A new machine learning algorithm outperformed existing methods in differentiating benign nodules from cancer.
The use of artificial intelligence (AI) may help reduce false positive rates in lung cancer screening while not missing any cases of actual cancer, according to new findings.
Researchers at the University of Pittsburgh have developed a new lung cancer predictor that outperformed existing methods in differentiating cancer from benign nodules.
“We incorporated a machine learning algorithm to see which features would allow us to predict cancer, or more importantly, no cancer,” said senior author David Wilson, MD, MPH, codirector of the Lung Cancer Center at UPMC Hillman, Pittsburgh, Pennsylvania. “What we found was that by using our algorithm, we could reliably eliminate cancer in about 30% of these indeterminate nodules.”
The study was published online March 12 in Thorax.
Screening for lung cancer using low-dose computed tomography (LDCT) is recommended by the US Preventive Services Task Force for certain groups at high risk for the disease.
However, a major problem with LDCT screening is the high rate of false positives. About a quarter (24%) of LDCT screening exams produce a positive result that requires follow-up, but 96% of these findings are false positives.
“Even if we change our definition of what constitutes a positive screen, such as by limiting it to a large nodule, there will still be many that do not represent cancer,” Wilson said. “It will still cause anxiety, the need for follow-up scans, and invasive biopsies.”
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