Computed Tomography Pulmonary Angiography Research Articles

Epicardial adipose tissue as aprognostic marker in acute pulmonary embolism.

Epicardial adipose tissue (EAT) has been established as aquantitative imaging biomarker associated with disease severity in coronary heart disease. Our aim was to use this prognostic marker derived from computed tomography pulmonary angiography (CTPA) for the prediction of mortality and prognosis in patients with acute pulmonary embolism. The clinical database was retrospectively screened for patients with acute pulmonary embolism between 2015 and 2021. Overall, 513 patients (216 female, 42.1%) were included in the analysis. The study end-point was 30-day mortality. Epicardial adipose tissue was measured on the diagnostic CTPA in asemiquantitative manner. The volume and density of EAT were measured for every patient. Overall, 60patients (10.4%) died within the 30-day observation period. The mean EAT volume was 128.3 ± 65.0 cm3 in survivors and 154.6 ± 84.5 cm3 in nonsurvivors (p = 0.02). The density of EAT was -79.4 ± 8.3 HU in survivors and -76.0 ± 8.4 HU in nonsurvivors (p = 0.86), and EAT density was associated with 30-day mortality (odds ratio [OR] = 1.07; 95% confidence interval [CI]: 1.03; 1.1, p < 0.001) but did not remain statistically significant in multivariable analysis. No association was identified between EAT volume and 30-day mortality (OR = 1.0; 95% CI: 1.0; 1.0, p = 0.48). There might be an association between EAT density and mortality in patients with acute pulmonary embolism. Further studies are needed to elucidate the prognostic relevance of EAT parameters in patients with acute pulmonary embolism.

Seeking an optimal approach for Computer-aided Diagnosis of Pulmonary Embolism

Pulmonary Embolism (PE) represents a thrombus (“blood clot”), usually originating from a lower extremity vein, that travels to the blood vessels in the lung, causing vascular obstruction and in some patients death. This disorder is commonly diagnosed using Computed Tomography Pulmonary Angiography (CTPA). Deep learning holds great promise for the Computer-aided Diagnosis (CAD) of PE. However, numerous deep learning methods, such as Convolutional Neural Networks (CNN) and Transformer-based models, exist for a given task, causing great confusion regarding the development of CAD systems for PE. To address this confusion, we present a comprehensive analysis of competing deep learning methods applicable to PE diagnosis based on four datasets. First, we use the RSNA PE dataset, which includes (weak) slice-level and exam-level labels, for PE classification and diagnosis, respectively. At the slice level, we compare CNNs with the Vision Transformer (ViT) and the Swin Transformer. We also investigate the impact of self-supervised versus (fully) supervised ImageNet pre-training, and transfer learning over training models from scratch. Additionally, at the exam level, we compare sequence model learning with our proposed transformer-based architecture, Embedding-based ViT (E-ViT). For the second and third datasets, we utilize the CAD-PE Challenge Dataset and Ferdowsi University of Mashad’s PE Dataset, where we convert (strong) clot-level masks into slice-level annotations to evaluate the optimal CNN model for slice-level PE classification. Finally, we use our in-house PE-CAD dataset, which contains (strong) clot-level masks. Here, we investigate the impact of our vessel-oriented image representations and self-supervised pre-training on PE false positive reduction at the clot level across image dimensions (2D, 2.5D, and 3D). Our experiments show that (1) transfer learning boosts performance despite differences between photographic images and CTPA scans; (2) self-supervised pre-training can surpass (fully) supervised pre-training; (3) transformer-based models demonstrate comparable performance but slower convergence compared with CNNs for slice-level PE classification; (4) model trained on the RSNA PE dataset demonstrates promising performance when tested on unseen datasets for slice-level PE classification; (5) our E-ViT framework excels in handling variable numbers of slices and outperforms sequence model learning for exam-level diagnosis; and (6) vessel-oriented image representation and self-supervised pre-training both enhance performance for PE false positive reduction across image dimensions. Our optimal approach surpasses state-of-the-art results on the RSNA PE dataset, enhancing AUC by 0.62% (slice-level) and 2.22% (exam-level). On our in-house PE-CAD dataset, 3D vessel-oriented images improve performance from 80.07% to 91.35%, a remarkable 11% gain. Codes are available at GitHub.com/JLiangLab/CAD_PE.

A case of metastatic breast cancer complicated by pulmonary tumor thrombotic microangiopathy

Pulmonary tumor thrombotic microangiopathy is a malignancy-related complication with rapid progression and high mortality. To improve the understanding of the disease, early diagnosis and treatment are key to successful treatment. A 39-year-old patient with pulmonary hypertension transferred from another hospital was admitted to the First Affiliated Hospital of Guangzhou Medical University on September 26, 2021. The patient developed shortness of breath and progressive exacerbation over the past month. No pulmonary artery embolism was seen on computed tomography pulmonary angiography (CTPA) at the outside hospital where the breast cancer was diagnosed. Pulmonary tumor thrombotic microangiopathy was immediately considered on admission and oncological endocrine therapy was started. After treatment, the patient's dyspnoea improved, PET-CT showed significant tumor regression, and cardiac ultrasound showed a significant decrease in pulmonary artery pressure. The successful treatment experience of this case was summarized for reference.

Detection and severity quantification of pulmonary embolism with 3D CT data using an automated deep learning-based artificial solution

PurposeThe purpose of this study was to propose a deep learning-based approach to detect pulmonary embolism and quantify its severity using the Qanadli score and the right-to-left ventricle diameter (RV/LV) ratio on three-dimensional (3D) computed tomography pulmonary angiography (CTPA) examinations with limited annotations. Materials and methodsUsing a database of 3D CTPA examinations of 1268 patients with image-level annotations, and two other public datasets of CTPA examinations from 91 (CAD-PE) and 35 (FUME-PE) patients with pixel-level annotations, a pipeline consisting of: (i), detecting blood clots; (ii), performing PE-positive versus negative classification; (iii), estimating the Qanadli score; and (iv), predicting RV/LV diameter ratio was followed. The method was evaluated on a test set including 378 patients. The performance of PE classification and severity quantification was quantitatively assessed using an area under the curve (AUC) analysis for PE classification and a coefficient of determination (R²) for the Qanadli score and the RV/LV diameter ratio. ResultsQuantitative evaluation led to an overall AUC of 0.870 (95% confidence interval [CI]: 0.850–0.900) for PE classification task on the training set and an AUC of 0.852 (95% CI: 0.810–0.890) on the test set. Regression analysis yielded R² value of 0.717 (95% CI: 0.668–0.760) and of 0.723 (95% CI: 0.668–0.766) for the Qanadli score and the RV/LV diameter ratio estimation, respectively on the test set. ConclusionThis study shows the feasibility of utilizing AI-based assistance tools in detecting blood clots and estimating PE severity scores with 3D CTPA examinations. This is achieved by leveraging blood clots and cardiac segmentations. Further studies are needed to assess the effectiveness of these tools in clinical practice.

Isolated pulmonary artery involvement in Takayasu arteritis: case report and review of the literature

BackgroundTakayasu arteritis (TAK) is a chronic inflammatory arteritis. It most often affects non-specific large vessel progressively, such as the aorta and its branches. The diagnosis in TAK is typically delayed. Isolated pulmonary artery involvement in Takayasu arteritis is uncommon. Owing to its rarity, the diagnosis is challenging and requires an integrated approach comprising clinical and imaging findings. In order to facilitate early diagnosis of TAK for clinicians, wider use of non-invasive imaging is impacting this.Case presentationIn this report, we present the imaging characteristics of a rare isolated pulmonary artery involvement in Takayasu arteritis. Pulmonary computed tomography angiography revealed only limited thick walls of both proximal pulmonary artery and stenosis of pulmonary artery lumen, and the other large blood vessels were not involved. The patient undergone pulmonary endarterectomy and pulmonary angioplasty. Then, approximately one month afterward, she passed away due to heart failure.ConclusionsImaging examination is the main basis for diagnosing this disease. This impression might improve disease awareness among doctors and progress in diagnosis.

Incidence of pulmonary thromboembolism in patients who underwent CT pulmonary angiography in the first quarter of 2023 at CHC Rijeka

Cilj: Plućna tromboembolija (PTE) je hitno medicinsko stanje kod kojeg dolazi do okluzije jedne ili više grana plućnih arterija tromboembolusom. CT plućna angiografija (CTPA) je brza, neinvazivna radiološka metoda kojom se postavlja dijagnoza PTE. Glavni cilj ovog rada je odrediti incidenciju pozitivnih PTE nalaza kod 524 pacijenta podvrgnutih CTPA te odrediti učestalost zahvaćenih grana plućnih arterija. Nadalje, jedan od ciljeva je i odrediti incidenciju usputnih nalaza (plućni nodusi, tumor pluća, pneumonija, infarkt pluća, pleuralni izljev). Ispitanici i metode: U ovom retrospektivnom istraživanju prikupljeni su podaci 524 bolesnika koji su zbog sumnje na PTE podvrgnuti CTPA pregledu na Kliničkom zavodu za dijagnostičku i intervencijsku radiologiju, na lokalitetu Sušak, u Kliničkom bolničkom centru (KBC) Rijeka, u razdoblju od siječnja do travnja 2023. godine. Svi potrebni podaci navedeni u nalazu radiologa prikupljeni su iz integriranog bolničkog informacijskog sustava (IBIS) i pripadajuće aplikacije (ISSA). Rezultati: Od ukupno 524 bolesnika podvrgnutih CTPA, PTE je dokazana kod 97 ili 18,51% pacijenata. U našem istraživanju bio je podjednak broj ispitanika muškog i ženskog spola (260 ili 49,62% naprema 264 ili 50,39%). Nešto je veća incidencija PTE bila kod muškaraca (52 ili 53,61%) nego kod žena (45 ili 46,39%). Najveći broj bolesnika (262 ili 50,00%) pripadao je dobnom razredu između 61. i 80. godine života. Usputni nalazi opisani na CTPA, prema redoslijedu učestalosti su bili: pneumonije (37,15%), pleuralni izljevi (34,86%), plućni nodusi (16,79%), tumori pluća (6,87%) i plućni infarkti (4,33%). Zaključak: CT plućna angiografija predstavlja zlatni standard za detekciju plućne tromboembolije. Pregledom dostupne literature, incidencija PTE je komparabilna s rezultatima našeg istraživanja. U našoj, kao i u većini recentnih studija, PTE je najčešće detektirana u segmentalnim i subsegmentalnim granama plućnih arterija, dok se incidencija usputnih nalaza detektiranih na CTPA ne podudara s rezultatima drugih istraživanja.

Algorithms of Electrocardiographic Changes for Quantitative and Localization Analysis of Thrombus Burden in Patients with Acute Pulmonary Thromboembolism.

Various electrocardiographic (ECG) abnormalities are associated with the severity of pulmonary thromboembolism (PTE). The utility of evaluating the clot burden of PTE based on ECG findings alone has yet to be thoroughly investigated in Chinese patients. The aim of this study was therefore to use ECG signs to establish novel models for quantitative and localization analysis of clot burden in patients with acute PTE. Acute PTE patients from three centers were enrolled between 2015 and 2019 in a retrospective cohort study (NCT03802929). We analyzed the 12-lead ECGs at admission and studied computed tomography pulmonary angiography (CTPA) features to obtain the Qanadli score of clot burden and location of thrombus. Novel risk prediction models were developed and validated using derivation and external validation cohorts, respectively. A total of 341 acute PTE patients were screened, of whom 246 (72.1%) were from Shanghai Tenth People's Hospital, 71 (20.8%) were from Shanghai Pulmonary Hospital and 24 (7.0%) were from Qidong People's Hospital. In the derivation cohort, predictors included in the final models were congestive heart failure, chronic obstructive pulmonary disease, hypertension, coronary heart disease, atrial fibrillation and ECG abnormalities. The CHARIS (COPD/CHF/CHD, HTN, Atrial arrhythmias/AF, RBBB/RAD, Inverted T wave and S1Q3T3/ Sinus tachycardia) I model was established for quantitatively assessing Qanadli score. It had moderate discrimination in both the derivation cohort (concordance index (c-index) of 0.720, 95% CI 0.655-0.780) and the validation cohort (c-index of 0.663, 95% CI 0.559-0.757). The CHARIS II model was used to predict the probability of trunk obstruction. It showed similar discrimination in the derivation cohort (c-index of 0.753, 95% CI 0.691-0.811) and in the validation cohort (c-index of 0.741, 95% CI 0.641-0.827). Calibration curves and Hosmer-Lemeshow test confirmed the accuracy of the risk prediction equations in the external validation dataset. Decision curve analysis showed the CHARIS I and CHARIS II algorithms had positive net benefits in both the derivation and validation cohorts. From quantitative and localization perspectives, the CHARIS algorithms can identify acute PTE patients with heavy thrombus burdens prior to imaging diagnosis. NCT03802929, https://www.clinicaltrials.gov/study/NCT03802929.

Automated detection and segmentation of pulmonary embolisms on computed tomography pulmonary angiography (CTPA) using deep learning but without manual outlining.

We present a novel computer algorithm to automatically detect and segment pulmonary embolisms (PEs) on computed tomography pulmonary angiography (CTPA). This algorithm is based on deep learning but does not require manual outlines of the PE regions. Given a CTPA scan, both intra- and extra-pulmonary arteries were firstly segmented. The arteries were then partitioned into several parts based on size (radius). Adaptive thresholding and constrained morphological operations were used to identify suspicious PE regions within each part. The confidence of a suspicious region to be PE was scored based on its contrast in the arteries. This approach was applied to the publicly available RSNA Pulmonary Embolism CT Dataset (RSNA-PE) to identify three-dimensional (3-D) PE negative and positive image patches, which were used to train a 3-D Recurrent Residual U-Net (R2-Unet) to automatically segment PE. The feasibility of this computer algorithm was validated on an independent test set consisting of 91 CTPA scans acquired from a different medical institute, where the PE regions were manually located and outlined by a thoracic radiologist (>18 years' experience). An R2-Unet model was also trained and validated on the manual outlines using a 5-fold cross-validation method. The CNN model trained on the high-confident PE regions showed a Dice coefficient of 0.676±0.168 and a false positive rate of 1.86 per CT scan, while the CNN model trained on the manual outlines demonstrated a Dice coefficient of 0.647±0.192 and a false positive rate of 4.20 per CT scan. The former model performed significantly better than the latter model (p<0.01). The promising performance of the developed PE detection and segmentation algorithm suggests the feasibility of training a deep learning network without dedicating significant efforts to manual annotations of the PE regions on CTPA scans.

Development and validation of a lung graph-based machine learning model to predict acute pulmonary thromboembolism on chest noncontrast computed tomography.

Computed tomography pulmonary angiography (CTPA) is a first-line noninvasive method to diagnose acute pulmonary thromboembolism (APE); however, whether chest noncontrast CT (NC-CT) could aid in the diagnosis of APE remains unknown. The aim of this study was to build and evaluate a holistic lung graph-based machine learning (HLG-ML) using NC-CT for the diagnosis of APE and to compare its performance with that of radiologists and the YEARS algorithm. This study enrolled 178 cases (77 males; age 63.9±16.7 years) who underwent NC-CT and CTPA in the same day from January 2019 to December 2020. Of these patients, 133 (75% of cases; 58 males; age 65.4±15.6 years) were placed into a training group and 45 (25% of cases; 19 males; age 59.6±19.2 years) into a testing group. The other 43 cases (18 males; age 62.8±20.0 years) were used to externally validate the model between January 2021 and March 2022. A HLG was developed with a pulmonary radiomics descriptor derived from NC-CT images. The approach extracted local radiomics features and encoded these local features into a radiomics descriptor as a characterization of global radiomics feature distribution. Subsequently, 8 ML models were trained and compared based on the radiomics descriptor. In the validation group, area under the curves (AUCs) of the HLG-ML model in the diagnosis of APE were compared with those of the 3 radiologists and the YEARS algorithm. Among the 8 ML models, gradient boosting decision tree demonstrated the best classification performance (AUC =0.772) on the training set. In the testing set, the AUC of gradient boosting decision trees was 0.857 [95% confidence intervals (CIs): 0.699-0.951]. In the validation set, the performance of gradient boosting decision tree (AUC =0.810; 95% CI: 0.669-0.952; Youden index =0.621) outperformed 3 radiologists (AUC =0.508, 95% CI: 0.335-0.681, Youden index =0.016; AUC =0.504, 95% CI: 0.354-0.654, Youden index =0.008; AUC =0.527, 95% CI: 0.363-0.691, Youden index =0.050) and the YEARS algorithm (AUC =0.618; 95% CI: 0.469-0.767; Youden index =0.237). Compared to all 3 radiologists and the YEARS algorithm, the proposed HLG-based gradient boosting decision tree model achieved a superior performance in the diagnosis of APE on the NC-CT and may thus serve as a valuable tool for physicians in the diagnosis of APE.

Prospective Evaluation of AI Triage of Pulmonary Emboli on CT Pulmonary Angiograms.

Background Artificial intelligence (AI) algorithms have shown high accuracy for detection of pulmonary embolism (PE) on CT pulmonary angiography (CTPA) studies in academic studies. Purpose To determine whether use of an AI triage system to detect PE on CTPA studies improves radiologist performance or examination and report turnaround times in a clinical setting. Materials and Methods This prospective single-center study included adult participants who underwent CTPA for suspected PE in a clinical practice setting. Consecutive CTPA studies were evaluated in two phases, first by radiologists alone (n = 31) (May 2021 to June 2021) and then by radiologists aided by a commercially available AI triage system (n = 37) (September 2021 to December 2021). Sixty-two percent of radiologists (26 of 42 radiologists) interpreted studies in both phases. The reference standard was determined by an independent re-review of studies by thoracic radiologists and was used to calculate performance metrics. Diagnostic accuracy and turnaround times were compared using Pearson χ2 and Wilcoxon rank sum tests. Results Phases 1 and 2 included 503 studies (participant mean age, 54.0 years ± 17.8 [SD]; 275 female, 228 male) and 1023 studies (participant mean age, 55.1 years ± 17.5; 583 female, 440 male), respectively. In phases 1 and 2, 14.5% (73 of 503) and 15.9% (163 of 1023) of CTPA studies were positive for PE (P = .47). Mean wait time for positive PE studies decreased from 21.5 minutes without AI to 11.3 minutes with AI (P < .001). The accuracy and miss rate, respectively, for radiologist detection of any PE on CTPA studies was 97.6% and 12.3% without AI and 98.6% and 6.1% with AI, which was not significantly different (P = .15 and P = .11, respectively). Conclusion The use of an AI triage system to detect any PE on CTPA studies improved wait times but did not improve radiologist accuracy, miss rate, or examination and report turnaround times. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Murphy and Tee in this issue.

OC 01.1 Discordance between Computed Tomography Pulmonary Angiography (CTPA) and Transthoracic Echocardiography (TTE) in Identification of Right Ventricular Strain in Patients with Acute Pulmonary Embolism

OC 01.1 Discordance between Computed Tomography Pulmonary Angiography (CTPA) and Transthoracic Echocardiography (TTE) in Identification of Right Ventricular Strain in Patients with Acute Pulmonary Embolism

External validation, radiological evaluation, and development of deep learning automatic lung segmentation in contrast-enhanced chest CT

ObjectivesThere is a need for CT pulmonary angiography (CTPA) lung segmentation models. Clinical translation requires radiological evaluation of model outputs, understanding of limitations, and identification of failure points. This multicentre study aims to develop an accurate CTPA lung segmentation model, with evaluation of outputs in two diverse patient cohorts with pulmonary hypertension (PH) and interstitial lung disease (ILD).MethodsThis retrospective study develops an nnU-Net-based segmentation model using data from two specialist centres (UK and USA). Model was trained (n = 37), tested (n = 12), and clinically evaluated (n = 176) on a diverse ‘real-world’ cohort of 225 PH patients with volumetric CTPAs. Dice score coefficient (DSC) and normalised surface distance (NSD) were used for testing. Clinical evaluation of outputs was performed by two radiologists who assessed clinical significance of errors. External validation was performed on heterogenous contrast and non-contrast scans from 28 ILD patients.ResultsA total of 225 PH and 28 ILD patients with diverse demographic and clinical characteristics were evaluated. Mean accuracy, DSC, and NSD scores were 0.998 (95% CI 0.9976, 0.9989), 0.990 (0.9840, 0.9962), and 0.983 (0.9686, 0.9972) respectively. There were no segmentation failures. On radiological review, 82% and 71% of internal and external cases respectively had no errors. Eighteen percent and 25% respectively had clinically insignificant errors. Peripheral atelectasis and consolidation were common causes for suboptimal segmentation. One external case (0.5%) with patulous oesophagus had a clinically significant error.ConclusionState-of-the-art CTPA lung segmentation model provides accurate outputs with minimal clinical errors on evaluation across two diverse cohorts with PH and ILD.Clinical relevanceClinical translation of artificial intelligence models requires radiological review and understanding of model limitations. This study develops an externally validated state-of-the-art model with robust radiological review. Intended clinical use is in techniques such as lung volume or parenchymal disease quantification.Key Points• Accurate, externally validated CT pulmonary angiography (CTPA) lung segmentation model tested in two large heterogeneous clinical cohorts (pulmonary hypertension and interstitial lung disease).• No segmentation failures and robust review of model outputs by radiologists found 1 (0.5%) clinically significant segmentation error.• Intended clinical use of this model is a necessary step in techniques such as lung volume, parenchymal disease quantification, or pulmonary vessel analysis.Graphical

Picc Line Associated Brachiocephalic Vein Rupture with Consequent Mediastinal Placement Causing Pneumomediastinum and Large Total Parenteral Nutrition Bilateral Effusion.

PICC lines, although seen as safer, have potential for catastrophic adverse effects.The use of intracavitary electrocardiogram may be a safe adjunct and even an alternative to the use of a chest X-ray.Aspirating blood from all lumens with particular emphasis on the distal most lumen is very important.

Pulmonary embolism diagnostic strategies in patients with COPD exacerbation: Post-hoc analysis of the PEP trial

BackgroundThe prevalence of pulmonary embolism (PE) is approximately 11–17 % in patients with an acute exacerbation of chronic obstructive pulmonary disease (AE-COPD). The optimal diagnostic strategy for PE in these patients remains undetermined. AimsTo evaluate the safety and efficacy of standard (revised Geneva and Wells PE scores combined with fixed D-dimer cut-off) and computed tomography pulmonary angiogram (CTPA)-sparing diagnostic strategies (ADJUST-PE, YEARS, PEGeD, 4PEPS) in patients with AE-COPD. MethodPost-hoc analyses of data from the multicenter prospective PEP study were performed. The primary outcome was the diagnostic failure rate of venous thromboembolism (VTE) during the entire study period. Secondary outcomes included diagnostic failure rate of PE and deep venous thrombosis (DVT), respectively, during the entire study period and the number of CTPA needed per diagnostic strategy. Results740 patients were included. The revised Geneva and Wells PE scores combined with fixed D-dimer cut-off had a diagnostic failure rate of VTE of 0.7 % (95%CI 0.3 %–1.7 %), but >70.0 % of the patients needed imaging. All CTPA-sparing diagnostic algorithms reduced the need for CTPAs (−10.1 % to −32.4 %, depending on the algorithm), at the cost of an increased VTE diagnosis failure rate of up to 2.1 % (95%CI 1.2 %–3.4 %). ConclusionRevised Geneva and Wells PE scores combined with fixed D-dimer cut-off were safe, but a high number of CTPA remained needed. CTPA-sparing algorithms would reduce imaging, at the cost of an increased VTE diagnosis failure rate that exceeds the safety threshold. Further studies are needed to improve diagnostic management in this population.

Ten-Year Trend in Advanced Imaging Utilization for Suspected Pulmonary Embolism in Pregnancy

PurposeAdvanced imaging is essential to diagnose pulmonary embolism (PE) in pregnancy, but there are associated maternal and fetal radiation risks. The purpose of this study was to evaluate the 10-year trend in advanced imaging utilization for the evaluation of suspected PE in pregnancy. MethodsThe authors evaluated pregnant women with advanced imaging using CT pulmonary angiography (CTPA) or lung scintigraphy (LS) for evaluation of suspected PE presenting to two tertiary hospitals from 2007 to 2016. The rate of imaging was evaluated relative to positive PE rate and local pregnancy rate. positive PE was defined as a new acute PE finding on any advanced imaging within 3 days of first advanced imaging test. Local pregnancy rates were defined per 1,000 pregnancies in the county serviced by both hospitals. Chi-square testing was used to evaluate statistical significance (P < .05) in the utilization trend of advanced imaging and relative to local pregnancy rates and evaluations positive for PE. ResultsA total of 707 pregnant patients were identified, of whom 92.5% (n = 654) underwent CTPA and 7.5% (n = 53) underwent LS. Regression analysis showed an average increase of 5.2 advanced imaging studies per year (P < .001), with 61 and 105 studies performed in 2007 and 2016, respectively. Additionally, there was an average increase of 0.08 (P < .001) advanced imaging studies per 1,000 local pregnancies per year, doubling from 0.7 in 2007 to 1.4 in 2016 (P < .001). Finally, there was a decrease of 0.004 (P = .009) in advanced imaging positive for PE, from 3% (2 of 61) in 2007 to 0% (0 of 100) in 2016. ConclusionsAdvanced imaging utilization increased by 72% over the 10-year window, driven by higher use of CTPA. Although the detection rate of PE on advanced imaging has decreased, the utilization rate among pregnant patients doubled during this period. These results highlight the need to consider the radiation risks and costs of advanced imaging in specific patient populations.

Computed Tomography Pulmonary Angiography Prediction of Adverse Long-Term Outcomes in Chronic Thromboembolic Pulmonary Hypertension: Correlation with Hemodynamic Measurements Pre- and Post-Pulmonary Endarterectomy.

CT pulmonary angiography is commonly used in diagnosing chronic thromboembolic pulmonary hypertension (CTEPH). This work was conducted to determine if cardiac chamber size on CTPA may also be useful for predicting the outcome of CTEPH treatment. A retrospective analysis of paired CTPA and right heart hemodynamics in 33 consecutive CTEPH cases before and after pulmonary thromboendarterectomy (PTE) was performed. Semiautomated and manual CT biatrial and biventricular size quantifications were correlated with mean pulmonary artery pressure (mPAP), pulmonary vascular resistance (PVR) and cardiac output. The baseline indexed right atrioventricular volumes were twice the left atrioventricular volumes, with significant (p < 0.001) augmentation of left heart filling following PTE. Except for the left atrial volume to cardiac index, all other chamber ratios significantly correlated with hemodynamics. Left to right ventricular ratio cut point <0.82 has high sensitivity (91% and 97%) and specificity (88% and 85%) for identifying significant elevations of mPAP and PVR, respectively (AUC 0.90 and 0.95), outperforming atrial ratios (sensitivity 78% and 79%, specificity 82% and 92%, and AUC 0.86 and 0.91). Manual LV:RV basal dimension ratio correlates strongly with semiautomated volume ratio (r 0.77, 95% CI 0.64-0.85) and is an expeditious alternative with comparable prognostic utility (AUC 0.90 and 0.95). LV:RV dimension ratio of <1.03 and ≤0.99 (alternatively expressed as RV:LV ratio of >0.97 and ≥1.01) is a simple metric that can be used for CTEPH outcome prediction.

Pulmonary endarterectomy for chronic thromboembolic pulmonary hypertension in Cape Town, South Africa.

Pulmonary endarterectomy (PEA) is the only definitive and potentially curative therapy for chronic thromboembolic pulmonary hypertension (CTEPH), associated with impressive improvements in symptoms and haemodynamics. However, it is only offered at a few centres in South Africa. The characteristics and outcomes of patients undergoing PEA in Cape Town have not been reported previously. To assess the difference in World Health Organization functional class (WHO-FC) before and at least 6 weeks after surgery. We interrogated the adult cardiothoracic surgery database at the University of Cape Town between December 2005 and April 2021 for patients undergoing PEA at Groote Schuur Hospital and a private hospital. A total of 32 patients underwent PEA, of whom 8 were excluded from the final analysis owing to incomplete data or a histological diagnosis other than CTEPH. The work-up of these patients for surgery was variable: all had a computed tomography pulmonary angiogram, 7 (29%) had a ventilation/perfusion scan, 5 (21%) underwent right heart catheterisation, and none had a pulmonary angiogram. The perioperative mortality was 4/24 (17%): 1 patient (4%) had a cardiac arrest on induction of anaesthesia, 2 patients (8%) died of postoperative pulmonary haemorrhage, and 1 patient (4%) died of septic complications in the intensive care unit. Among the survivors, the median (interquartile range) improvement in WHO-FC was 2 (1 - 3) classes (p=0.0004); 10/16 patients (63%) returned to a normal baseline (WHO-FC I). Even in a low-volume centre, PEA is associated with significant improvements in WHO-FC and a return to a normal baseline in survivors. What the study adds. South African patients undergoing pulmonary endarterectomy (PEA) for chronic thromboembolic pulmonary hypertension (CTEPH) have a marked improvement in functional status, with many returning to a normal functional baseline. However, the small number of patients included in this study indicates that PEA is probably underutilised. Pre- and postoperative assessment is inconsistent, despite availability of established guidelines.Implications of the findings. More patients should be referred to specialist centres for assessment for this potentially curative procedure. Use of guidelines to standardise investigations and monitoring of patients with CTEPH may improve patient selection for surgery.

Computed tomography pulmonary angiogram in a poly-trauma patient undergoing veno venous extracorporeal membrane oxygenation: our experience in diagnosing pulmonary embolism

Extracorporeal membrane oxygenation (ECMO) is an adapted form of the heart–lung machine that provides cardiopulmonary bypass life support for critically ill patients with acute cardiac and/or respiratory failure who are unresponsive to standard medical and surgical therapies. Although ECMO therapy has advanced significantly, complications like thrombosis and bleeding continue to be substantial, necessitating rapid transport of patients to the radiology suite to reveal crucial findings for further treatment plans or care. Even though the low flow rate for veno-arterial ECMO (VA-ECMO) support during contrast-enhanced CT is well known, the flow rate for veno-venous ECMO support during CT, especially computed tomography pulmonary angiography, is not well understood. Our case emphasizes that CTPA can be performed safely in trauma patients with ECMO runs and any suspected complications during ECMO runs may delay or worsen the clinical prognosis therefore immediate radiological verdicts may reveal significant findings to plan the treatment accordingly. Also, there were no complications associated with our examinations or transport. Our case highlights that VV-ECMO like VA-ECMO induces flow-dependent alterations in the patient’s circulation due to contrast injections, which might lead to an incorrect interpretation of CTPA. However, CTPA is safe, and bringing down the VV-ECMO flow rate of zero with vigilant monitoring of vitals and ventilation support helps radiologists to predict and interpret the contrast flow based on injection and cannula sites in the diagnosing and evaluation of pulmonary embolism patients with acute respiratory failure during ECMO.

Pulmonary vasculitis in Behçet's disease: reference atlas computed tomography pulmonary angiography (CTPA) findings and risk assessment-management proposal.

Pulmonary artery aneurysms (PAAs) are the most well-defined type of pulmonary vascular complication in Behçet's disease (BD).The aim of this study is to analyze which CT pulmonary angiography (CTPA) signs are associated with serious morbidity and mortality. The study included 42 BD patients with pulmonary vascular complications. All patients' medical records were reviewed retrospectively in terms of demographics, disease characteristics, laboratory investigations, pulmonary manifestations, arterial and/or venous thrombosis and CTPA vascular and parenchymal findings. Deep venous thrombosis was observed in 31(73.8%) patients, arterial thrombosis in 13(31%), peripheral arterial aneurysms in 12(286%), haemoptysis in 38 (90.5%), and fatal haemoptysis in 8(19 %) patients. CTPA revealed: in situ thrombosis in 14(33.3%) patients, true stable PAAs in 13(31), true unstable PAAs in 11(26.2%), stable pulmonary artery pseudoaneurysms (PAPs) in 7(16.7%), unstable PAPs in 17(40.5%), the latter were associated with perianeurysmal leaking in 26(61.9%) and bronchial indentation in 19(45.2%).In regression analysis, fatal outcomes were associated with age in years (p=0.035), arterial thrombosis (p=0.025), peripheral arterial aneurysms (p=0.010), intracardiac thrombosis (p=0.026) and positively associated with haemoptysis severity (p<0.001). Peripheral arterial thrombosis and/or aneurysms, intracardiac thrombosis and haemoptysis severity are predictor of fatal outcomes in BD pulmonary vasculitis. PAPs with perianeurysmal alveolar haemorrhage and/or bronchial indentation are serious CTPA signs that require prompt identification and aggressive treatment. PAPs are a more serious aneurysmal pattern than true PAAs because they are a contained rupture of a PA branch in the context of pulmonary vasculitis.

An audit of CTPA and V/Q scan for investigation of pulmonary embolism in pregnancy.

Pulmonary embolism (PE) can be fatal yet difficult to diagnose in pregnancy. Computed tomography pulmonary angiogram (CTPA) or ventilation/perfusion (V/Q) scans are often conducted, potentially leading to low positive scan rates. Retrospective data analysis was conducted for pregnant women and non-pregnant age-matched control who underwent CTPA and/or V/Q scan for investigation of PE. The main outcomes were the positive and non-diagnostic imaging rates. In total, 440 women underwent V/Q or CTPA scans, 86 of whom were pregnant (19.5%). The positive scan rate was 3.5% and 8.8% in the pregnant and non-pregnant groups, respectively (p = 0.1). The non-diagnostic scan rate was similar between pregnant and non-pregnant groups (13.9% vs 9.9%, p = 0.3). Within the pregnant group, there were more non-diagnostic CTPAs than V/Q scans (p = 0.005). Our study confirms a low positive imaging rate and a relatively high non-diagnostic CTPA rate in pregnancy. Newer strategies are needed to reduce the number of negative imaging studies conducted.