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

PURPOSE: The 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 METHODS: Using 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. RESULTS: Quantitative 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. CONCLUSION: This 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.

Comparison of Qanadli score with conventional risk stratifiers in non-massive pulmonary emboli.

OBJECTIVE: The management and risk stratification of non-massive pulmonary embolism (PE) remain unclear. However, early assessment of PE severity can aid physicians in establishing better treatment milestones. There has been no direct comparison of mortality rates in patients with non-massive PE, and existing data are sometimes contradictory. Therefore, we examined the relationship between the Qanadli index and conventional risk stratifiers in PE. METHODS: We retrospectively analyzed 200 consecutively selected patients diagnosed with PE. The assessment included computed tomography pulmonary angiography, electrocardiography, echocardiography findings, outcomes, and a comparison with the Simplified Pulmonary Embolism Severity Index (SPESI) score. Descriptive, regression, and receiver operating characteristic analyses were performed. RESULTS: The mean Qanadli score was 13.5 ± 1.15. Pearson correlation analysis revealed significant associations between the total Qanadli score and several variables: right ventricular enlargement, follow-up ejection fraction, and SPESI score. Although the Qanadli score did not significantly predict mortality, the risk of death increased by 58.8% for each 1-unit increase in the SPESI score. CONCLUSIONS: Although the Qanadli index is valuable in assessing PE and guiding treatment strategies, its standalone predictive value for mortality may be insufficient. Therefore, incorporating scoring systems such as the SPESI and echocardiographic findings is recommended for more accurate mortality prediction.

Detection and quantification of pulmonary embolism with artificial intelligence: The SFR 2022 artificial intelligence data challenge.

PURPOSE: In 2022, the French Society of Radiology together with the French Society of Thoracic Imaging and CentraleSupelec organized their 13th data challenge. The aim was to aid in the diagnosis of pulmonary embolism, by identifying the presence of pulmonary embolism and by estimating the ratio between right and left ventricular (RV/LV) diameters, and an arterial obstruction index (Qanadli's score) using artificial intelligence. MATERIALS AND METHODS: The data challenge was composed of three tasks: the detection of pulmonary embolism, the RV/LV diameter ratio, and Qanadli's score. Sixteen centers all over France participated in the inclusion of the cases. A health data hosting certified web platform was established to facilitate the inclusion process of the anonymized CT examinations in compliance with general data protection regulation. CT pulmonary angiography images were collected. Each center provided the CT examinations with their annotations. A randomization process was established to pool the scans from different centers. Each team was required to have at least a radiologist, a data scientist, and an engineer. Data were provided in three batches to the teams, two for training and one for evaluation. The evaluation of the results was determined to rank the participants on the three tasks. RESULTS: A total of 1268 CT examinations were collected from the 16 centers following the inclusion criteria. The dataset was split into three batches of 310, 580 and 378 C T examinations provided to the participants respectively on September 5, 2022, October 7, 2022 and October 9, 2022. Seventy percent of the data from each center were used for training, and 30% for the evaluation. Seven teams with a total of 48 participants including data scientists, researchers, radiologists and engineering students were registered for participation. The metrics chosen for evaluation included areas under receiver operating characteristic curves, specificity and sensitivity for the classification task, and the coefficient of determination r2 for the regression tasks. The winning team achieved an overall score of 0.784. CONCLUSION: This multicenter study suggests that the use of artificial intelligence for the diagnosis of pulmonary embolism is possible on real data. Moreover, providing quantitative measures is mandatory for the interpretability of the results, and is of great aid to the radiologists especially in emergency settings.

Pulmonary Thromboembolism Following Spine Surgery: Clinical Suspicion is the Key.

Pulmonary thromboembolism following spine surgery, although rare, could end into devastating outcome. Gold standard for it diagnosis is pulmonary CT angiography but in operating theatre, clinical suspicion is the key to diagnose. Here we report a case of pulmonary embolism with classic clinical findings which approved using pulmonary CT angiography and echocardiography.

Value of CT pulmonary angiography to predict short-term outcome in patient with pulmonary embolism.

To evaluate the role of CT pulmonary angiography (CTPA) in the assessment of pulmonary embolism (PE) severity and the related CT cardiac changes, reflecting the clinical status of the patients and predicting the outcome. A prospective study of 184 patients presented with suspicious acute PE. All patients underwent CTPA followed by ECHO. Pulmonary artery obstructive index (PAOI) using Qanadli Score was calculated and cardiac changes recorded. The patients' outcome was followed up for 30 days. Only 150 patients completed the study; 26.7% needed ICU admission while 13.3% died during follow-up. There was a significant relationship between the PAOI and the risk classification, right ventricular dysfunction (RVD) diagnosed by ECHO and the patients' short outcome. We found PAOI cut off value 45% for mortality and 35% for ICU admission and 27.5% for RVD with 60, 75 and 90% sensitivity and 80, 73.3 and 68.6% specificity respectively. CT RV/LV ratio was the most sensitive parameter to predict RV dysfunction followed by pulmonary artery diameter. CTPA is not only used for diagnosis but also to assess the severity of PE, the effect on the right ventricular function and subsequently the need for ICU admission and prediction of the outcome.

Quality of life after pulmonary embolism as assessed with SF-36 and PEmb-QoL.

INTRODUCTION: Although quality of life (QoL) is recognized as an important indicator of the course of a disease, it has rarely been addressed in studies evaluating the outcome of care for patients with pulmonary embolism (PE). This study primarily aimed to evaluate the QoL of patients with acute PE in comparison to population norms and to patients with other cardiopulmonary diseases, using a generic QoL questionnaire. Secondary, the impact of time period from diagnosis and clinical patient characteristics on QoL was assessed, using a disease-specific questionnaire. METHODS: QoL was assessed in 109 consecutive out-patients with a history of objectively confirmed acute PE (mean age 60.4 ± 15.0 years, 56 females), using the generic Short Form-36 (SF-36) and the disease specific Pulmonary Embolism Quality of Life questionnaire (PEmb-QoL). The score of the SF-36 were compared with scores of the general Dutch population and reference populations with chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF), a history of acute myocardial infarction (AMI), derived from the literature. Scores on the SF-35 and PEmb-QoL were used to evaluate QoL in the short-term and long-term clinical course of patients with acute PE. In addition, we examined correlations between PEmb-QoL scores and clinical patient characteristics. RESULTS: Compared to scores of the general Dutch population, scores of PE patients were worse on several subscales of the SF-36 (social functioning, role emotional, general health (P<0.001), role physical and vitality (P<0.05)). Compared to patients with COPD and CHF, patients with PE scored higher (=better) on all subscales of the SF-36 (P ≤ 0.004) and had scores comparable with patients with AMI the previous year. Comparing intermediately assessed QoL with QoL assessed in long-term follow-up, PE patients scored worse on SF-36 subscales: physical functioning, social functioning, vitality (P<0.05), and on the PEmb-QoL subscales: emotional complaints and limitations in ADL (P ≤ 0.03). Clinical characteristics did not correlate with QoL as measured by PEmb-QoL. CONCLUSION: Our study demonstrated an impaired QoL in patients after treatment of PE. The results of this study provided more knowledge about QoL in patients treated for PE.