Indoor Air Pollution and Impaired Cardiac Autonomic Function in Chronic Obstructive Pulmonary Disease.

Rationale: Indoor air pollution represents a modifiable risk factor for respiratory morbidity in chronic obstructive pulmonary disease (COPD). The effects of indoor air pollution, as well as the impact of interventions to improve indoor air quality, on cardiovascular morbidity in COPD remain unknown. Objectives: To determine the association between indoor particulate matter (PM) and heart rate variability (HRV), a measure of cardiac autonomic function tied to cardiovascular morbidity and mortality, as well as the impact of household air purifiers on HRV. Methods: Former smokers with moderate-severe COPD were recruited from a 6-month randomized controlled trial of a portable air cleaner intervention to undergo paired assessment of both in-home PM and HRV using 24-hour Holter monitoring at up to five time points. Primary outcomes were HRV measures tied to cardiovascular morbidity (standard deviation of normal-to-normal intervals [SDNN] and root mean square of successive differences between normal-to-normal intervals [RMSSD]). Measurements and Results: Eighty-five participants contributed 317 HRV measurements. A twofold increase in household PM ⩽2.5 µm in aerodynamic diameter was associated with decreases in SDNN (ß, -2.98% [95% confidence interval (CI), -5.12 to -0.78]) and RMSSD (ß, -4.57% [95% CI, -10.1 to -1.60]). The greatest effects were observed with ultrafine particles (<100 nm) (RMSSD; ß, -16.4% [95% CI, -22.3 to -10.1]) and among obese participants. Participants randomized to the active air cleaner saw improvements in RMSSD (ß, 25.2% [95% CI, 2.99 to 52.1]), but not SDNN (ß, 2.65% [95% CI, -10.8 to 18.1]), compared with the placebo group. Conclusions: This is the first U.S. study to describe the association between household PM and cardiac autonomic function among individuals with COPD, as well as the potential cardiovascular health benefits of household air cleaners.

A Multicenter, Single-Arm, Prospective Trial Assessing the Diagnostic Yield of Electromagnetic Bronchoscopic and Transthoracic Navigation for Peripheral Pulmonary Nodules.

Rationale: Strict adherence to procedural protocols and diagnostic definitions is critical to understand the efficacy of new technologies. Electromagnetic navigational bronchoscopy (ENB) for lung nodule biopsy has been used for decades without a solid understanding of its efficacy, but offers the opportunity for simultaneous tissue acquisition via electromagnetic navigational transthoracic biopsy (EMN-TTNA) and staging via endobronchial ultrasound (EBUS). Objective: To evaluate the diagnostic yield of EBUS, ENB, and EMN-TTNA during a single procedure using a strict a priori definition of diagnostic yield with central pathology adjudication. Methods: A prospective, single-arm trial was conducted at eight centers enrolling participants with pulmonary nodules (<3 cm; without computed tomography [CT]- and/or positron emission tomography-positive mediastinal lymph nodes) who underwent a staged procedure with same-day CT, EBUS, ENB, and EMN-TTNA. The procedure was staged such that, when a diagnosis had been achieved via rapid on-site pathologic evaluation, the procedure was ended and subsequent biopsy modalities were not attempted. A study finding was diagnostic if an independent pathology core laboratory confirmed malignancy or a definitive benign finding. The primary endpoint was the diagnostic yield of the combination of CT, EBUS, ENB, and EMN-TTNA. Measurements and Main Results: A total of 160 participants at 8 centers with a mean nodule size of 18 ± 6 mm were enrolled. The diagnostic yield of the combined procedure was 59% (94 of 160; 95% confidence interval [CI], 51-66%). Nodule regression was found on same-day CT in 2.5% of cases (4 of 160; 95% CI, 0.69-6.3%), and EBUS confirmed malignancy in 7.1% of cases (11 of 156; 95% CI, 3.6-12%). The yield of ENB alone was 49% (74 of 150; 95% CI, 41-58%), that of EMN-TTNA alone was 27% (8 of 30; 95% CI, 12-46%), and that of ENB plus EMN-TTNA was 53% (79 of 150; 95% CI, 44-61%). Complications included a pneumothorax rate of 10% and a 2% bleeding rate. When EMN-TTNA was performed, the pneumothorax rate was 30%. Conclusions: The diagnostic yield for ENB is 49%, which increases to 59% with the addition of same-day CT, EBUS, and EMN-TTNA, lower than in prior reports in the literature. The high complication rate and low diagnostic yield of EMN-TTNA does not support its routine use. Clinical trial registered with www.clinicaltrials.gov (NCT03338049).

Anti-MDA5-positive dermatomyositis and remission in a single referral centre population.

OBJECTIVES: To describe a single-centre North American adult cohort of anti-MDA5-positive dermatomyositis patients, with emphasis on drug-free long-term remission. METHODS: We conducted an observational retrospective cohort study of anti-MDA5-positive DM patients. All consented patients seen in the Johns Hopkins Myositis Centre from 2003-2020 with suspected muscle disease were routinely screened for myositis-specific autoantibodies. All sera were screened for anti-MDA5 autoantibodies by line blot; positives were verified by enzyme-linked immunoassay. Patients whose sera were anti-MDA5 positive by both assays (n=52) were followed longitudinally. If clinical status was unavailable, structured telephone interviews were conducted. Clinical remission was defined as being off all immunosuppression >1 year while remaining asymptomatic. RESULTS: 38/52 (73%) of the patients were women with a median age at disease-onset of 47 (IQR 40-54). Twenty-five of the patients (48%) were White, 16 (30%) were Black and 3 (6%) were Asian. Most patients (42/52, 80%) had interstitial lung disease, defined by inflammatory or fibrotic changes on high resolution computed tomography (HRCT). 18/52 (35%) of patients required pulse-dose methylprednisolone, 4/52 (8%) experienced spontaneous pneumothorax/pneumomediastinum, 6/52 (12%) required intubation, and 5/52 (10%) died. Over longitudinal follow-up (median 3.5 years), 9 (18%) patients achieved clinical remission. The median time from symptom onset to clinical remission was 4 years, and the median duration of sustained remission was 3.5 years (range 1.4-7.8). No demographic or disease characteristics were significantly associated with remission. CONCLUSIONS: In this single centre, tertiary referral population of anti-MDA5-positive dermatomyositis, ~20% of patients experienced long-term drug-free remission after a median disease duration of 4 years. No clinical or biologic factors were associated with clinical remission.

COVID-19 mortality prediction in the intensive care unit with deep learning based on longitudinal chest X-rays and clinical data.

OBJECTIVES: We aimed to develop deep learning models using longitudinal chest X-rays (CXRs) and clinical data to predict in-hospital mortality of COVID-19 patients in the intensive care unit (ICU). METHODS: Six hundred fifty-four patients (212 deceased, 442 alive, 5645 total CXRs) were identified across two institutions. Imaging and clinical data from one institution were used to train five longitudinal transformer-based networks applying five-fold cross-validation. The models were tested on data from the other institution, and pairwise comparisons were used to determine the best-performing models. RESULTS: A higher proportion of deceased patients had elevated white blood cell count, decreased absolute lymphocyte count, elevated creatine concentration, and incidence of cardiovascular and chronic kidney disease. A model based on pre-ICU CXRs achieved an AUC of 0.632 and an accuracy of 0.593, and a model based on ICU CXRs achieved an AUC of 0.697 and an accuracy of 0.657. A model based on all longitudinal CXRs (both pre-ICU and ICU) achieved an AUC of 0.702 and an accuracy of 0.694. A model based on clinical data alone achieved an AUC of 0.653 and an accuracy of 0.657. The addition of longitudinal imaging to clinical data in a combined model significantly improved performance, reaching an AUC of 0.727 (p = 0.039) and an accuracy of 0.732. CONCLUSIONS: The addition of longitudinal CXRs to clinical data significantly improves mortality prediction with deep learning for COVID-19 patients in the ICU. KEY POINTS: • Deep learning was used to predict mortality in COVID-19 ICU patients. • Serial radiographs and clinical data were used. • The models could inform clinical decision-making and resource allocation.

Deep Learning Algorithms for Interpretation of Upper Extremity Radiographs: Laterality and Technologist Initial Labels as Confounding Factors.

Convolutional neural networks (CNNs) trained to identify abnormalities on upper extremity radiographs achieved an AUC of 0.844 with a frequent emphasis on radiograph laterality and/or technologist labels for decision-making. Covering the labels increased the AUC to 0.857 (p = .02) and redirected CNN attention from the labels to the bones. Using images of radiograph labels alone, the AUC was 0.638, indicating that radiograph labels are associated with abnormal examinations. Potential radiographic confounding features should be considered when curating data for radiology CNN development.

Deep learning prediction of sex on chest radiographs: a potential contributor to biased algorithms.

BACKGROUND: Deep convolutional neural networks (DCNNs) for diagnosis of disease on chest radiographs (CXR) have been shown to be biased against males or females if the datasets used to train them have unbalanced sex representation. Prior work has suggested that DCNNs can predict sex on CXR, which could aid forensic evaluations, but also be a source of bias. OBJECTIVE: To (1) evaluate the performance of DCNNs for predicting sex across different datasets and architectures and (2) evaluate visual biomarkers used by DCNNs to predict sex on CXRs. MATERIALS AND METHODS: Chest radiographs were obtained from the Stanford CheXPert and NIH Chest XRay14 datasets which comprised of 224,316 and 112,120 CXRs, respectively. To control for dataset size and class imbalance, random undersampling was used to reduce each dataset to 97,560 images that were balanced for sex. Each dataset was randomly split into training (70%), validation (10%), and test (20%) sets. Four DCNN architectures pre-trained on ImageNet were used for transfer learning. DCNNs were externally validated using a test set from the opposing dataset. Performance was evaluated using area under the receiver operating characteristic curve (AUC). Class activation mapping (CAM) was used to generate heatmaps visualizing the regions contributing to the DCNN's prediction. RESULTS: On the internal test set, DCNNs achieved AUROCs ranging from 0.98 to 0.99. On external validation, the models reached peak cross-dataset performance of 0.94 for the VGG19-Stanford model and 0.95 for the InceptionV3-NIH model. Heatmaps highlighted similar regions of attention between model architectures and datasets, localizing to the mediastinal and upper rib regions, as well as to the lower chest/diaphragmatic regions. CONCLUSION: DCNNs trained on two large CXR datasets accurately predicted sex on internal and external test data with similar heatmap localizations across DCNN architectures and datasets. These findings support the notion that DCNNs can leverage imaging biomarkers to predict sex and potentially confound the accurate prediction of disease on CXRs and contribute to biased models. On the other hand, these DCNNs can be beneficial to emergency radiologists for forensic evaluations and identifying patient sex for patients whose identities are unknown, such as in acute trauma.

Rapidly progressive interstitial lung disease in patients with anti-melanoma differentiation-associated gene 5-positive dermatomyositis: serial changes on HRCT.

BACKGROUND: Anti-melanoma differentiation-associated gene 5 (anti-MDA5) antibodies in patients with dermatomyositis are associated with rapidly progressive interstitial lung disease (RP-ILD). Computed tomography (CT) plays a central role in the diagnosis of RP-ILD and may help characterize the temporal changes. METHODS: We report five anti-MDA5-positive dermatomyositis patients with serial CT scans spanning their acute RP-ILD disease course. RESULTS: Our case series highlights the variable imaging pattern that can manifest in this setting, including diffuse alveolar damage and nonspecific interstitial pneumonia patterns. Three patients in our series died within 4 months of their disease onset, whereas the other two patients survived. CONCLUSION: The serial CT changes in anti-MDA5 disease are dynamic and variable; therefore, it is imperative to maintain a broad differential when faced with these HRCT patterns to improve the diagnosis and management of this underrecognized entity.

Radiation Versus Immune Checkpoint Inhibitor Associated Pneumonitis: Distinct Radiologic Morphologies.

BACKGROUND: Patients with non-small cell lung cancer may develop pneumonitis after thoracic radiotherapy (RT) and immune checkpoint inhibitors (ICIs). We hypothesized that distinct morphologic features are associated with different pneumonitis etiologies. MATERIALS AND METHODS: We systematically compared computed tomography (CT) features of RT- versus ICI-pneumonitis. Clinical and imaging features were tested for association with pneumonitis severity. Lastly, we constructed an exploratory radiomics-based machine learning (ML) model to discern pneumonitis etiology. RESULTS: Between 2009 and 2019, 82 patients developed pneumonitis: 29 after thoracic RT, 23 after ICI, and 30 after RT + ICI. Fifty patients had grade 2 pneumonitis, 22 grade 3, and 7 grade 4. ICI-pneumonitis was more likely bilateral (65% vs. 28%; p = .01) and involved more lobes (66% vs. 45% involving at least three lobes) and was less likely to have sharp border (17% vs. 59%; p = .004) compared with RT-pneumonitis. Pneumonitis morphology after RT + ICI was heterogeneous, with 47% bilateral, 37% involving at least three lobes, and 40% sharp borders. Among all patients, risk factors for severe pneumonitis included poor performance status, smoking history, worse lung function, and bilateral and multifocal involvement on CT. An ML model based on seven radiomic features alone could distinguish ICI- from RT-pneumonitis with an area under the receiver-operating curve of 0.76 and identified the predominant etiology after RT + ICI concordant with multidisciplinary consensus. CONCLUSION: RT- and ICI-pneumonitis exhibit distinct spatial features on CT. Bilateral and multifocal lung involvement is associated with severe pneumonitis. Integrating these morphologic features in the clinical management of patients who develop pneumonitis after RT and ICIs may improve treatment decision-making. IMPLICATIONS FOR PRACTICE: Patients with non-small cell lung cancer often receive thoracic radiation and immune checkpoint inhibitors (ICIs), both of which can cause pneumonitis. This study identified similarities and differences in pneumonitis morphology on computed tomography (CT) scans among pneumonitis due to radiotherapy (RT) alone, ICI alone, and the combination of both. Patients who have bilateral CT changes involving at least three lobes are more likely to have ICI-pneumonitis, whereas those with unilateral CT changes with sharp borders are more likely to have radiation pneumonitis. After RT and/or ICI, severe pneumonitis is associated with bilateral and multifocal CT changes. These results can help guide clinicians in triaging patients who develop pneumonitis after radiation and during ICI treatment.

Cinematic rendering enhancements to virtual bronchoscopy: assessment of emergent tracheal pathology.

Utilizing complex lighting models, cinematic rendering is a novel technique for demonstrating computed tomography data with exquisite 3D anatomic detail. The tracheal lumen, tracheal wall, and adjacent soft tissue structures are represented with photorealistic detail exceeding that of conventional volume rendering or virtual bronchoscopy techniques. We applied cinematic rendering to a spectrum of emergent tracheal pathologies: traumatic tracheal tears, tracheoesophageal fistulas, tracheal foreign bodies, tracheal stenosis (intrinsic and extrinsic causes), tracheal neoplasms, and tracheomalacia. Cinematic rendering images enable visually accessible evaluation and comprehensive understanding of acute tracheal pathology, which is likely to be of value to both interventional pulmonologists and thoracic surgeons who are determining patient treatment plans.

Radiology "forensics": determination of age and sex from chest radiographs using deep learning.

PURPOSE: To develop and test the performance of deep convolutional neural networks (DCNNs) for automated classification of age and sex on chest radiographs (CXR). METHODS: We obtained 112,120 frontal CXRs from the NIH ChestX-ray14 database performed in 48,780 females (44%) and 63,340 males (56%) ranging from 1 to 95 years old. The dataset was split into training (70%), validation (10%), and test (20%) datasets, and used to fine-tune ResNet-18 DCNNs pretrained on ImageNet for (1) determination of sex (using entire dataset and only pediatric CXRs); (2) determination of age < 18 years old or ≥ 18 years old (using entire dataset); and (3) determination of age < 11 years old or 11-18 years old (using only pediatric CXRs). External testing was performed on 662 CXRs from China. Area under the receiver operating characteristic curve (AUC) was used to evaluate DCNN test performance. RESULTS: DCNNs trained to determine sex on the entire dataset and pediatric CXRs only had AUCs of 1.0 and 0.91, respectively (p < 0.0001). DCNNs trained to determine age < or ≥ 18 years old and < 11 vs. 11-18 years old had AUCs of 0.99 and 0.96 (p < 0.0001), respectively. External testing showed AUC of 0.98 for sex (p = 0.01) and 0.91 for determining age < or ≥ 18 years old (p < 0.001). CONCLUSION: DCNNs can accurately predict sex from CXRs and distinguish between adult and pediatric patients in both American and Chinese populations. The ability to glean demographic information from CXRs may aid forensic investigations, as well as help identify novel anatomic landmarks for sex and age.

Can AI outperform a junior resident? Comparison of deep neural network to first-year radiology residents for identification of pneumothorax.

PURPOSE: To (1) develop a deep learning system (DLS) using a deep convolutional neural network (DCNN) for identification of pneumothorax, (2) compare its performance to first-year radiology residents, and (3) evaluate the ability of a DLS to augment radiology residents by detecting missed pneumothoraces. METHODS: This was a retrospective study performed in September 2018. We obtained 112,120 chest radiographs (CXRs) from the NIH ChestXray14 database, of which 4360 cases (4%) were labeled as pneumothorax by natural language processing. We utilized 111,518 CXRs to train and validate the ResNet-152 DCNN pretrained on ImageNet to identify pneumothorax. DCNN testing was performed on a hold-out set of 602 CXRs, whose groundtruth was determined by a cardiothoracic radiologist. Two first-year radiology residents evaluated the test CXRs for presence of pneumothorax. Receiver operating characteristic (ROC) curves were generated for each evaluator with area under the curve (AUC) compared using the DeLong parametric method. RESULTS: The DCNN achieved AUC of 0.841 for identification of pneumothorax at a rate of 1980 images/min. In contrast, both first-year residents achieved significantly higher AUCs of 0.942 and 0.905 (p < 0.01 for both compared to DCNN), but at a slower rate of two images/min. The DCNN identified 3 of 31 (9.7%) additional pneumothoraces missed by at least one of the residents. CONCLUSION: A DLS for pneumothorax identification had lower AUC than 1st-year radiology residents, but interpreted images > 1000× as fast and identified 3 additional pneumothoraces missed by the residents. Our findings suggest that DLS could augment radiologists-in-training to identify potential urgent findings.

Clinical Benefit to an Aurora A Kinase Inhibitor in a Patient with Metastatic Integrase Interactor 1-Deficient Carcinoma.

Integrase interactor 1 (INI-1)-deficient carcinoma is a rare cancer characterized by the loss of the SWItch/Sucrose Non-Fermentable-related matrix-associated actin-dependent regulator of chromatin subfamily B member 1 gene (SMARCB1) and tends to follow an aggressive clinical course. There is no currently available standard therapy option, although a few promising treatment strategies, including enhancer of zeste homolog 2 (EZH2) inhibition, are under active investigation. This report describes a 30-year-old woman with INI-1-deficient carcinoma who progressed on combination chemotherapy and an EZH2 inhibitor. Next-generation-sequencing-based targeted cancer-related gene assay confirmed SMARCB1 loss and revealed other mutations in breast cancer 1 gene and checkpoint kinase 2 gene, which may have impacted her clinical course. After discussion at the molecular tumor board, she was offered alisertib, an aurora A kinase inhibitor, on a single-patient expanded-use program and achieved prolonged disease stabilization. Aurora A kinase inhibition may have an important role in the management of patients with INI-1-deficient tumors, warranting further evaluation in clinical studies. KEY POINTS: Loss of the SWItch/Sucrose Non-Fermentable-related matrix-associated actin-dependent regulator of chromatin subfamily B member 1 gene (SMARCB1), which encodes integrase interactor 1 (INI-1), is associated with various mesenchymal malignancies, but a few carcinomas with rhabdoid features have been recently described as a distinct entity.INI-1-deficient carcinoma can be very aggressive, and there is no known treatment option available.There are encouraging preliminary data with an enhancer of zeste homolog 2 inhibitor, tazematostat, in INI-1-deficient malignancies, including INI-1-deficient carcinomas.Loss of INI-1 can activate aurora A kinase (AurkA), and inhibition of AurkA by alisertib could be a viable option and warrants further investigation in this cancer.Clinical genomic profiling can confirm diagnosis of molecularly defined malignancy and provide insights on therapeutic options.

Deep Learning Method for Automated Classification of Anteroposterior and Posteroanterior Chest Radiographs.

Ensuring correct radiograph view labeling is important for machine learning algorithm development and quality control of studies obtained from multiple facilities. The purpose of this study was to develop and test the performance of a deep convolutional neural network (DCNN) for the automated classification of frontal chest radiographs (CXRs) into anteroposterior (AP) or posteroanterior (PA) views. We obtained 112,120 CXRs from the NIH ChestX-ray14 database, a publicly available CXR database performed in adult (106,179 (95%)) and pediatric (5941 (5%)) patients consisting of 44,810 (40%) AP and 67,310 (60%) PA views. CXRs were used to train, validate, and test the ResNet-18 DCNN for classification of radiographs into anteroposterior and posteroanterior views. A second DCNN was developed in the same manner using only the pediatric CXRs (2885 (49%) AP and 3056 (51%) PA). Receiver operating characteristic (ROC) curves with area under the curve (AUC) and standard diagnostic measures were used to evaluate the DCNN's performance on the test dataset. The DCNNs trained on the entire CXR dataset and pediatric CXR dataset had AUCs of 1.0 and 0.997, respectively, and accuracy of 99.6% and 98%, respectively, for distinguishing between AP and PA CXR. Sensitivity and specificity were 99.6% and 99.5%, respectively, for the DCNN trained on the entire dataset and 98% for both sensitivity and specificity for the DCNN trained on the pediatric dataset. The observed difference in performance between the two algorithms was not statistically significant (p = 0.17). Our DCNNs have high accuracy for classifying AP/PA orientation of frontal CXRs, with only slight reduction in performance when the training dataset was reduced by 95%. Rapid classification of CXRs by the DCNN can facilitate annotation of large image datasets for machine learning and quality assurance purposes.

Use of ChatGPT, GPT-4, and Bard to Improve Readability of ChatGPT's Answers to Common Questions About Lung Cancer and Lung Cancer Screening.

ChatGPT's responses to questions about lung cancer and LCS, although deemed clinically appropriate by cardiothoracic radiologists, were difficult to read. Simplified responses from three LLMs (ChatGPT, GPT-4, and Bard) had improved reading ease and readability (in terms of U.S. grade levels). However, some simplified responses were no longer clinically appropriate.

CT findings of fungal pneumonia with emphasis on aspergillosis.

Fungal pneumonias are increasingly common in the population of immunosuppressed patients. The diagnosis of fungal pneumonias represents a challenge for clinicians, and the morbidity and mortality of these infections are high in immunocompromised patients. CT findings may be nonspecific; however, in the appropriate clinical setting, they may suggest and even help establish the specific diagnosis. This article provides an overview about the CT findings and possible differential diagnosis of the most common pulmonary fungal infections.

Evaluation of Coronary Artery Disease and Coronary Anomalies with a Handheld Smartphone.

The purpose of this study was to determine the diagnostic accuracy of an iPhone for evaluation of the coronary arteries on coronary CT angiography (CTA) in comparison to a standard clinical workstation. Fifty coronary CTA exams were selected to include a range of normal and abnormal cases including both coronary artery disease (CAD) of varying severity and coronary artery anomalies. Two cardiac radiologists reviewed each exam on a standard clinical workstation initially and then on an iPhone 6 after a washout period. Coronary stenosis was evaluated on a 4-point scale and presence of coronary anomalies was recorded. Two additional cardiac radiologists reviewed all cases in consensus on the standard workstation and these results were used as the reference standard. When reader results were compared to the reference standard, there was no significant difference in agreement for per-vessel stenosis scores using either the iPhone or standard clinical workstation. The intraobserver intertechnology agreement on a per-vessel basis for obstructive CAD were 97.4% (299/307, kappa = 0.777) and 97.5% (317/325, kappa = 0.804) for the two readers. All cases of coronary anomalies were identified by both readers regardless of the device used. Coronary CTA examinations can be interpreted on a smartphone with diagnostic accuracy comparable to a standard workstation. 3D visualization app on the iPhone may facilitate urgent coronary CTA review when a workstation is not available.

Interlaboratory assessment of nitrous oxide isotopomer analysis by isotope ratio mass spectrometry and laser spectroscopy: current status and perspectives.

RATIONALE: In recent years, research and applications of the N2O site-specific nitrogen isotope composition have advanced, reflecting awareness of the contribution of N2O to the anthropogenic greenhouse effect, and leading to significant progress in instrument development. Further dissemination of N2O isotopomer analysis, however, is hampered by a lack of internationally agreed gaseous N2O reference materials and an uncertain compatibility of different laboratories and analytical techniques. METHODS: In a first comparison approach, eleven laboratories were each provided with N2O at tropospheric mole fractions (target gas T) and two reference gases (REF1 and REF2). The laboratories analysed all gases, applying their specific analytical routines. Compatibility of laboratories was assessed based on N2O isotopocule data for T, REF1 and REF2. Results for T were then standardised using REF1 and REF2 to evaluate the potential of N2O reference materials for improving compatibility between laboratories. RESULTS: Compatibility between laboratories depended on the analytical technique: isotope ratio mass spectrometry (IRMS) results showed better compatibility for δ(15)N values, while the performance of laser spectroscopy was superior with respect to N2O site preference. This comparison, however, is restricted by the small number of participating laboratories applying laser spectroscopy. Offset and two-point calibration correction of the N2O isotopomer data significantly improved the consistency of position-dependent nitrogen isotope data while the effect on δ(15)N values was only minor. CONCLUSIONS: The study reveals that for future research on N2O isotopocules, standardisation against N2O reference material is essential to improve interlaboratory compatibility. For atmospheric monitoring activities, we suggest N2O in whole air as a unifying scale anchor.

Missed pulmonary embolism on abdominal CT.

OBJECTIVE: The purpose of this study is to evaluate a series of missed pulmonary emboli (PE) identified on abdominal CT and to describe their characteristics and the clinical scenario. MATERIALS AND METHODS: All reports of chest CT scans performed during a 12-month period were searched for keywords indicative of PE. Among patients with PE, patients who also underwent an enhanced abdominal CT within 3 months were assessed for missed PE. Three radiologists reviewed the abdominal CT to confirm the presence of a missed PE. Missed PEs were classified as unknown or known. Each study was assessed for characteristics of the missed PE and the image quality of the PE study. The electronic medical record was used to document the clinical context in which the PE occurred. RESULTS: Eighteen patients (12 men and six women; average age, 58.8 years) were identified as having missed PE on abdominal CT. In seven patients (38.9%), the PE had not been previously diagnosed. Most of the missed PEs were segmental, but three missed PEs occurred in lobar vessels. In a slight majority of the cases, the reviewing radiologists judged the contrast bolus as good. The abdominal CT on which PE was overlooked was obtained for a variety of reasons, most commonly because of abdominal pain or to follow up a preexisting condition. CONCLUSION: This study shows that missed PE can occur on abdominal CT. It is recommended that interpretation include a careful search of the lower pulmonary arterial vasculature on contrast-enhanced abdominal CT scans.