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Objectives: In precision medicine, where oncologic management is tailored to the individual's clinical and genetic profiles, advanced diagnostic testing provides prognostic information and guides management in a growing number of malignancies. There is a need to capture the work pathologists perform to meet this demand by providing medically relevant, timely, and accurate testing results. This work includes not only direct patient consults (interpretation of results and issuing reports) but the administrative and medical oversight as well as the research needed to provide the necessary quality assurance, quality control, direction, and framework for the laboratory.Methods: An expert panel of Canadian pathologists involved in advanced diagnostics was convened to establish and beta test a model for workload assessment in advanced diagnostics.Results: All aspects of the advanced diagnostics workload were detailed and applied to models based on members' experience, including medical oversight, administration, and the introduction of new testing and platforms. Models for biomarker testing were developed for simple and complex or multiplexed assays, and a detailed model was developed to assess the workload for next-generation sequencing-based assays.Conclusions: This paper provides the first detailed proposal for capturing an advanced diagnostic workload to enable appropriate pathologist allotment for performing all the steps required to run an advanced diagnostic service. [ABSTRACT FROM AUTHOR]

Motivation Current fusion detection tools use diverse calling approaches and provide varying results, making selection of the appropriate tool challenging. Ensemble fusion calling techniques appear promising; however, current options have limited accessibility and function. Results MetaFusion is a flexible metacalling tool that amalgamates outputs from any number of fusion callers. Individual caller results are standardized by conversion into the new file type Common Fusion Format. Calls are annotated, merged using graph clustering, filtered and ranked to provide a final output of high-confidence candidates. MetaFusion consistently achieves higher precision and recall than individual callers on real and simulated datasets, and reaches up to 100% precision, indicating that ensemble calling is imperative for high-confidence results. MetaFusion uses FusionAnnotator to annotate calls with information from cancer fusion databases and is provided with a Benchmarking Toolkit to calibrate new callers. Availability and implementation MetaFusion is freely available at https://github.com/ccmbioinfo/MetaFusion. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]