Researchers develop AI model to predict cancer patient survival

A team of researchers from the University of British Columbia and BC Cancer have developed an artificial intelligence (AI) model that predicts cancer patient survival more accurately and with more readily available data than previous tools, the Neuroscience News reported.

The report said the model uses natural language processing (NLP)—a branch of AI that understands complex human language—to analyze oncologist notes following a patient’s initial consultation visit—the first step in the cancer journey after diagnosis.

By identifying characteristics unique to each patient, the model was shown to predict six-month, 36-month and 60-month survival with greater than 80 percent accuracy.

“Predicting cancer survival is an important factor that can be used to improve cancer care,” said lead author Dr. John-Jose Nunez, a psychiatrist and clinical research fellow with the UBC Mood Disorders Centre and BC Cancer.

“It might suggest health providers make an earlier referral to support services or offer a more aggressive treatment option upfront. Our hope is that a tool like this could be used to personalize and optimize the care a patient receives right away, giving them the best outcome possible.”

Traditionally, cancer survival rates have been calculated retrospectively and categorized by only a few generic factors such as cancer site and tissue type. Despite familiarity with these rates, it can be challenging for oncologists to accurately predict an individual patient’s survival due to the many complex factors that influence patient outcomes.

The model developed by Dr. Nunez and his collaborators, which includes researchers from BC Cancer and UBC’s departments of computer science and psychiatry, is able to pick up on unique clues within a patient’s initial consultation document to provide a more nuanced assessment. It is also applicable to all cancers, whereas previous models have been limited to certain cancer types.

“The AI essentially reads the consultation document similar to how a human would read it,” said Dr. Nunez. “These documents have many details like the age of the patient, the type of cancer, underlying health conditions, past substance use, and family histories. The AI brings all of this together to paint a more complete picture of patient outcomes.”

The researchers trained and tested the model using data from 47,625 patients across all six BC Cancer sites located across British Columbia. To protect privacy, all patient data remained stored securely at BC Cancer and was presented anonymously. Unlike chart reviews by human research assistants, the new AI approach has the added benefit of maintaining complete confidentiality of patient records.