July 3, 2019 — 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.
“From my point of view, it can be very easy for patients just give a sample of urine and our ML system can produce a “red flag” analysis result telling them to go immediately to an oncologist for testing,” said Tsigelny. “We believe that a lot of early stages and even more advanced stages of bladder cancer go untreated because patients don’t pay attention to mediate pain signals from the body, and may be thinking that there are less dangerous problems causing the symptoms. Our machine learning model uses metabolites and corresponding genes to determine if a patient has bladder cancer and if so, at what stage.”