Chest X-rays and AI identify non-smokers at risk of lung cancer

Approximately 10-20 per cent of lung cancers occur in so-called ‘never-smokers’, who are people who have never smoked cigarettes or smoked fewer than 100 cigarettes in their lifetime.

Currently, the United States Preventive Services Task Force (USPSTF) does not recommend screening for individuals who have never smoked or who have smoked very little, but the incidence of lung cancer among never-smokers is increasing. When discovered, these cancers tend to be more advanced than those found in smokers.

“Current Medicare and USPSTF guidelines recommend lung cancer screening CT only for individuals with a substantial smoking history,” said the study’s lead author, Anika S. Walia, B.A., a medical student at Boston University School of Medicine and researcher at the Cardiovascular Imaging Research Center (CIRC) at Massachusetts General Hospital (MGH) and Harvard Medical School in Boston. “However, lung cancer is increasingly common in never-smokers and often presents at an advanced stage.”

One reason federal guidelines exclude never-smokers from screening recommendations is because it is difficult to predict lung cancer risk in this population. Existing lung cancer risk scores require information that is not readily available for most individuals, such as family history of lung cancer, pulmonary function testing or serum biomarkers.

For the study, CIRC researchers set out to improve lung cancer risk prediction in never-smokers by testing whether a deep learning model could identify never-smokers at high risk for lung cancer, based on their chest X-rays from the electronic medical record.

“A major advantage to our approach is that it only requires a single chest-X-ray image, which is one of the most common tests in medicine and widely available in the electronic medical record,” Walia said in a statement. 

The CXR-Lung-Risk model was developed using 147,497 chest X-rays of 40,643 asymptomatic smokers and never-smokers from the Prostate, Lung, Colorectal, and Ovarian (PLCO) cancer screening trial - a large randomised controlled trial designed by the US National Cancer Institute - to predict lung-related mortality risk, based on a single chest X-ray image as input.

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According to CIRC, the researchers externally validated the model in a separate group of never-smokers having routine outpatient chest X-rays from 2013 to 2014. The primary outcome was six-year incident lung cancer, identified using International Classification of Disease codes. Risk scores were then converted to low, moderate and high-risk groups based on externally derived risk thresholds.

Of 17,407 patients (with an average of 63 years) included in the study, 28 per cent were deemed high risk by the deep learning model, and 2.9 per cent of these patients later had a diagnosis of lung cancer. The high-risk group exceeded the 1.3 per cent six-year risk threshold where lung cancer screening CT is recommended by US National Comprehensive Cancer Network guidelines.

After adjusting for age, sex, race, previous lower respiratory tract infection and prevalent chronic obstructive pulmonary disease, there was still a 2.1 times greater risk of developing lung cancer in the high-risk group compared to the low-risk group.

“This AI tool opens the door for opportunistic screening for never-smokers at high risk of lung cancer, using existing chest X-rays in the electronic medical record,” said senior author Michael T. Lu, M.D., M.P.H., director of artificial intelligence and co-director of CIRC at MGH. “Since cigarette smoking rates are declining, approaches to detect lung cancer early in those who do not smoke are going to be increasingly important.”  

The study will be presented at the annual meeting of the Radiological Society of North America (RSNA).