Simple Summary The use of immune checkpoint inhibitors (ICIs) to treat oncologic diseases is progressively increasing. Computed tomography (CT) features of ICI therapy-related pneumonitis may overlap with other diseases, including coronavirus disease 2019 (COVID-19). Thus, oncologic patients undergoing ICI therapy and developing pneumonitis are at risk of being misdiagnosed. Exploring the strengths and weaknesses of artificial intelligence in distinguishing between ICI therapy-related pneumonitis and COVID-19 is of great importance for oncologic patients and for clinicians in order to increase awareness on this topic and stimulate novel strategies aimed to promptly and correctly classify and treat this category of vulnerable patients. Abstract Background: Coronavirus disease 2019 (COVID-19) pneumonia and immune checkpoint inhibitor (ICI) therapy-related pneumonitis share common features. The aim of this study was to determine on chest computed tomography (CT) images whether a deep convolutional neural network algorithm is able to solve the challenge of differential diagnosis between COVID-19 pneumonia and ICI therapy-related pneumonitis. Methods: We enrolled three groups: a pneumonia-free group ( n = 30), a COVID-19 group ( n = 34), and a group of patients with ICI therapy-related pneumonitis ( n = 21). Computed tomography images were analyzed with an artificial intelligence (AI) algorithm based on a deep convolutional neural network structure. Statistical analysis included the Mann–Whitney U test (significance threshold at p < 0.05) and the receiver operating characteristic curve (ROC curve). Results: The algorithm showed low specificity in distinguishing COVID-19 from ICI therapy-related pneumonitis (sensitivity 97.1%, specificity 14.3%, area under the curve (AUC) = 0.62). ICI therapy-related pneumonitis was identified by the AI when compared to pneumonia-free controls (sensitivity = 85.7%, specificity 100%, AUC = 0.97). Conclusions: The deep learning algorithm is not able to distinguish between COVID-19 pneumonia and ICI therapy-related pneumonitis. Awareness must be increased among clinicians about imaging similarities between COVID-19 and ICI therapy-related pneumonitis. ICI therapy-related pneumonitis can be applied as a challenge population for cross-validation to test the robustness of AI models used to analyze interstitial pneumonias of variable etiology.
【저자키워드】 COVID-19, deep learning, artificial intelligence, Chest CT, immune checkpoint inhibitors therapy, drug-induced pneumonitis, 【초록키워드】 coronavirus disease, Coronavirus disease 2019, therapy, Pneumonia, risk, tomography, immune, Chest computed tomography, sensitivity, specificity, Features, Algorithm, Patient, Awareness, Control, Immune checkpoint inhibitor, interstitial pneumonia, etiology, inhibitor, threshold, characteristic, disease, differential diagnosis, Analysis, similarity, ROC Curve, AUC, Topic, other diseases, overlap, clinician, treat, COVID-19 group, vulnerable patients, simple, Mann–Whitney U test, feature, enrolled, analyzed, determine, stimulate, 【제목키워드】 learning, Pneumonitis, deep, with COVID-19,