Abstract
Simple SummaryThe 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.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.
Highlights
SARS-CoV-2 is a novel coronavirus that was first identified in Wuhan, China, and that is responsible for a highly contagious respiratory disease named coronavirus disease 2019 (COVID-19) [1,2]
We found excellent accuracy of the algorithm in the detection of COVID-19 pneumonia when compared with pneumonia-free patients (AUC = 0.99)
These results support the use of the algorithm as a valid tool for diagnosis and management support of COVID-19 pneumonia in the Italian population, to what was reported in the Chinese population [9], especially in those settings where the automated analysis could be used to rapidly screen patients admitted with symptoms of suspect COVID-19 pneumonia
Summary
SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a novel coronavirus that was first identified in Wuhan, China, and that is responsible for a highly contagious respiratory disease named coronavirus disease 2019 (COVID-19) [1,2]. The CT features of COVID-19 pneumonia are nonspecific and may overlap with those of other types of pneumonia and pneumonitis [9], including immune checkpoint inhibitor (ICI) therapy-related pneumonitis. 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. Computed tomography images were analyzed with an artificial intelligence (AI) algorithm based on a deep convolutional neural network structure. 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). Conclusions: The deep learning algorithm is not able to distinguish between COVID-19 pneumonia 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
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