Incidental diagnosis of lung cancer on chest CT scan performed for suspected or documented COVID-19 infection.

CONTEXT: Recent studies have shown a benefit of chest computed tomography (CT scan) in lung cancer screening. The COVID-19 pandemic has led to many chest CT scan performed on a large population. The objective of this study was to describe the incidence and characteristics of lung cancer detected on chest CT scan, outside the framework of a clinical trial, for a suspected or documented COVID-19 infection. METHODS: We conducted a multicenter study, carried out from the analysis of data from the prospective COVID-19 database of the Clinical Data Warehouse of the Greater Paris University Hospitals (AP-HP). We identified the patients who had been diagnosed with a lung cancer, due to a chest CT scan done for a suspected or confirmed COVID-19 infection. The study period was limited to the first two epidemic lockdowns: (03/01/20 - 05/31/20) and (10/10/20 - 11/30/20). RESULTS: Over the study period, 24 390 patients had at least one chest CT scan. Among them, 72 lung cancer diagnoses were made (incidence 0.30 %; median age 67.4 years old, 50.0 % current smokers, 55.6 % adenocarcinoma). Half of the lung cancer patients (n = 36) did not meet the National Lung Screening Trial inclusion criteria. Twenty-six patients (36.1 %) were diagnosed at an early stage, 25 (34.7 %) of whom received radical curative treatment. Twenty-six patients died during the follow-up (36.1 %) but none in early stages. The median overall survival in lung cancer patients was 693 days [532 - NA]. CONCLUSIONS: A large-scale chest CT scan strategy for suspected or documented COVID-19 infection has allowed a significant proportion of early-stage lung cancer diagnosis, all of which have benefited from curative treatment.

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.

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.

Occurrence of pulmonary embolism related to COVID-19.

Recent reports have suggested an increased risk of pulmonary embolism (PE) related to COVID-19. The aim of this cohort study is to compare the incidence of PE during a 3-year period and to assess the characteristics of PE in COVID-19. We studied consecutive patients presenting with PE (January 2017-April 2020). Clinical presentation, computed tomography (CT) and biological markers were systematically assessed. We recorded the global number of hospitalizations during the COVID-19 pandemic and during the same period in 2018-2019. We included 347 patients: 326 without COVID-19 and 21 with COVID-19. Patients with COVID-19 experienced more likely dyspnea (p=0.04), had lower arterial oxygen saturation (p<0.001), higher C-reactive protein and white blood cell (WBC) count (p<0.0001 and p=0.001, respectively), and a significantly higher in-hospital mortality (14% versus 3.4%, p=0.04). Among COVID-19 patients, diagnosis of PE was performed at admission in 38% (n=8). COVID-19 patients with diagnosis of PE during hospitalization (n=13) had significantly more dyspnea (p=0.04), lower arterial oxygen saturation (p=0.01), less proximal PE (p=0.02), and higher heart rate (p=0.009), CT severity score (p=0.001), C-reactive protein (p=0.006) and WBC count (p=0.04). During the COVID-19 outbreak, a 97.4% increase of PE incidence was observed as compared to 2017-2019 and the proportion of hospitalizations related to PE was 3.7% versus 1.3% in 2018-2019 (p<0.0001). In conclusion, the COVID-19 pandemic leads to a dramatic increased incidence of PE. Physicians should be aware that PE may be diagnosed at admission, but also after several days of hospitalization, with a different clinical, CT and biological features of thrombotic disease.