Smokers Outlet Flags for AI Tools High-Risk Non-Smokers for Lung Cancer

Smokers Outlet Flags for AI Tools High-Risk Non-Smokers for Lung Cancer

Flags for AI Tools High-Risk Non-Smokers for Lung Cancer

Researchers have also created an AI-deep learning tool that flags non-smokers at high-risk for lung cancer from a single chest X-ray in order to identify the 10% to 20% of lung cancers that occur in people who have never smoked.

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This week at the RSNA meeting, researchers from Massachusetts General Hospital presented data demonstrating the predictive power of the Chest-X-ray (CXR) Lung-Risk tool in a clinical setting. The study included 17,407 patients.

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As the director of artificial intelligence and co-director of the Cardiovascular Imaging Research Center (CIRC) at Massachusetts General Hospital (MGH), senior author Michael Lu, MD, MPH, states, “We do not have a good way of identifying people who are high risk for lung cancer who do not smoke.” At the moment, the instruments doctors are currently based on pack-based smoking history when used clinically.

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However, a greater percentage of lung cancer patients are never smokers, and there is currently no way to detect them before they develop the disease.

With the use of this tool, high-risk patients can be identified from routine chest X-rays; these results may already be recorded in the patient’s electronic medical record.

 

Studies using this tool to predict who is most likely to develop lung cancer in the next six years or so based on smokers’ chest X-rays have already been published by Lu’s team.

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Between 2013 and 2014, a group of never-smokers underwent routine outpatient chest X-rays as part of the current study. Lung cancer that developed after six years was the main result. The risk scores were then divided into low, moderate, and high-risk categories using the CXR tool.

The deep learning model identified 28% of the patients (mean age 63 years) as high-risk, and 2.9% of these patients subsequently received a lung cancer diagnosis. The National Comprehensive Cancer Network guidelines recommend lung cancer screening CT at a risk of 1.3% every six years, but the high-risk group’s risk was higher than that.

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According to Lu, the study discovered that the tool pinpoints a subset of nonsmokers who would be regarded as high-risk individuals. “Since the six-year risk was more than twice as high for that groupf lung cancer, there is compelling evidence to support the use of CT-based lung cancer screening in these people.

 

According to Lu, the tool is not a screening test. Rather, the concept would be to use routine chest X-rays for symptoms like fever, cough, and other ailments to identify these never-smokers who are at high risk of developing lung cancer. CT screening for lung cancer may be beneficial for those patients if you are able to accomplish that.

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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 to predict lung-related mortality risk, based on a single chest X-ray image as input.

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“The only input to this AI model is the chest X-ray,” adds Lu, explaining that the team showed an AI tool many tens of thousands of patients’ X-rays, along with what happened to that person in the following 6-12 years. “By doing that, you can teach these tools to make a prediction about how likely it is that a person will get lung cancer.”

An advantage of the AI tool is that it is reproducible and could sit in an electronic record and scan all the chest X-rays and flag people who it identifies as high-risk and might benefit from additional testing.

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