Qlucore Newsletter: Advancing Lung Cancer Classification

Our new white paper about leveraging machine learning to streamline analysis and improve diagnostic accuracy for lung cancer is available now. Turn knowledge and data into action! Qlucore Insights RUO model for lung cancer samples utilizes molecular data to define subclasses and pinpoint the tissue of origin for lung metastases, offering a new approach to fast, cost-efficient, and objective sample analysis.

Our AI-based machine learning model supports the analysis of lung tumors and lung diseases from fixed and embedded tissue specimens (as well as from fine-needle biopsies). Ensuring ease of use, the solution integrates seamlessly with standard RNA-seq lab workflows.

Performance and Accuracy 

The Qlucore Insights classifier model for lung cancer covers 19 subtypes and includes inbuilt gene fusion analysis to identify both druggable and novel gene fusions. It enables molecular classification of 4 types of primary lung cancers as well as 13 types of metastases!

Overcoming Challenges in Lung Cancer Classification

Lung cancer molecular investigations face numerous challenges, including the need for highly skilled staff, subjective image analysis, and high costs. Our model addresses these issues by utilizing RNA sequencing to detect key genetic alterations and gene expression levels. This enables accurate classification of cancer subtypes and determination of cancer origin.

Learn More 

Discover how you can get actionable results, read our White Paper and stay tuned for upcoming webinars.

24th of April: Automated Tissue of Origin Determination for Lung Lesions with Qlucore Insights

29th of April: Qlucore Diagnostics BCP-ALL