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Background Chest x-ray is a relatively accessible, inexpensive, fast imaging modality that might be valuable in the prognostication of patients with COVID-19. We aimed to develop and evaluate an artificial intelligence system using chest x-rays and clinical data to predict disease severity and progression in patients with COVID-19. Methods We did a retrospective study in multiple hospitals in the University of Pennsylvania Health System in Philadelphia, PA, USA, and Brown University affiliated hospitals in Providence, RI, USA. Patients who presented to a hospital in the University of Pennsylvania Health System via the emergency department, with a diagnosis of COVID-19 confirmed by RT-PCR and with an available chest x-ray from their initial presentation or admission, were retrospectively identified and randomly divided into training, validation, and test sets (7:1:2). Using the chest x-rays as input to an EfficientNet deep neural network and clinical data, models were trained to predict the binary outcome of disease severity (ie, critical or non-critical). The deep-learning features extracted from the model and clinical data were used to build time-to-event models to predict the risk of disease progression. The models were externally tested on patients who presented to an independent multicentre institution, Brown University affiliated hospitals, and compared with severity scores provided by radiologists. Findings 1834 patients who presented via the University of Pennsylvania Health System between March 9 and July 20, 2020, were identified and assigned to the model training (n=1285), validation (n=183), or testing (n=366) sets. 475 patients who presented via the Brown University affiliated hospitals between March 1 and July 18, 2020, were identified for external testing of the models. When chest x-rays were added to clinical data for severity prediction, area under the receiver operating characteristic curve (ROC-AUC) increased from 0·821 (95% CI 0·796–0·828) to 0·846 (0·815–0·852; p<0·0001) on internal testing and 0·731 (0·712–0·738) to 0·792 (0·780–0 ·803; p<0·0001) on external testing. When deep-learning features were added to clinical data for progression prediction, the concordance index (C-index) increased from 0·769 (0·755–0·786) to 0·805 (0·800–0·820; p<0·0001) on internal testing and 0·707 (0·695–0·729) to 0·752 (0·739–0·764; p<0·0001) on external testing. The image and clinical data combined model had significantly better prognostic performance than combined severity scores and clinical data on internal testing (C-index 0·805 vs 0·781; p=0·0002) and external testing (C-inde 0·752 vs 0·715; p<0·0001). Interpretation In patients with COVID-19, artificial intelligence based on chest x-rays had better prognostic performance than clinical data or radiologist-derived severity scores. Using artificial intelligence, chest x-rays can augment clinical data i...

Objectives Early recognition of coronavirus disease 2019 (COVID-19) severity can guide patient management. However, it is challenging to predict when COVID-19 patients will progress to critical illness. This study aimed to develop an artificial intelligence system to predict future deterioration to critical illness in COVID-19 patients. Methods An artificial intelligence (AI) system in a time-to-event analysis framework was developed to integrate chest CT and clinical data for risk prediction of future deterioration to critical illness in patients with COVID-19. Results A multi-institutional international cohort of 1,051 patients with RT-PCR confirmed COVID-19 and chest CT was included in this study. Of them, 282 patients developed critical illness, which was defined as requiring ICU admission and/or mechanical ventilation and/or reaching death during their hospital stay. The AI system achieved a C-index of 0.80 for predicting individual COVID-19 patients’ to critical illness. The AI system successfully stratified the patients into high-risk and low-risk groups with distinct progression risks ( p < 0.0001). Conclusions Using CT imaging and clinical data, the AI system successfully predicted time to critical illness for individual patients and identified patients with high risk. AI has the potential to accurately triage patients and facilitate personalized treatment. Key Point • AI system can predict time to critical illness for patients with COVID-19 by using CT imaging and clinical data. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-021-08049-8.

Primary pulmonary myxoid sarcoma (PPMS) is a recently reported, exceedingly rare low-grade lung neoplasm characterized by reticular/lace-like growth of spindle to epithelioid cells embedded in an abundant myxoid matrix. Morphologically, it overlaps with a myxoid variant of angiomatoid fibrous histiocytoma (AFH) of the soft tissue. Genetically, they were both reported to harbor EWSR1-CREB1 fusion, while EWSR1-ATF1 has only been reported in AFH thus far. We report a case of primary pulmonary low-grade myxoid spindle cell tumor with morphologic and immunohistochemical features of PPMS but with an EWSR1-ATF1 fusion gene. In addition, we also encountered a case of endobronchial AFH with EWSR1-CREB1 translocation but also focal morphologic features of PPMS. These findings provide new evidence supporting the concept that PPMS and a myxoid variant of AFH represent a continuum with overlapping histologic, immunohistochemical, and genetic features.

Atypical lipomatous tumor/well-differentiated liposarcoma (ALT/WDL) is an indolent, locally aggressive mesenchymal neoplasm, most often confined to the lower extremities and retroperitoneum and rarely identified in the orbit. Diagnosis of ALT/WDL can be challenging due to its frequent morphologic overlap with benign adipose lesions and other more aggressive liposarcoma subtypes, including myxoid liposarcoma. We describe a 26-year-old female with a history of hereditary retinoblastoma and external-beam radiotherapy to the orbit, who developed orbital liposarcoma. Although initial morphologic assessment raised the consideration of myxoid liposarcoma, subsequent fluorescein in situ hybridization studies demonstrated MDM2 and DDIT3 coamplification without DDIT3 rearrangement, supporting the diagnosis of ALT/WDL with myxoid stroma. The literature review of previously reported orbital myxoid liposarcomas revealed a morphologic overlap of documented tumors with ALT/WDL, dedifferentiated liposarcoma, and pleomorphic liposarcoma with myxoid stroma as well as an absence of immunohistochemical and molecular genetic data supportive of the diagnosis of myxoid liposarcoma. This case emphasizes the potential overlap of ALT/WDL with myxoid liposarcoma and the increasing importance of molecular genetic studies in the diagnosis, prognosis, and management of orbital liposarcoma.