Abstract
Coronavirus disease 2019 (COVID-19) pneumonia is associated with a high rate of pulmonary embolism (PE). In patients with contraindications for CT pulmonary angiography (CTPA) or non-diagnostic on CTPA, perfusion single photon emission computed tomography/computed tomography (Q-SPECT/CT) is a diagnosis option. The goal of this work is to develop an Intelligent Radiomic system for the detection of PE in COVID-19 patients from the analysis of Q-SPECT/CT scans.Our Intelligent Radiomic System for identification of patients with PE (with/without pneumonia) is based on a local analysis of SPECT-CT volumes that considers both CT and SPECT values for each volume point. We present an hybrid approach that uses radiomic features extracted from each scan as input to a siamese classification network trained to discriminate among 4 different types of tissue: no pneumonia without PE (control group), no pneumonia with PE, pneumonia without PE and pneumonia with PE.The proposed radiomic system has been tested on 133 patients, 63 with COVID-19 (26 with PE, 22 without PE, 15 indeterminate-PE) and 70 without COVID-19 (31 healthy/control, 39 with PE). The per-patient recall for the detection of COVID-19 pneumonia and COVID-19 pneumonia with PE was, respectively, 91% and 81% with an area under the receiver operating characteristic curves equal to 0.99 and 0.87.