Cancer prognosis: A meaningful combination of methods improves accuracy

Illustration by: Isabella Hoskins

Original paper: Verleyen, W., Langdon, S. P., Faratian, D., Harrison, D. J., & Smith, V. A. (2015). Novel Monte Carlo approach quantifies data assemblage utility and reveals power of integrating molecular and clinical information for cancer prognosis. Scientific reports, 5.

If you want to find out more, here is the paper!

When body cells start to grow abnormally and spread into other tissues and body parts, scientists diagnose it as cancer. Cancer is becoming an increasing health issue with millions of patients being affected each year.
To diagnose and to prognose cancer, there are two main categories of scientific methods: With histopathology, researchers look at cancer cells through a microscope to see how they look and behave. In a different approach, molecular testing, researchers have a detailed look at the u201cinterioru201d of cancer cells e.g. the DNA where all the genetic information is encoded. This method is thought to help develop a more u201cpersonalized medicineu201d that enables doctors to treat each patient individually. However, this method is usually employed without taking data from other methods into account, and hence often overlooks meaningful information. Wim Verleyen and his colleagues analyse data from many cancer patients and show that by using both methods in parallel and combining them meaningfully, cancer diagnosis and prognosis become much more accurate. This improves the survival analysis for cancer patients as well as the selection of medical treatment.

 

Contact the illustrator

Isabella Hoskins

 

 

Contact the researcher

Dr. V. Anne Smith

 

 

 

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