Machine Learning Tool Can Predict Cancer in Patients With Multiple Lung Nodules

Tool outperformed preexisting mathematical models and a previously established artificial intelligence tool.

A machine learning model can predict the probability of cancer in patients with multiple nodules in their lungs, which may help avoid unnecessary invasive procedures including surgery, according to a recent study in the AACR journal Clinical Cancer Research. 

“Current guidelines recommend the use of clinical models that incorporate nodule and sociodemographic features to estimate the probability of cancer prior to surgical treatment, and while there are several tools for patients that present with a single nodule, no such tool currently exists for patients with multiple nodules, representing an urgent medical need,” said study author Kezhong Chen, MD, vice professor in the Department of Thoracic Surgery at Peking University People’s Hospital in China. With the widespread use of computed tomography (CT) scans for lung cancer screening, the detection of multiple nodules is becoming increasingly common, he said.

To address this unmet need, the study authors developed a tool to specifically assess patients with multiple nodules in their lungs to better predict the probability of cancer.

“Because many nodules are found to be benign either after long term follow-up or surgery, it is important to carefully evaluate these nodules prior to invasive procedures,” said Young Tae Kim, MD, PhD, an author of the study and professor in the Department of Thoracic and Cardiovascular Surgery at Seoul National University Hospital and the Seoul National University College of Medicine in the Republic of Korea. “Our prediction model, which was exclusively established for patients with multiple nodules, can help not only mitigate unnecessary surgery but also facilitate the diagnosis and treatment of lung cancer.”

The tool analyzes numerous factors, such as the nodule size, the number and distribution of the nodules, and the age of the patient. It then predicts the probability of cancer. 

The tool was shown to be more accurate than human experts, previously validated mathematical models, and an existing artificial intelligence tool, the authors wrote. 

“Models are developed to assist in clinical diagnosis, which means that they should be practical,” said study author Jun Wang, MD, professor in the Department of Thoracic Surgery at Peking University People’s Hospital. “This tool can quickly generate an objective diagnosis and can aid in clinical decision making.”