Meet CHARM: A New Artificial Intelligence AI Tool that can Decode Brain Cancer’s Genome during Surgery for Real-Time Tumor Profiling

In a groundbreaking development, Harvard researchers have unveiled an artificial intelligence (AI) tool capable of rapidly decoding a brain tumor’s DNA during surgery, providing critical information that can significantly impact patient outcomes. This innovative technology, known as CHARM (Cryosection Histopathology Assessment and Review Machine), has the potential to revolutionize the field of neurosurgery by enabling real-time molecular diagnosis, which was previously a time-consuming and challenging process.

The current diagnostic approach used in neurosurgery involves freezing brain tissue and examining it under a microscope. However, this method presents limitations, as freezing can alter cell appearance and impede accurate clinical evaluation. Moreover, detecting subtle genomic variations through microscopic examination remains challenging for human pathologists, even with powerful microscopes.

Enter CHARM, an AI-driven solution to overcome these obstacles and extract valuable biomedical signals from frozen pathology slides. By swiftly determining a tumor’s molecular identity during surgery, neurosurgeons gain crucial insights into the tumor’s aggressiveness and behavior. This information allows them to make informed decisions about the extent of brain tissue removal, a delicate balance between preserving cognitive function and eliminating malignant tissue effectively.

One of the most significant advantages of CHARM is its ability to facilitate the administration of on-the-spot treatment with drug-coated wafers directly into the brain during surgery. This personalized approach is particularly beneficial for certain tumors that respond well to this targeted therapy.

The AI tool was trained using a vast dataset comprising brain tumor samples from different patient populations. CHARM demonstrated remarkable accuracy during testing, successfully distinguishing tumors with specific molecular mutations at 93 percent. Moreover, it accurately classified three significant types of gliomas, the most aggressive and common form of brain cancer, based on their distinct molecular features and prognoses.

CHARM’s capabilities extend beyond molecular profiling, as it also captures visual characteristics of the surrounding tissue, enabling the identification of areas with higher cellular density and increased cell death—a telltale sign of more aggressive gliomas. Additionally, the AI tool can detect clinically important molecular alterations in low-grade gliomas, which are less aggressive and less likely to invade surrounding tissue.

The tool’s success in connecting the appearance of cells to the tumor’s molecular profile further enhances its accuracy and simulates how human pathologists visually assess tumor samples. This multidimensional assessment facilitates a comprehensive understanding of the tumor, ensuring more informed decisions regarding treatment plans.

CHARM’s potential is not limited to gliomas; researchers suggest that it can be retrained to identify other subtypes of brain cancer and potentially even cancers in other organs, such as the colon, lung, and breast. The adaptability of this AI tool will be essential in keeping up with emerging disease classifications and incorporating the latest knowledge to maintain peak performance.

However, before CHARM can be deployed in hospitals for clinical use, it must undergo rigorous validation testing in real-world settings and obtain clearance from regulatory authorities such as the FDA. Nevertheless, its implications for real-time precision oncology are profound, as it opens new avenues for enhancing patient outcomes and optimizing treatment strategies for brain tumor patients.

In conclusion, the advent of CHARM represents a monumental leap forward in neurosurgery. By enabling rapid and accurate molecular diagnosis during surgery, this AI tool empowers neurosurgeons to make crucial decisions that can significantly impact patients’ lives. As further research and validation continue, the future of real-time precision oncology looks brighter than ever, offering hope to countless individuals battling brain tumors and other forms of cancer.

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Niharika is a Technical consulting intern at Marktechpost. She is a third year undergraduate, currently pursuing her B.Tech from Indian Institute of Technology(IIT), Kharagpur. She is a highly enthusiastic individual with a keen interest in Machine learning, Data science and AI and an avid reader of the latest developments in these fields.

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