GE HealthCare Announces Publication on Artificial Intelligence Models Leveraging Routinely Collected Clinical Data to Predict Patient Response to Immunotherapy

A study of GE HealthCare Artificial Intelligence (AI) models, which have demonstrated the ability to predict patients’ responses to immunotherapies with 70 to 80 percent accuracy based on a pan-cancer cohort, leveraged routinely collected clinical data to accurately forecast effectiveness and toxicities of cancer immunotherapy [2], according to an article published in the Journal of Clinical Oncology Clinical Cancer Informatics (JCO CCI).

To the authors’ knowledge, this approach is the first attempt to design AI models capable of assessing the risks and benefits of immunotherapy using only routinely collected electronic health record (EHR) data. A primary advantage of the models used for the study is that inputs are readily available in patients’ medical records, such as diagnosis codes and medication. Only two features– smoking status and number of prior immune checkpoint inhibitor (ICI) drugs – were drawn from manually collected data. These additional features are easily obtainable by clinicians and could be readily entered into the model.

“We focused primarily on this routinely collected structured data to build predictive models with the goal that these models would be able to be implemented in any clinical setting,” said Travis Osterman, DO, MS, Associate Vice President for Research Informatics and Associate Chief Medical Information Officer for Vanderbilt University Medical Center, and Director of Cancer Clinical Informatics at Vanderbilt-Ingram Cancer Center.

To develop the AI models, GE HealthCare and Vanderbilt University Medical Center (VUMC) retrospectively analyzed and correlated the immunotherapy treatment response of thousands of VUMC cancer patients, with their deidentified demographic, genomic, tumor, cellular, proteomic, and imaging data. The models were trained to predict efficacy outcomes and the likelihood of an individual patient developing an adverse reaction, providing information that may help clinicians select the most appropriate treatment pathway sooner while potentially sparing unnecessary side effects and cost.

Immunotherapies use the immune system to recognize and attack cancer cells and can be more effective than traditional treatments, but response rates are often low and side effects can be severe.[3]

With the broad availability of input features, the models have the potential for wide deployment and adoption. GE HealthCare is evaluating plans to commercialize, upon securing applicable regulatory authorization, such models for use both in pharmaceutical drug development and for clinical decision support.

“We aim to partner with pharmaceutical companies, researchers, and clinicians to optimize and ultimately apply the AI models in therapy development and in clinical practice,” said Jan Wolber, Global Product Leader – Digital at GE HealthCare’s Pharmaceutical Diagnostics segment. “We want to use AI to personalize predictions and provide decision support for the clinician in determining appropriate therapies.”

The methodology of these AI models is scalable, with the long-term potential for use in other care areas, such as neurology or cardiology.

AI is central to GE HealthCare’ digital strategy, which is focused on its precision care framework that includes smart devices, a disease-specific focus, and digital solutions. Precision care aims to bring data together in the right place at the right time, so as to improve patient care quality across the care pathway. GE HealthCare is working to unlock the full potential of AI to help solve operational and diagnosis challenges and develop personalized approaches for better patient outcomes.