AI Reduces False Positives in Lung Cancer CT Screening

AI Reduces False Positives in Lung Cancer Screening

Roxanne Nelson, RN, BSN

March 18, 2019

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.”

Surrounding Vessels Is New Indicator

In this study, the authors investigated if they could improve the prediction of lung cancer by integrating features of an LDCT scan with other clinical data and comorbidities. 

Demographic data, smoking history, comorbidities, and LDCT scan features of lung nodules were drawn from participants of the Pittsburgh Lung Screening Study (PLuSS), a community-based research cohort of 3642 smokers (current or former) recruited in 2002-2006. All participants received a baseline LDCT scan, and the majority also had a follow-up LDCT scan the following year. All participants completed a questionnaire that included questions on smoking history, underwent spirometry for pulmonary function testing, and provided a blood sample. 

The first step was to test a training cohort of 50 participants who had cancer detected on their baseline LDCT scan and 42 others with screen-detected nodules.

Participants ranged in age from 55 to 77 years, had pack years > 30 years and quit < 15 years, and represented a very homogeneous population of individuals at very high risk of lung cancer. Therefore, age, gender, and smoking history were similar in individuals with malignant and benign nodules.