Image: New AI-based biomarker can help predict immunotherapy response for patients with lung cancer (Photo courtesy of Emory University)
Immunotherapy is often the first line of treatment for patients with non-small cell lung cancer, which represents 84% of all lung cancers, according to the American Cancer Society. However, most patients don’t achieve durable results from immune checkpoint inhibitor (ICI) therapies, a type of immunotherapy. In a retrospective study, researchers have now discovered a new artificial intelligence (AI)-derived biomarker that uses routine imaging scans to help predict which patients with lung cancer will respond to immunotherapy. The findings not only offer guidance for patients and their physicians making treatment decisions, but can also curtail the financial burden associated with immunotherapy.
The new biomarker, quantitative vessel tortuosity (QVT), was discovered by a team of researchers from several health care systems and universities, including Emory University (Atlanta, GA, USA), and can influence tumor behavior and therapeutic resistance. Tumors appropriate the body’s machinery for building new blood vessels and redirect as much blood as possible to the tumors so they can grow faster and spread throughout the body. Compared to normal blood vessels, tumor-associated vasculature is chaotically arranged and twisted.
The researchers used AI tools to evaluate different aspects of QVT biomarkers in more than 500 cases of patients with non-small cell lung cancer before and after they were treated with ICI therapies. The researchers discovered that the tumor vasculature of patients who do not respond to ICI therapies is more twisted compared to those who do respond. They hypothesize that blood vessel twistedness causes antitumor cells to accumulate at the tumor site but fail to efficiently infiltrate the tumor, diminishing the effectiveness of immunotherapy. In future work, the researchers will seek to validate QVT biomarkers in prospective clinical trials.
“Immunotherapy only tends to benefit approximately 30% of patients. With the high expense of treatments and a 70% failure rate, we have to find better ways to predict and monitor responses to therapy,” says Anant Madabhushi, PhD, study author and professor in the Wallace H. Coulter Department of Biomedical Engineering at Emory University School of Medicine and Georgia Institute of Technology College of Engineering, and member of the Cancer Immunology research program at Winship Cancer Institute of Emory University. “When making decisions on who to treat and how to treat them, clinicians really need interpretable features. Vessel tortuosity is a novel radiomics method that uses an interpretable and intuitive AI approach to evaluate whether the tumor is responding to therapy even before more obvious changes like tumor size become apparent.”
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