Using machine learning models to better predict bladder cancer stages

The invasive and expensive diagnosis process of bladder cancer, which is one of the most common and aggressive cancers in the United States, may be soon helped by a novel non-invasive diagnostic method thanks to advances in machine learning research at the San Diego Supercomputer Center (SDSC), Moores Cancer Center, and CureMatch Incorporated. Research scientists Igor Tsigelny and Valentina Kouznetsova have been working on the development of a machine-learning (ML) model that looks at a patient's metabolites and their chemical descriptors. The model accurately classifies the stages of bladder cancer in a patient, according to the researchers. Tsigelny is the lead author on a recently published study in the Metabolomics journal called'Recognition of Early and Late Stages of Bladder Cancer using Metabolites and Machine Learning'. When a patient experiences early symptoms of bladder cancer (e.g., blood in urine, pain during urination, etc.), the current method of diagnosis is often a painful, invasive series of tests.