Radiation-Induced Pneumonitis in the Era of the COVID-19 Pandemic: Artificial Intelligence for Differential Diagnosis

Giordano, Francesco Maria; Ippolito, Edy; Quattrocchi, Carlo Cosimo; Greco, Carlo; Mallio, Carlo Augusto; Santo, Bianca; D’Alessio, Pasquale; Crucitti, Pierfilippo; Fiore, Michele; Zobel, Bruno Beomonte; D’Angelillo, Rolando Maria; Ramella, Sara

SIMPLE SUMMARY: Radiation-induced pneumonitis and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) interstitial pneumonia show overlapping clinical features. As we are facing the COVID-19 pandemic, the discrimination between these two entities is of paramount importance. In fact, lung cancer patients are at higher risk of complications from SARS-CoV-2. In this study, we aimed to investigate if a deep learning algorithm was able to discriminate between COVID-19 and radiation therapy-related pneumonitis (RP). 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). ABSTRACT: (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.