Evaluating lung perfusion SPECT/CT imaging in patients with COVID-19 through radiomics and formal methods

ABSTRACT

Background-Aim: The inflammatory cascade in patients (pts) with COVID-19 may lead to pulmonary embolism (PE), worsening prognosis. Lung perfusion SPECT/CT (Q-scan) in symptomatic pts discharged after COVID-19 can confirm or rule out pulmonary vascular involvement, helping the differential diagnosis with other respiratory diseases. We aim to investigate an innovative methodology, based on radiomic features and formal methods, as a virtual second look able to detect perfusion abnormalities to better define appropriate patient-centered diagnostic and therapeutic strategies. Methods: A total of 23 pts with a recent history of COVID-19, without any previous pulmonary disease (e.g. lung cancer, emphysema, or pathological findings at CT such as lung bullae) were enrolled for Q-scan for persistent dyspnea 1 month after discharge. They were classified as negative (14 pts) and positive (9 pts) for lung perfusion abnormalities by visual and semiquantitative analysis. Q-Lung® software by GE Healthcare was used to obtain percent evaluation of pulmonary lobar perfusion (cts/volume % for each lobe), assuming as a normal value any defect lower than 10% for each lobe. We analysed these data using an innovative methodology based on formal methods techniques centered on mathematical logical reasoning, to build a formal and rigorous representation of a system merging patients clinical conditions and disease-specific characteristics, to confirm or exclude the disease. Results: In a comparative analysis with Q-Scan results, the model showed concordant features in 13/23 pts, identifying perfusion defects in 8/9 pts with a positive Q-Scan, and excluding perfusion defects in 5/14 pts with a negative Q-Scan. Discordant results were observed in the remaining 10/23 pts, in particular in negative pts however, in this sub-group, the Q-Lung semiquantitative analysis revealed perfusion defects lower than 10% per lobe, which we considered unsignificant but may deserve further evaluation. Conclusions: Although our data are still preliminary and based on a limited population, this methodology based on formal methods showed promising concordance with Q-scan results and needs to be implemented with further analyses including co-registered CT data. When compared to artificial intelligence techniques, this mathematical reasoning may enable (i) to use a reduced dataset of patients and/ or images, without having any impact on the robustness of the model;(ii) to produce an intuitive model easy to understand;(iii) to represent a rigorous and formal tool that may be used by medical specialists in a clinical setting.