COVID-19 vaccination simulating lymph node progression in a patient with prostate cancer.

Several cases of cancer patients with 18-fluorodeoxyglucose (18FDG) Positron Emission Tomography/Computed Tomography (PET/CT) evidence of metabolically active axillary lymph nodes after COVID-19 vaccination have been described, creating a diagnostic dilemma and sometimes leading to further unnecessary examinations. A 62-year-old male, diagnosed with prostate cancer, treated with hormone-therapy and radiotherapy of the prostate 2 years before, underwent fluorine-18 choline (F-FCH) PET/CT for restaging purpose, less than 3 weeks after he had received the second dose of the Pfizer BioNTech-BNT162b2 mRNA COVID-19 vaccine. This exam showed an increased F-FCH uptake and an enlargement of the left axillary, paratracheal, para-aortic, subcarinal, and hilar bilateral lymph nodes. Fourteen weeks later, the patient underwent a new F-FCH PET-CT scan, displaying an almost complete regularization of the FCH uptake in all the previously involved regions. The patient was not treated after the first PET-CT scan, thus, the aforementioned PET/CT findings represented inflammatory vaccine-related lymph nodes. This case highlights the significance of knowing vaccination history to correctly interpret imaging findings and to avoid false-positive reports.

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.