As the demand for personalized cancer treatment grows, clinical diagnostic labs are facing unprecedented pressure to scale-up, increase test throughput, reduce turnaround times, and deliver comprehensive, oncologist-ready reports that match patients to the most appropriate therapies and clinical trials, in a time-efficient manner.

In this webinar, learn how QIAGEN Clinical Insight (QCI) Interpret for Oncology, the world’s leading variant interpretation and reporting software, can help your lab accelerate your clinical reporting workflow for high-throughput precision oncology NGS testing. The webinar will demonstrate the capabilities and features of QCI Interpret for Oncology through an example workflow for comprehensive genomic profiling. Key highlights include the software’s capability to expedite variant interpretation, dynamically compute pathogenicity and actionability based on the ACMG and AMP/ASCO/CAP guidelines for every variant in over 30,000 cancer types and subtypes with full transparency. Additionally learn how to leverage over 500,000 pre-formulated, oncologist-reviewed variant interpretation summaries to build customizable, oncologist-ready reports with up-to-date gene, variant, diagnostic, prognostic and therapeutic information.

Topics covered in this webinar: 

• QCI Interpret for Oncology’s new performance and scalability enhancements, including bulk variant assessment, flagging of co-occurring variants, and a somatic reporting policy that allows users to set pathogenicity in a phenotype-agnostic manner.
• Learn how the panel- and sequencer-agnostic software can facilitate true scalability by rendering efficient test and process management capabilities, quick identification and comparison of tests from a patient for follow-up.
• How the software’s underlying knowledge base combines the unmatched accuracy and consistency of QIAGEN’s proprietary expert (MD/PhD) curation with the superior efficiency of machine curation (AI-powered) to identify all the literature, treatments, and clinical trials relevant to a patient’s tumor type with just one-click.