Physicians and healthcare workers may one day use a machine learning model, called deep learning, to guide their treatment decisions for lung cancer patients, according to a team of Penn State Great Valley researchers.

In a study, published in International Journal of Medical Informatics, the researchers report that they developed a deep learning model that, in certain conditions, was more than 71% accurate in predicting survival expectancy of lung cancer patients. This rate is significantly better than traditional machine learning models that the team tested. The other machine learning models the team tested had about a 61% accuracy rate.

Information on a patient’s survival expectancy could help guide caregivers in making better decisions on using medicines, allocating resources and determining the intensity of care for patients, according to Youakim Badr, associate professor of data analytics.

“This is a high-performance system that is highly accurate and is aimed at helping doctors make these important decisions about providing care to their patients,” said Badr. “Of course, this tool can’t be used as a substitute for a doctor in making decisions on lung cancer treatments.”

According to Robin Qiu, professor of information science and engineering and an affiliate of the Institute for Computational and Data Sciences, the model can analyze a large amount of data, typically called features in machine learning, that describe the patients and the disease to understand how a combination of factors affect lung cancer survival periods. Features can include information such as types of cancer, size of tumors, the speed of tumor growth and demographic data.

Deep learning may be uniquely suited to tackle lung cancer prognosis because the model can provide the robust analysis necessary in cancer research, according to the researchers, who report their findings in International Journal of Medical Informatics. Deep learning is a type of machine learning that is based on artificial neural networks, which are generally modelled on how the human brain’s own neural network functions.

In deep learning, however, developers apply a sophisticated structure of multiple layers of these artificial neurons, which is why the model is referred to as “deep.” The learning aspect of deep learning comes from how the system learns from connections between data and labels, said Badr.

“Deep learning is a machine-learning algorithm that makes associations between the data, itself, and the labels that we use to describe the data examples,” said Badr. “By making these associations, it learns from the data.”