PDAC compared to the conventional high b-value computed and high-resolution high b-value images acquired by DWI.

Time is bone - Quantitative comparison of decalcification solvents in human femur samples using dual-X-ray-absorptiometry and computed tomography.

INTRODUCTION: Bone decalcification is a necessary preprocessing step in histological and anatomical studies. Several solutions for decalcification with different claimed times for full decalcification are commercially available. Current literature lacks direct, quantitative measurement of calcium hydrocyapatite degradation during decalcification to compare different solutions. Therefore, the aim of this study was to test the performance of three different decalcification solutions in human bone by direct measurement of calcium hydroxyapatite using dual-X-ray-absorptiometry (DEXA) and volumetric computed tomography (CT). METHODS: Four femur slices were acquired from the proximal femur of a 76-year-old body donor. The slices were submerged in formaldehyde (control), EDTA, Osteosoft (Merck, Darmstadt, Germany) and "Rapid Bone Decalcifier" (RBD) (American MasterTech Scientific, Lodi, USA). Consecutive DEXA and CT scans were performed at 2 h, 4 h, 8 h, 11 h, 20 h, 44 h and 77 h after solutions were added. Besides the calcium hydroxyapatite concentration, the bone volume was measured each time. RESULTS: Fastest decline in volume was seen in the RBD probe. Further, RBD was the only solution, being able to fully decalcify the bone slice after 77 h. Although a steady decline in volume and hydroxyapatite concentration was seen for EDTA and Osteosoft as well, both were not able to decalcify the slices. CONCLUSION: Overall, the purely qualititve acquired literature data on bone decalcifiers was verified by our quantitative data for human, cortical-rich bones. Hydrochloric-acid based solutions seem to be preferable in order to rapidly dissolve the calcium hydroxyapatite.

Beyond the d-dimer - Machine-learning assisted pre-test probability evaluation in patients with suspected pulmonary embolism and elevated d-dimers.

INTRODUCTION: Acute pulmonary embolism (PE) is a leading cardiovascular cause of death, resembling a common indication for emergency computed tomography (CT). Nonetheless, in clinical routine most CTs performed for suspicion of PE excluded the suspected diagnosis. As patients with low to intermediate risk for PE are triaged according to the d-dimer, its relatively low specifity and widespread elevation among elderly might be an underlying issue. Aim of this study was to find potential predictors based on initial emergency blood tests in patients with elevated d-dimers and suspected PE to further increase pre-test probability. METHODS: In this retrospective study all patients at the local university hospital's emergency room from 2009 to 2019 with suspected PE, emergency blood testing and CT were included. Cluster analysis was performed to separate groups with distinct laboratory parameter profiles and PE frequencies were compared. Machine learning algorithms were trained on the groups to predict individual PE probability based on emergency laboratory parameters. RESULTS: Overall, PE frequency among the 2045 analyzed patients was 41%. Three clusters with significant differences (p ≤ 0.05) in PE frequency were identified: C1 showed a PE frequency of 43%, C2 40% and C3 33%. Laboratory parameter profiles (e.g. creatinine) differed significantly between clusters (p ≤ 0.0001). Both logistic regression and support-vector machines were able to predict clusters with an accuracy of over 90%. DISCUSSION: Initial blood parameters seem to enable further differentiation of patients with suspected PE and elevated d-dimers to raise pre-test probability of PE. Machine-learning-based prediction models might help to further narrow down CT indications in the future.

Individualized Determination of the Mechanical Fracture Environment After Tibial Exchange Nailing-A Simulation-Based Feasibility Study.

Non-union rate after tibial fractures remains high. Apart from largely uncontrollable biologic, injury, and patient-specific factors, the mechanical fracture environment is a key determinant of healing. Our aim was to establish a patient-specific simulation workflow to determine the mechanical fracture environment and allow for an estimation of its healing potential. In a referred patient with failed nail-osteosynthesis after tibial-shaft fracture exchange nailing was performed. Post-operative CT-scans were used to construct a three-dimensional model of the treatment situation in an image processing and computer-aided design system. Resulting forces, computed in a simulation-driven workflow based on patient monitoring and motion capturing were used to simulate the mechanical fracture environment before and after exchange nailing. Implant stresses for the initial and revision situation, as well as interfragmentary movement, resulting hydrostatic, and octahedral shear strain were calculated and compared to the clinical course. The simulation model was able to adequately predict hardware stresses in the initial situation where mechanical implant failure occurred. Furthermore, hydrostatic and octahedral shear strain of the revision situation were calculated to be within published healing boundaries-accordingly the fracture healed uneventfully. Our workflow is able to determine the mechanical environment of a fracture fixation, calculate implant stresses, interfragmentary movement, and the resulting strain. Critical mechanical boundary conditions for fracture healing can be determined in relation to individual loading parameters. Based on this individualized treatment recommendations during the early post-operative phase in lower leg fractures are possible in order to prevent implant failure and non-union development.

Incomplete neo-endothelialization of left atrial appendage closure devices is frequent after 6 months: a pilot imaging study.

PURPOSE: To bridge neo-endothelialization (NE) of implanted left atrial appendage closure (LAA/LAAC) devices, dual antiplatelet therapy is prescribed. Cardiac computed tomography angiography (cCTA) has been proposed for the evaluation of interventional LAAC. This prospective longitudinal observational study applied a standardized imaging protocol to detect progression of NE of LAAC devices 6 months after implantation. METHODS: Consecutive cCTA datasets of patients six months after LAAC were acquired and the standardized multi-planar reconstruction LAA occluder view for post-implantation evaluation (LOVE) algorithm was used. Residual flow of contrast agent inside the LAA without a peri-device leak (PDL) was defined as incomplete neo-endothelialization. Absence of residual flow was defined as complete neo-endothelialization. Since PDL allows residual flow in the LAA, irrespective of neoendothelialization, PDL were excluded from this study. Diabetes mellitus, liver disease, body-mass-index, age, device sizes and type will be assessed as predictors for incomplete NE. RESULTS: 53 consecutive patients were recruited for cCTA imaging. 36 (68%) showed no PDL and were included in the study (median age 77 years, 19% female). At median follow-up of 6 months (median 180 days, IQR 178-180), 44% of patients showed complete NE compared to 56% with NE still incomplete. Age, BMI, device type and size as well as prevalence of diabetes mellitus and liver disease did not show significant correlation with the completeness of NE. CONCLUSION: This pilot study showed that neo-endothelialization is still incomplete in a majority of patients at mid-term follow-up of 6 months after successful LAAC therapy. Further investigation on the consequences of incomplete endothelialization is needed to guide antiplatelet therapy schedules.

Lung nodule detection in chest X-rays using synthetic ground-truth data comparing CNN-based diagnosis to human performance.

We present a method to generate synthetic thorax radiographs with realistic nodules from CT scans, and a perfect ground truth knowledge. We evaluated the detection performance of nine radiologists and two convolutional neural networks in a reader study. Nodules were artificially inserted into the lung of a CT volume and synthetic radiographs were obtained by forward-projecting the volume. Hence, our framework allowed for a detailed evaluation of CAD systems' and radiologists' performance due to the availability of accurate ground-truth labels for nodules from synthetic data. Radiographs for network training (U-Net and RetinaNet) were generated from 855 CT scans of a public dataset. For the reader study, 201 radiographs were generated from 21 nodule-free CT scans with altering nodule positions, sizes and nodule counts of inserted nodules. Average true positive detections by nine radiologists were 248.8 nodules, 51.7 false positive predicted nodules and 121.2 false negative predicted nodules. The best performing CAD system achieved 268 true positives, 66 false positives and 102 false negatives. Corresponding weighted alternative free response operating characteristic figure-of-merits (wAFROC FOM) for the radiologists range from 0.54 to 0.87 compared to a value of 0.81 (CI 0.75-0.87) for the best performing CNN. The CNN did not perform significantly better against the combined average of the 9 readers (p = 0.49). Paramediastinal nodules accounted for most false positive and false negative detections by readers, which can be explained by the presence of more tissue in this area.

COPD Imaging on a 3rd Generation Dual-Source CT: Acquisition of Paired Inspiratory-Expiratory Chest Scans at an Overall Reduced Radiation Risk.

As stated by the Fleischner Society, an additional computed tomography (CT) scan in expiration is beneficial in patients with chronic obstructive pulmonary disease (COPD). It was thus the aim of this study to evaluate the radiation risk of a state-of-the-art paired inspiratory-expiratory chest scan compared to inspiration-only examinations. Radiation doses to 28 organs were determined for 824 COPD patients undergoing routine chest examinations at three different CT systems-a conventional multi-slice CT (MSCT), a 2nd generation (2nd-DSCT), and 3rd generation dual-source CT (3rd-DSCT). Patients examined at the 3rd-DSCT received a paired inspiratory-expiratory scan. Organ doses, effective doses, and lifetime attributable cancer risks (LAR) were calculated. All organ and effective doses were significantly lower for the paired inspiratory-expiratory protocol (effective doses: 4.3 ± 1.5 mSv (MSCT), 3.0 ± 1.2 mSv (2nd-DSCT), and 2.0 ± 0.8 mSv (3rd-DSCT)). Accordingly, LAR was lowest for the paired protocol with an estimate of 0.025 % and 0.013% for female and male patients (50 years) respectively. Image quality was not compromised. Paired inspiratory-expiratory scans can be acquired on 3rd-DSCT systems at substantially lower dose and risk levels when compared to inspiration-only scans at conventional CT systems, offering promising prospects for improved COPD diagnosis.

From infancy to adulthood-Developmental changes in pulmonary quantitative computed tomography parameters.

PURPOSE: Quantified computed tomography (qCT) is known for correlations with airflow obstruction and fibrotic changes of the lung. However, as qCT studies often focus on diseased and elderly subjects, current literature lacks physiological qCT values during body development. We evaluated chest CT examinations of a healthy cohort, reaching from infancy to adulthood, to determine physiological qCT values and changes during body development. METHOD: Dose-optimized chest CT examinations performed over the last 3 years using a dual-source CT were retrospectively analysed. Exclusion criteria were age >30 years and any known or newly diagnosed lung pathology. Lung volume, mean lung density, full-width-at-half-maximum and low attenuated volume (LAV) were semi-automated quantified in 151 patients. qCT values between different age groups as well as unenhanced (Group 1) and contrast-enhanced (Group 2) protocols were compared. Models for projection of age-dependant changes in qCT values were fitted. RESULTS: Significant differences in qCT parameters were found between the age groups from 0 to 15 years (p < 0.05). All parameters except LAV merge into a plateau level above this age as shown by polynomial models (r2 between 0.85 and 0.67). In group 2, this plateau phase is shifted back around five years. Except for the volume, significant differences in all qCT values were found between group 1 and 2 (p < 0.01). CONCLUSION: qCT parameters underly a specific age-dependant dynamic. Except for LAV, qCT parameters reach a plateau around adolescence. Contrast-enhanced protocols seem to shift this plateau backwards.