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Sarcoma treatment during the covid-19 pandemic is a new challenge. This patient population is often immunocompromised and potentially more susceptible to viral complications. Government guidelines highlight the need to minimize patient exposure to unnecessary hospital visits. However, those guidelines lack practical recommendations on ways to manage triage and diagnosis expressly for new cancer patients. Furthermore, there are no reports on the efficiency of the guidelines. One of the main issues in treating musculoskeletal tumours is the complexity and variability of presentation. We offer a triage model, used in a quaternary-referral musculoskeletal oncology centre, that allows us to maintain an open pathway for referral of new patients while minimizing exposure risks. A multidisciplinary approach and analysis of existing investigations allow for a pre-clinic evaluation. The model identifies 3 groups of patients: ? Patients with suspected high-grade malignancy, or benign cases with aggressive features, both in need of further evaluation in the clinic and prompt treatment ? Patients with low-grade malignancy, and benign cases whose treatment is not urgent, that are managed during the pandemic by telemedicine, with reassurance and information about their illness ? Patients who can be managed by their local medical professionals. In comparison to a pre-pandemic period, that approach resulted in a higher ratio of malignant-to-benign conditions for new patients seen in the clinic (3:4 vs. 1:3 respectively), thus using available resources more efficiently and prioritizing patients with suspected high-grade malignancy. We believe that this triage system could be applied in other surgical oncology fields during a pandemic. [ABSTRACT FROM AUTHOR]

Sarcoma classification is challenging and can lead to treatment delays. Previous studies used DNA aberrations and machine-learning classifiers based on methylation profiles for diagnosis. We aimed to classify sarcomas by analyzing methylation signatures obtained from low-coverage whole-genome sequencing, which also identifies copy-number alterations. DNA was extracted from 23 suspected sarcoma samples and sequenced on an Oxford Nanopore sequencer. The methylation-based classifier, applied in the nanoDx pipeline, was customized using a reference set based on processed Illumina-based methylation data. Classification analysis utilized the Random Forest algorithm and t-distributed stochastic neighbor embedding, while copy-number alterations were detected using a designated R package. Out of the 23 samples encompassing a restricted range of sarcoma types, 20 were successfully sequenced, but two did not contain tumor tissue, according to the pathologist. Among the 18 tumor samples, 14 were classified as reported in the pathology results. Four classifications were discordant with the pathological report, with one compatible and three showing discrepancies. Improving tissue handling, DNA extraction methods, and detecting point mutations and translocations could enhance accuracy. We envision that rapid, accurate, point-of-care sarcoma classification using nanopore sequencing could be achieved through additional validation in a diverse tumor cohort and the integration of methylation-based classification and other DNA aberrations.