B-Cell Lymphoma Associated with COVID-19 Infection: A Case Report.

PURPOSE: Most common publications are related to COVID-19 diagnosis in hematological malignancy patients. However, here we report a case involving a patient diagnosed with B-cell lymphoma while undergoing treatment for COVID-19, including the changes in major clinical symptoms and medical examinations, then explain the probable causes of the case. CASE PRESENTATION: A 74-year-old woman with a previous history of oesophageal cancer was admitted to the hospital after having cough and sputum for 15 days. Despite the COVID-19 symptoms, this patient did not have a fever at the time of the onset. Results of routine blood tests were normal at first but then declined with persistent fever, and A whole-body C.T. examination ruled out the possibility of tumor-metastasis-related fever. This patient had no hepatosplenomegaly or regional lymphadenopathy, and there was no concrete evidence of haemophagocytic lymphohistiocytosis or lymphoma until bone marrow biopsy results confirmed the latter. CONCLUSION: We describe an uncommon case of COVID-19 who was finally diagnosed with B-cell lymphoma. An awareness of persistent fever and declined routine blood tests caused by hematological malignancies instead of COVID-19 itself can aid in providing appropriate guidelines for management and treatment.

A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-19 pneumonia from chest X-ray images.

Common lung diseases are first diagnosed using chest X-rays. Here, we show that a fully automated deep-learning pipeline for the standardization of chest X-ray images, for the visualization of lesions and for disease diagnosis can identify viral pneumonia caused by coronavirus disease 2019 (COVID-19) and assess its severity, and can also discriminate between viral pneumonia caused by COVID-19 and other types of pneumonia. The deep-learning system was developed using a heterogeneous multicentre dataset of 145,202 images, and tested retrospectively and prospectively with thousands of additional images across four patient cohorts and multiple countries. The system generalized across settings, discriminating between viral pneumonia, other types of pneumonia and the absence of disease with areas under the receiver operating characteristic curve (AUCs) of 0.94-0.98; between severe and non-severe COVID-19 with an AUC of 0.87; and between COVID-19 pneumonia and other viral or non-viral pneumonia with AUCs of 0.87-0.97. In an independent set of 440 chest X-rays, the system performed comparably to senior radiologists and improved the performance of junior radiologists. Automated deep-learning systems for the assessment of pneumonia could facilitate early intervention and provide support for clinical decision-making.