AI support for accurate and fast radiological diagnosis of COVID-19: an international multicenter, multivendor CT study.

OBJECTIVES: Differentiation between COVID-19 and community-acquired pneumonia (CAP) in computed tomography (CT) is a task that can be performed by human radiologists and artificial intelligence (AI). The present study aims to (1) develop an AI algorithm for differentiating COVID-19 from CAP and (2) evaluate its performance. (3) Evaluate the benefit of using the AI result as assistance for radiological diagnosis and the impact on relevant parameters such as accuracy of the diagnosis, diagnostic time, and confidence. METHODS: We included n = 1591 multicenter, multivendor chest CT scans and divided them into AI training and validation datasets to develop an AI algorithm (n = 991 CT scans; n = 462 COVID-19, and n = 529 CAP) from three centers in China. An independent Chinese and German test dataset of n = 600 CT scans from six centers (COVID-19 / CAP; n = 300 each) was used to test the performance of eight blinded radiologists and the AI algorithm. A subtest dataset (180 CT scans; n = 90 each) was used to evaluate the radiologists' performance without and with AI assistance to quantify changes in diagnostic accuracy, reporting time, and diagnostic confidence. RESULTS: The diagnostic accuracy of the AI algorithm in the Chinese-German test dataset was 76.5%. Without AI assistance, the eight radiologists' diagnostic accuracy was 79.1% and increased with AI assistance to 81.5%, going along with significantly shorter decision times and higher confidence scores. CONCLUSION: This large multicenter study demonstrates that AI assistance in CT-based differentiation of COVID-19 and CAP increases radiological performance with higher accuracy and specificity, faster diagnostic time, and improved diagnostic confidence. KEY POINTS: • AI can help radiologists to get higher diagnostic accuracy, make faster decisions, and improve diagnostic confidence. • The China-German multicenter study demonstrates the advantages of a human-machine interaction using AI in clinical radiology for diagnostic differentiation between COVID-19 and CAP in CT scans.

Refractory primary central nervous system lymphoma treated with programmed death-1 monoclonal antibody and Bruton tyrosine kinase inhibitor combined with rituximab: report of 1 case and review of literature / 白血病·淋巴瘤

Objective:To investigate the effect and safety of rituximab, programmed death 1 (PD-1) monoclonal antibody, and Bruton tyrosine kinase (BTK) inhibitor on elderly refractory primary central nervous system lymphoma (PCNSL).Methods:The clinical data of an elderly patient with refractory PCNSL treated with the combination of rituximab, PD-1 monoclonal antibody and BTK inhibitor in the First Hospital of Jilin University in February 2020 were retrospectively analyzed. The relevant literature was reviewed.Results:The patient had primary central nervous system diffuse large B-cell lymphoma (high-risk group), and the Memorial Sloan Kettering Cancer Center (MSKCC) score was 2 (estimated overall survival time was 7 months). Disease progressed after 1 course of treatment. Complete remission was achieved after the therapy of rituximab, PD-1 monoclonal antibody combined with BTK inhibitor. PD-1 monoclonal antibody maintenance therapy was performed and patient was followed up until November 17, 2021. The patient's condition was stable. The second progression-free survival (PFS) time was 20 months, and the overall survival time was 21 months. The patient well tolerated the new drug treatment, and no adverse reactions of grade 3 or above occurred.Conclusions:The new targeted combination therapy can be used as a treatment option for elderly PCNSL patients, which can further improve the curative effect and significantly improve the prognosis.