Korean Researchers Develop AI to Predict Cancer Patients’ Response to Immunotherapy

A research team whose leadership is based in South Korea has developed an AI model for predicting how cancer patients will respond to immunotherapy. Designed to improve the precision of treatment in colorectal and gastric cancer, the AI model could provide physicians with a significantly more accurate tool for customizing therapies.

Immunotherapy is a relatively new approach to cancer treatment that involves “training” the immune system to recognize and destroy malignant cells. In contrast to chemotherapy and radiation which can damage surrounding healthy tissue, immunotherapy allows physicians to more precisely target tumor cells. As a result, many patients undergoing immunotherapy experience fewer of the harsh physical side effects often seen with conventional treatments.

However, immunotherapy isn’t without its drawbacks. Some patients experience immune-related side effects, and the treatment is not consistently effective for all cancer types, including colon cancer. In response, researchers have been working to enhance the accuracy and effectiveness of immunotherapy. With rapid advances in artificial intelligence, a South Korean-led team decided to integrate AI into the process of treating colorectal and gastric cancers.

Yonsei University College of Medicine’s Prof. J. Ho Jeong led the research team that collaborated with the Medical Center at Vanderbilt University and the Mayo Clinic in America. They developed an AI tool that analyzes images of cancer cells to predict whether a patient has a likelihood of benefiting from immune checkpoint inhibitors, medications responsible for activating individuals’ immune systems to seek out and destroy tumors.

The success of immunotherapy often depends on the availability of specific genetic biomarkers, such as MSI-H, or microsatellite instability-high. This biomarker indicates a higher likelihood of a positive response to immunotherapy in some gastrointestinal cancers. However, existing detection methods like immunohistochemical staining frequently fail to identify MSI-H, particularly in situations in which it exists in minimally visible regions of the tumor.

The novel AI model solves this challenge by segmenting pathological images so that the resulting thousands of tiny fragments can be individually analyzed to detect MSI-H. In addition to its precise detection capabilities, the tool can also map the exact distribution and location of MSI-H within a tumor. This gives physicians valuable visual and objective data to guide treatment decisions more effectively.

During clinical validation trials, the AI tool identified MSI-H in several patients who had not qualified for immunotherapy under standard testing methods. These patients ultimately received immune checkpoint inhibitors and showed positive treatment outcomes. Professor Jeong says the AI tool enhances physicians’ ability to personalize cancer care and boosts the accuracy of clinical decisions by providing a clear scientific basis for treatment recommendations.

As more such advances are made and enterprises like Calidi Biotherapeutics Inc. (NYSE American: CLDI) continue to make progress in their efforts to develop novel immunotherapies against cancer, patients can have hope that the diseases they suffer from will be treatable.

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