Excluding pulmonary embolism with helical (spiral) computed tomography: Evidence is catching up with enthusiasm.
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ABSTRACT:
SUBMITTER: Kearon C
PROVIDER: S-EPMC155960 | biostudies-literature | 2003 May
REPOSITORIES: biostudies-literature
Publications
Excluding pulmonary embolism with helical (spiral) computed tomography: Evidence is catching up with enthusiasm.
CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne 20030501 11
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Project description:ObjectiveWe compared the efficacy of single phase-computed tomography pulmonary angiography (SP-CTPA) and dual phase-computed tomography pulmonary angiography (DP-CTPA) for the diagnosis of pulmonary embolism (PE).MethodsWe recruited 1,019 consecutive patients (359 with PE) who underwent DP-CTPA (phase I: pulmonary artery phase; phase II: aortic phase) for suspected PE between January and October 2021. Phase I of DP-CTPA was used as SP-CTPA, and the final clinical diagnosis (FCD) was used as the gold standard.ResultsThree hundred fifty-two cases of PE were detected by both methods, with the same sensitivity of 98.1% (99.6-99.5%). Using SP-CTPA, 142 cases [13 pulmonary insufficiency artifacts (PIA) and 129 systemic-pulmonary shunt artifacts (S-PSA)] were false-positive with specificity of 78.5% (75.3-81.6%). No false-positive was found with DP-CTPA, with specificity of 100%, positive predictive value of 1, and negative predictive value of 0.990 (Net Reclassification Improvement = 0.215; P < 0.05). According to FCD, the positive results of SP-CTPA were divided into PIA, S-PSA, and true-positive (TPSP-CTPA) groups, and pairwise comparisons were performed. The bronchiectasis and hemoptysis rate in S-PSA group was higher than that in PIA and TP groups (P < 0.001), and the pulmonary hypertension (PH) rate in PIA group was higher than that in S-PSA and TP groups (P < 0.001).ConclusionThe diagnostic efficiency of DP-CTPA for the diagnosis of PE was high. SP-CTPA may misdiagnose PIA (common in patients with PH) and S-PSA (common in patients with bronchiectasis and hemoptysis) as PE.
| S-EPMC8914113 | biostudies-literature
Project description:AimsThe use of computed tomography pulmonary angiography (CTPA) in the detection of pulmonary embolism (PE) has considerably increased due developing technology and better availability of imaging. The underuse of pre-test probability scores and overuse of CTPA has been previously reported. We sought to investigate the indications for CTPA at a University Hospital emergency clinic and seek for factors eliciting the potential overuse of CTPA.Methods and resultsAltogether 1001 patients were retrospectively collected and analysed from the medical records using a structured case report form. PE was diagnosed in 222/1001 (22.2%) of patients. Patients with PE had more often prior PE/deep vein thrombosis, bleeding/thrombotic diathesis and less often asthma, chronic obstructive pulmonary disease, coronary artery disease, or decompensated heart failure. Patients were divided into three groups based on Wells PE risk-stratification score and two groups based on the revised Geneva score. A total of 9/382 (2.4%), 166/527 (31.5%), and 47/92 (52.2%) patients had PE in the CTPA in the low, intermediate, and high pre-test likelihood groups according to Wells score, and 200/955 (20.9%) and 22/46 (47.8%) patients had PE in the CTPA in the low-intermediate and the high pre-test likelihood groups according to the revised Geneva score, respectively. D-dimer was only measured from 568/909 (62.5%) and 597/955 (62.5%) patients who were either in the low or the intermediate-risk group according to Wells score and the revised Geneva score. Noteworthy, 105/1001 (10.5%) and 107/1001 (10.7%) of the CTPAs were inappropriately ordered according to the Wells score and the revised Geneva score. Altogether 168/1001 (16.8%) could theoretically be avoided.ConclusionsThis study highlights scant utilization of guideline-recommended risk-stratification tools in CTPA use at the emergency department.
| S-EPMC9170567 | biostudies-literature
Project description:Computed tomographic pulmonary angiography (CTPA) is the gold standard for pulmonary embolism (PE) diagnosis. However, this diagnosis is susceptible to misdiagnosis. In this study, we aimed to perform a systematic review of current literature applying deep learning for the diagnosis of PE on CTPA. MEDLINE/PUBMED were searched for studies that reported on the accuracy of deep learning algorithms for PE on CTPA. The risk of bias was evaluated using the QUADAS-2 tool. Pooled sensitivity and specificity were calculated. Summary receiver operating characteristic curves were plotted. Seven studies met our inclusion criteria. A total of 36,847 CTPA studies were analyzed. All studies were retrospective. Five studies provided enough data to calculate summary estimates. The pooled sensitivity and specificity for PE detection were 0.88 (95% CI 0.803-0.927) and 0.86 (95% CI 0.756-0.924), respectively. Most studies had a high risk of bias. Our study suggests that deep learning models can detect PE on CTPA with satisfactory sensitivity and an acceptable number of false positive cases. Yet, these are only preliminary retrospective works, indicating the need for future research to determine the clinical impact of automated PE detection on patient care. Deep learning models are gradually being implemented in hospital systems, and it is important to understand the strengths and limitations of these algorithms.
| S-EPMC8338977 | biostudies-literature
Project description: Not available
| S-EPMC9531291 | biostudies-literature
Project description:The lack of publicly available datasets of computed-tomography angiography (CTA) images for pulmonary embolism (PE) is a problem felt by physicians and researchers. Although a number of computer-aided detection (CAD) systems have been developed for PE diagnosis, their performance is often evaluated using private datasets. In this paper, we introduce a new public dataset called FUMPE (standing for Ferdowsi University of Mashhad's PE dataset) which consists of three-dimensional PE-CTA images of 35 different subjects with 8792 slices in total. For each benchmark image, two expert radiologists provided the ground-truth with the assistance of a semi-automated image processing software tool. FUMPE is a challenging benchmark for CAD methods because of the large number (i.e., 3438) of PE regions and, more especially, because of the location of most of them (i.e., 67%) in lung peripheral arteries. Moreover, due to the reporting of the Qanadli score for each PE-CTA image, FUMPE is the first public dataset which can be used for the analysis of mortality and morbidity risks associated with PE. We also report some complementary prognosis information for each subject.
| S-EPMC6122162 | biostudies-literature
Project description: Not available
| S-EPMC9851412 | biostudies-literature
Project description:BackgroundDiagnosis of pulmonary embolism (PE) constitutes a challenge for practitioners. Current practice involves use of pre-test probability prediction rules. Several strategies to optimize this process have been explored.ObjectivesTo explore whether application of the pulmonary embolism rule-out criteria (PERC rule) and age-adjusted D-dimer (DD) would have reduced the number of computed tomography pulmonary angiography (CTPA) examinations performed in patients with suspected PE.MethodsA retrospective cross-sectional study of adult patients taken for CTPA under suspicion of PE in 2018 and 2020. The PERC rule and age-adjusted DD were applied. The number of cases without indications for imaging studies was estimated and the operational characteristics for diagnosis of PE were calculated.Results302 patients were included. PE was diagnosed in 29.8%. Only 27.2% of 'not probable' cases according to the Wells criteria had D-dimer assays. Age adjustment would have reduced tomography use by 11.1%, with an AUC of 0.5. The PERC rule would have reduced use by 7%, with an AUC of 0.72.ConclusionsApplication of age-adjusted D-dimer and the PERC rule to patients taken for CTPA because of suspected PE seems to reduce the number of indications for the procedure.
| S-EPMC10153795 | biostudies-literature
Project description:ObjectivesCloser reading of computed tomography pulmonary angiography (CTPA) scans of patients presenting with acute pulmonary embolism (PE) may identify those at high risk of developing chronic thromboembolic pulmonary hypertension (CTEPH). We aimed to validate the predictive value of six radiological predictors that were previously proposed.MethodsThree hundred forty-one patients with acute PE were prospectively followed for development of CTEPH in six European hospitals. Index CTPAs were analysed post hoc by expert chest radiologists blinded to the final diagnosis. The accuracy of the predictors using a predefined threshold for 'high risk' (≥ 3 predictors) and the expert overall judgment on the presence of CTEPH were assessed.ResultsCTEPH was confirmed in nine patients (2.6%) during 2-year follow-up. Any sign of chronic thrombi was already present in 74/341 patients (22%) on the index CTPA, which was associated with CTEPH (OR 7.8, 95%CI 1.9-32); 37 patients (11%) had ≥ 3 of 6 radiological predictors, of whom 4 (11%) were diagnosed with CTEPH (sensitivity 44%, 95%CI 14-79; specificity 90%, 95%CI 86-93). Expert judgment raised suspicion of CTEPH in 27 patients, which was confirmed in 8 (30%; sensitivity 89%, 95%CI 52-100; specificity 94%, 95%CI 91-97).ConclusionsThe presence of ≥ 3 of 6 predefined radiological predictors was highly specific for a future CTEPH diagnosis, comparable to overall expert judgment, while the latter was associated with higher sensitivity. Dedicated CTPA reading for signs of CTEPH may therefore help in early detection of CTEPH after PE, although in our cohort this strategy would not have detected all cases.Key points• Three expert chest radiologists re-assessed CTPA scans performed at the moment of acute pulmonary embolism diagnosis and observed a high prevalence of chronic thrombi and signs of pulmonary hypertension. • On these index scans, the presence of ≥ 3 of 6 predefined radiological predictors was highly specific for a future diagnosis of chronic thromboembolic pulmonary hypertension (CTEPH), comparable to overall expert judgment. • Dedicated CTPA reading for signs of CTEPH may help in early detection of CTEPH after acute pulmonary embolism.
| S-EPMC8921171 | biostudies-literature
Project description:BackgroundPulmonary embolism (PE) is life-threatening and requires timely and accurate diagnosis, yet current imaging methods, like computed tomography pulmonary angiography, present limitations, particularly for patients with contraindications to iodinated contrast agents. We aimed to develop a quantitative texture analysis pipeline using machine learning (ML) based on non-contrast thoracic computed tomography (CT) scans to discover intensity and textural features correlated with regional lung perfusion (Q) physiology and pathology and synthesize voxel-wise Q surrogates to assist in PE diagnosis.MethodsWe retrospectively collected 99mTc-labeled macroaggregated albumin Q-SPECT/CT scans from patients suspected of PE, including an internal dataset of 76 patients (64 for training, 12 for testing) and an external testing dataset of 49 patients. Quantitative CT features were extracted from segmented lung subregions and underwent a two-stage feature selection pipeline. The prior-knowledge-driven preselection stage screened for robust and non-redundant perfusion-correlated features, while the data-driven selection stage further filtered features by fitting ML models for classification. The final classification model, trained with the highest-performing PE-associated feature combination, was evaluated in the testing cohorts based on the Area Under the Curve (AUC) for subregion-level predictability. The voxel-wise Q surrogate was then synthesized using the final selected feature maps (FMs) and model score maps (MSMs) to investigate spatial distributions. The Spearman correlation coefficient (SCC) and Dice similarity coefficient (DSC) were used to assess the spatial consistency between FMs or MSMs and Q-SPECT scans.ResultsThe optimal model performance achieved an AUC of 0.863 during internal testing and 0.828 on the external testing cohort. The model identified a combination containing 14 intensity and textural features that were non-redundant, robust, and capable of distinguishing between high- and low-functional lung regions. Spatial consistency assessment in the internal testing cohort showed moderate-to-high agreement between MSMs and reference Q-SPECT scans, with median SCC of 0.66, median DSCs of 0.86 and 0.64 for high- and low-functional regions, respectively.ConclusionsThis study validated the feasibility of using quantitative texture analysis and a data-driven ML pipeline to generate voxel-wise lung perfusion surrogates, providing a radiation-free, widely accessible alternative to functional lung imaging in managing pulmonary vascular diseases.Clinical trial numberNot applicable.
| S-EPMC11520386 | biostudies-literature
Project description:ImportancePulmonary embolism (PE) is a life-threatening clinical problem, and computed tomographic imaging is the standard for diagnosis. Clinical decision support rules based on PE risk-scoring models have been developed to compute pretest probability but are underused and tend to underperform in practice, leading to persistent overuse of CT imaging for PE.ObjectiveTo develop a machine learning model to generate a patient-specific risk score for PE by analyzing longitudinal clinical data as clinical decision support for patients referred for CT imaging for PE.Design, setting, and participantsIn this diagnostic study, the proposed workflow for the machine learning model, the Pulmonary Embolism Result Forecast Model (PERFORM), transforms raw electronic medical record (EMR) data into temporal feature vectors and develops a decision analytical model targeted toward adult patients referred for CT imaging for PE. The model was tested on holdout patient EMR data from 2 large, academic medical practices. A total of 3397 annotated CT imaging examinations for PE from 3214 unique patients seen at Stanford University hospitals and clinics were used for training and validation. The models were externally validated on 240 unique patients seen at Duke University Medical Center. The comparison with clinical scoring systems was done on randomly selected 100 outpatient samples from Stanford University hospitals and clinics and 101 outpatient samples from Duke University Medical Center.Main outcomes and measuresPrediction performance of diagnosing acute PE was evaluated using ElasticNet, artificial neural networks, and other machine learning approaches on holdout data sets from both institutions, and performance of models was measured by area under the receiver operating characteristic curve (AUROC).ResultsOf the 3214 patients included in the study, 1704 (53.0%) were women from Stanford University hospitals and clinics; mean (SD) age was 60.53 (19.43) years. The 240 patients from Duke University Medical Center used for validation included 132 women (55.0%); mean (SD) age was 70.2 (14.2) years. In the samples for clinical scoring system comparisons, the 100 outpatients from Stanford University hospitals and clinics included 67 women (67.0%); mean (SD) age was 57.74 (19.87) years, and the 101 patients from Duke University Medical Center included 59 women (58.4%); mean (SD) age was 73.06 (15.3) years. The best-performing model achieved an AUROC performance of predicting a positive PE study of 0.90 (95% CI, 0.87-0.91) on intrainstitutional holdout data with an AUROC of 0.71 (95% CI, 0.69-0.72) on an external data set from Duke University Medical Center; superior AUROC performance and cross-institutional generalization of the model of 0.81 (95% CI, 0.77-0.87) and 0.81 (95% CI, 0.73-0.82), respectively, were noted on holdout outpatient populations from both intrainstitutional and extrainstitutional data.Conclusions and relevanceThe machine learning model, PERFORM, may consider multitudes of applicable patient-specific risk factors and dependencies to arrive at a PE risk prediction that generalizes to new population distributions. This approach might be used as an automated clinical decision-support tool for patients referred for CT PE imaging to improve CT use.
| S-EPMC6686780 | biostudies-literature