Deep Learning Algorithm Trained with COVID-19 Pneumonia Also Identifies Immune Checkpoint Inhibitor Therapy-Related Pneumonitis.

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.

COVID-19 pneumonia and immune-related pneumonitis: critical issues on differential diagnosis, potential interactions, and management.

INTRODUCTION: The COVID-19 pandemic occurred amid the cancer immunotherapy revolution. Immune checkpoint inhibitors (ICIs) have become the standard of care for several solid cancers and are associated with peculiar toxicities, including pneumonitis which has similar features to COVID-19 pneumonia. AREAS COVERED: We summarize the main hallmarks of lung injury induced by ICIs and severe acute respiratory syndrome coronavirus 2 and discuss the critical aspects for differential diagnosis and management. Symptoms and radiological findings are often similar; conversely, treatments are quite different. Furthermore, we focus on potential interactions generating hypotheses that need confirmatory studies. EXPERT OPINION: All cancer patients treated with immunotherapy should receive screening for SARS-CoV-2. This would improve the diagnosis and management of pneumonia and guide therapeutic choices. Furthermore, clinicians could estimate the risk/benefit of continuing ICI treatment in COVID-19 positive patients. Temporary withdrawal of the immunotherapy treatment pending resolution of viral infection may be a reasonable option in long-responders patients.

Facing SARS-CoV-2 outbreak in immunotherapy era.

The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) represents a worldwide sanitary emergency. The viral biology is only partially known, with some aspects in common with other coronaviruses, and the damage observed in the most severe cases is due to intense inflammation. Immunotherapy restores immunological activity against cancer cells and it has become a standard treatment for several cancers. We carried out an examination of available data on the effects exerted by both SARS-CoV-2 and the most widespread immunotherapy treatments on the immune system in order to hypothesize mechanisms underlying potential and mutual interaction. We provided an analysis of laboratory, clinical and therapeutic data related with severe acute respiratory syndrome coronavirus. We finally focused on implications of immunotherapy treatments in clinical practice.