Qlucore Newsletter: Early Subtyping can Reduce Unclassified Lung Tumors by 80%

Unclassified tumors delay analysis and decision-making. With the Qlucore Insights* Lung Cancer Model, the proportion of unclassified tumors can be reduced early in the process, from 5% down to just 1% (1-3).

Qlucore Insights* is built on AI-powered machine learning and transcriptomics data. It delivers fast, cost-effective, and objective sample analysis. It works with standard lab workflows and supports fixed and embedded tissue specimens, making integration seamless. In four out of five cases, metastasis tissue of origin (TOO) is identified earlier, supporting faster and more informed decisions. Qlucore Insights combines classification, gene fusion detection, and TOO analysis, thereby streamlining workflows and reducing turnaround time. By minimizing ambiguity in tumor classification, focus can instead be on actionable insights. 

--> Faster origin identification: In four out of five cases, TOO is identified earlier, speeding up decisions and reducing uncertainty. 

--> Multiple analyses in one test: Classification, gene fusion detection, and TOO identification. 

--> Clearer results, less ambiguity: Automated interpretation minimizes subjectivity. 

Contact us to find out more.

*) Qlucore Insights is intended for research applications. For clinical use, Qlucore Diagnostics BCP-ALL for pediatric leukemia is IVDR-compliant, and a lung cancer version is currently in development. 

Comment on "the proportion of unclassified tumors can be reduced early in the process", from 5% down to just 1%:

This is a conservative estimate based on 3-5% incidence of CUPs and that up to 23% of tumors in the lung are unclassified or fall outside the major classes of lung cancer. Based on the following references: 

1) Kato et al. Cancer of Unknown Primary in the Molecular Era. Trends in Cancer, 2021. 

2) Rassy & Pavlidis. Declining Incidence of Cancer of Unknown Primary. Cancer Epidemiol, 2019. 

3) CDC. Types of Lung Cancer. U.S. Cancer Statistics.