Radiation-induced pneumonitis in the era of the COVID-19 pandemic: artificial intelligence for differential diagnosis

(1) aim: to test the performance of a deep learning algorithm in discriminating radiation therapy-related pneumonitis (RP) from COVID-19 pneumonia. (2) methods: In this retrospective study, we enrolled three groups of subjects: pneumonia-free (control group), COVID-19 pneumonia and RP patients. CT images were analyzed by mean of an artificial intelligence (AI) algorithm based on a novel deep convolutional neural network structure. the cut-off value of risk probability of COVID-19 was 30%; values higher than 30% were classified as COVID-19 high risk, and values below 30% as COVID-19 low risk. the statistical analysis included the mann-whitney U test (significance threshold at p < 0.05) and receiver operating characteristic (ROC) curve, with fitting performed using the maximum likelihood fit of a binormal model. (3) results: most patients presenting RP (66.7%) were classified by the algorithm as COVID-19 low risk. the algorithm showed high sensitivity but low specificity in the detection of RP against COVID-19 pneumonia (sensitivity = 97.0%, specificity = 2%, area under the curve (AUC = 0.72). the specificity increased when an estimated COVID-19 risk probability cut-off of 30% was applied (sensitivity 76%, specificity 63%, AUC = 0.84). (4) conclusions: the deep learning algorithm was able to discriminate RP from COVID-19 pneumonia, classifying most RP cases as COVID-19 low risk.