Pathline Begins Using GenomOncology Clinical Workbench
News Jul 13, 2016
GenomOncology has announced that Pathline Emerge has recently went live with the GO Clinical Workbench for rapid analysis and reporting of their next-generation sequencing tests. “Pathline Emerge takes a multi-disciplinary approach to evaluate specific types of cancer to support personalized medicine. Our full-service pathology laboratory was one of the first to obtain New York State approval for clinical NGS testing. As the demand for NGS testing and personalized medicine continues to grow and more information becomes available, better solutions are needed to quickly and thoroughly analyze the burgeoning amounts of data,” said Michael Lorenzo, VP of Operations at Pathline Emerge.
“With this in mind, Pathline Emerge partnered with GenomOncology for a software solution, which allows us to streamline our workflow and deliver robust clinical reports. The GO Clinical Workbench will allow Pathline Emerge to scale efficiently while also maintaining compliance.” The GO Clinical Workbench provides a configurable workflow for labs to manage relevant molecular testing results from NGS and other test modes, such as FISH, karyotyping, RT-PCR, IHC, etc. Specifically, labs can perform quality control analysis, evaluate all variants, manage confirmations, amendments, addendums, dynamically generate and sign-out a clinical report.
GenomOncology’s GO Clinical Workbench for analysis and reporting is the flagship product within the GO Precision Medicine Portfolio™, a full suite of tools designed to help healthcare institutions implement, perform, and maximize the benefits of precision medicine. The tools are installed behind the institution’s firewall and are configured to each laboratory’s specific needs.
“Today’s Molecular Pathology lab faces many challenges. Our goal is to enable our Laboratory partners to create value. One key area of value creation is being able to provide Oncologists with reports that provide decision support over integrated data sets. Therapy decisions can change depending on co-occurring genomic aberrations and it’s important for the oncologist to understand these potential changes. For example, combining gene fusion data, with karyotype results, SNV’s and Indels to provide an integrated interpretative report that proposes therapeutic options and suggests appropriate clinical trials,” added Manuel Glynias, President and CEO of GenomOncology.
An artificial intelligence (AI) approach based on deep learning convolutional neural network (CNN) could identify nuanced mammographic imaging features specific for recalled but benign (false-positive) mammograms and distinguish such mammograms from those identified as malignant or negative.
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