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IntroductionLi-Fraumeni syndrome (LFS) is a hereditary cancer predisposition syndrome primarily caused by germline TP53 pathogenic/likely pathogenic (P/LP) variants. Soft tissue and bone sarcomas are among the most frequently occurring of the many LFS-associated cancer types. Cancer screening recommendations for LFS are centered around annual whole-body MRI (wbMRI), the interpretation of which can be challenging. This study aims to characterize sarcoma topography in LFS.MethodsStudy subjects included individuals from clinically and genetically ascertained cohorts of germline TP53 variant-carriers, namely the National Cancer Institute’s LFS longitudinal cohort study (NCI-LFS), the NCI Genetic Epidemiology of Osteosarcoma (NCI-GEO) study, and the germline TP53 Database.ResultsData was aggregated for a total of 160 sarcomas that had detailed topography available. Abdominal sarcomas and extremity osteosarcomas were among the most frequent locations of sarcomas. Chi-squared analyses showed no statistical differences in sarcoma topography based on age (pediatric vs adult) or sex (male vs female). A case series of sarcomas from the NCI-LFS study highlights the diagnostic challenges due to topography-related imaging. DiscussionWhile LFS-related sarcomas frequently occur in expected locations such as the extremities, they also occur in less typical sites, leading to difficulties in discerning between differential diagnoses on wbMRI and imaging. Prospective collection of detailed cancer topography in individuals with LFS will further aid in recommendations for radiologic interpretation and personalized screening in individuals with LFS.

Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Here, the authors present a multinational study on the application of deep learning algorithms for COVID-19 diagnosis against multiple lung conditions as controls.