AI processing of images can help predict immunotherapy efficacy

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A new study finds that artificial intelligence can process medical images to extract biological and clinical information to aid immunotherapy treatment.

The results, from a French research institute, are important because the research suggests that AI can be used to create a predictive score on the efficacy of immunotherapy in a patient, thus saving time and increasing the chances for success in treatment.

Immunotherapy is the process of using the body’s own immune system to fight the cancer. The goal is for physicians to be able to use imaging to identify biological phenomena in a tumor located in any part of the body without having to perform a biopsy.

By designing an algorithm and developing it to analyze CT scan images, researchers at cancer research institute Gustave Roussy in France and four other institutions have created a “radiomic signature,” which is the extraction of large numbers of features from medical images.

This signature defines the level of lymphocyte infiltration of a tumor where white blood cells leave the bloodstream and migrate toward the tumor to fight it. This then generates a predictive score for the effectiveness of immunotherapy.

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Presently, no marker can accurately identify patients who will respond to anti-PD-1/PD-L1 (proteins) immunotherapy in a situation where only 15 percent to 30 percent of patients will respond. The greater the presence of lymphocytes, the greater the chance that immunotherapy will be effective, according to scientists in the study published in The Lancet Oncology.

With the use of machine learning technology, researchers taught the algorithm that they had designed to analyze CT scan images to use relevant information extracted from scans that held tumor genome data.

“Thus, based soley on images, the algorithm learned to predict what the genome might have revealed about the tumor immune infiltrate, in particular with respect to the presence of cytotoxic T-lymphocytes in the tumor, and it established a radiomic signature,” according to the study.

Additional tests of the signature found that patients in whom immunotherapy was effective at 3 and 6 months had higher scores and better overall survival rates.

Now, a forthcoming additional study will assess the radiomic signature retrospectively and prospectively, and will involve more patients stratified according to cancer type to refine the signature. “This will also employ more sophisticated automatic learning and artificial intelligence algorithms to predict patient response to immunotherapy,” according to the study. “To that end, the researchers are intending to integrate data from imaging, molecular biology and tissue analysis.”

The full study is available here.

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