Coronavirus disease (COVID-19) is highly infectious, has spread worldwide, and has a relatively high mortality rate. Early diagnosis and timely isolation are essential to control the spread of COVID-19. Computed tomography (CT) is considered to be an effective tool for the rapid diagnosis of COVID-19 and plays a key role in diagnosis, clinical course monitoring, and the evaluation of treatment outcomes. Artificial intelligence (AI) has emerged as a useful technology for early diagnosis, lesion quantification, and prognosis evaluation in patients with COVID-19. In this review, we discuss the role of CT in the diagnosis of COVID-19, typical CT manifestations of COVID-19 throughout the disease course, differential diagnoses, and the application of AI as a diagnostic and therapeutic tool in this patient population.

Pulmonary hydatid disease is a helminthic zoonotic disease caused by Echinococcus infection. The symptoms may appear several years after infection. Chest computed tomography (CT) is the preferred examination method and plays an important role in early diagnosis, treatment, and prognosis evaluation. CT can be used to diagnose simple cystic lesions. However, when the cysts are infected or ruptured, atypical imaging findings such as increased cyst density, blurring of the cyst wall, and surrounding exudation may lead to misdiagnosis of lung infection or lung abscess, hindering the therapeutic effect. We analyzed and compared the atypical imaging manifestations of pulmonary simple hydatid disease and hydatid cyst rupture. The aims of this report are to improve clinicians' understanding of these diseases, promote early diagnosis and treatment, and reduce the occurrence of complications.

Background: Extramedullary epidural metastatic tumors of small cell lung cancer (SCLC) are rare, and their clinical symptoms and imaging features lack specificity. This study was aimed at improving understanding of epidural metastatic SCLC tumors. Case report: We present the case of a 75-year-old patient with an extramedullary epidural metastatic SCLC tumor that was misinterpreted as a primary intraspinal tumor according to preoperative CT and MRI resonance imaging. Laboratory test results for CA-153 (28.30 U/mL) were substantially abnormal. A solid, well-defined, soft tissue mass approximately 0.3 cm × 1.5 cm in diameter at the seventh and eighth thoracic canals was observed on CT and MRI images. A dural tail sign was observed on contrast-enhanced magnetic MRI. Because the tumor compressed the spinal cord, the intraspinal mass was resected, and the vertebral canal was decompressed. Pathological examination confirmed the diagnosis of an extramedullary epidural metastatic SCLC tumor. Conclusions: Extramedullary epidural metastatic SCLC tumors lack clinical specificity. Imaging is helpful for early diagnosis, treatment, prediction of the disease course, and evaluation of curative effects. Ultimately, pathological examination and biopsy are required to confirm the diagnosis.