For prostate cancer screening, new technique may prove effective
the ONA take:
According to new research published in the journal Applied Physics Letters, researchers at Guangdong Medical College in Zhanjiang, China, have designed a new, non-invasive technique to screen for prostate cancer. To create the new method, the team combined surface-enhanced Raman scattering (SERS), a currently used spectroscopy technique, with support vector machine (SVM), a new analysis technique.
The team of researchers identified 68 healthy people and 93 people confirmed to have prostate cancer, and collected blood samples from each patient. Using SERS with SVM, researchers were able to identify cancers with a 98.1% accuracy. The researchers hope to begin clinical trials to prove the technique's safety and effectiveness after refining the method. They also hope that this new method can be used to distinguish cancer staging.
If proven safe and effective, this new method may eventually replace prostate specific antigen (PSA)-based screening, which the U.S. Preventive Services Task Force (USPSTF) now recommends against. Because various factors other than cancer can cause increased PSA levels, the screening method often contributes to over-diagnosis, unnecessary biopsies, and other unnecessary treatment.
Researchers have designed a new, non-invasive technique to screen for prostate cancer.
Cancer screening is a critical approach for preventing cancer deaths because cases caught early are often more treatable. But while there are already existing ways to screen for different types of cancer, there is a great need for even more safe, cheap and effective methods to save even more lives.
Now a team of researchers led by Shaoxin Li at Guangdong Medical College in China has demonstrated the potential of a new, non-invasive method to screen for prostate cancer, a common type of cancer in men worldwide. They describe their laboratory success testing an existing spectroscopy technique called surface-enhanced Raman scattering (SERS) with a new, sophisticated analysis technique called support vector machine (SVM).
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