RESUMEN
BACKGROUND: COVID-19 infection, especially in cases with pneumonia, is associated with a high rate of pulmonary embolism (PE). In patients with contraindications for CT pulmonary angiography (CTPA) or non-diagnostic CTPA, perfusion single-photon emission computed tomography/computed tomography (Q-SPECT/CT) is a diagnostic alternative. The goal of this study is to develop a radiomic diagnostic system to detect PE based only on the analysis of Q-SPECT/CT scans. METHODS: This radiomic diagnostic system is based on a local analysis of Q-SPECT/CT volumes that includes both CT and Q-SPECT values for each volume point. We present a combined approach that uses radiomic features extracted from each scan as input into a fully connected classification neural network that optimizes a weighted cross-entropy loss trained to discriminate between three different types of image patterns (pixel sample level): healthy lungs (control group), PE and pneumonia. Four types of models using different configuration of parameters were tested. RESULTS: The proposed radiomic diagnostic system was trained on 20 patients (4,927 sets of samples of three types of image patterns) and validated in a group of 39 patients (4,410 sets of samples of three types of image patterns). In the training group, COVID-19 infection corresponded to 45% of the cases and 51.28% in the test group. In the test group, the best model for determining different types of image patterns with PE presented a sensitivity, specificity, positive predictive value and negative predictive value of 75.1%, 98.2%, 88.9% and 95.4%, respectively. The best model for detecting pneumonia presented a sensitivity, specificity, positive predictive value and negative predictive value of 94.1%, 93.6%, 85.2% and 97.6%, respectively. The area under the curve (AUC) was 0.92 for PE and 0.91 for pneumonia. When the results obtained at the pixel sample level are aggregated into regions of interest, the sensitivity of the PE increases to 85%, and all metrics improve for pneumonia. CONCLUSION: This radiomic diagnostic system was able to identify the different lung imaging patterns and is a first step toward a comprehensive intelligent radiomic system to optimize the diagnosis of PE by Q-SPECT/CT. HIGHLIGHTS: Artificial intelligence applied to Q-SPECT/CT is a diagnostic option in patients with contraindications to CTPA or a non-diagnostic test in times of COVID-19.
RESUMEN
Ruling out pulmonary embolism (PE) can be challenging in a situation of elevated D-dimer values such as in a case of COVID-19 infection. Our objective was to evaluate the difference in D-dimer values of subjects infected with COVID-19 in those with PE and those without and to analyze the predictive value of D-dimer for PE in these subjects based on the day of D-dimer determination. This was an observational, retrospective study, conducted at a tertiary hospital. All subjects with PCR-confirmed COVID-19 infection requiring hospital admission at our institution between the months of March and April 2020 were included in the study. We compared D-dimer levels in subjects who went on to develop a PE and those who did not. We then created a model to predict the subsequent development of a PE with the current D-dimer levels of the subject. D-dimer levels changed over time from COVID-19 diagnosis, but were always higher in subjects who went on to develop a PE. Regarding the predictive model created, the area under the curve of the ROC analyses of the cross-validation predictions was 0.72. The risk of pulmonary embolism for the same D-dimer levels varied depending on the number of days elapsed since COVID-19 diagnosis and D-dimer determination. To conclude, D-dimer levels were elevated in subjects with a COVID-19 infection, especially in those with PE. D-dimer levels increased during the first 10 days after the diagnosis of the infection and can be used to predict the risk of PE in COVID-19 subjects.