• The first step in treatment of Brain Tumor is often surgery to remove as much of the mass as possible. • Next a tumor sample obtained and analyzed during surgery can help to precisely diagnose the
• The first step in treatment of Brain Tumor is often surgery to remove as much of the mass as possible.
• Next a tumor sample obtained and analyzed during surgery can help to precisely diagnose the tumor and define the margins between tumor and healthy brain tissue.
• The challenge is that such intraoperative pathology analysis takes time — the sample must be processed, stained, and analyzed by a pathologist while the surgeon and patient wait for the results.
• The new Approach as published in Jan 6 Nature Medicine shows that a process combining an advanced imaging technology and artificial intelligence (AI) can accurately diagnose brain tumors in fewer than 3 minutes during surgery. The approach was also able to accurately distinguish tumor tissue from healthy tissue.
• “This technology is especially encouraging for patients with newly detected tumors and patients with [recurrent tumors] who are undergoing second or third surgeries,”
• The approach — To combine the power of the SRH imager with AI, the researchers began by training an algorithm on SRH-produced images of brain tumor tissue. For the training, 2.5 million tumor tissue images from 415 patients were used. The images covered three nontumor-tissue classifications, including healthy grey or white matter, and the 10 most common brain tumor types, which account for more than 90% of all brain tumor diagnoses in the United States.’
• The challenge — “A major initial challenge was determining the ideal size and resolution of images to train the algorithm,” Dr. Hollon said. Once these ideal parameters were determined, the algorithm learned to classify tissue samples as definitive tumor, nontumor tissue, or nondiagnostic (meaning they couldn’t be analyzed by AI).
• In the clinical trial Tissue specimens were divided into two and analyzed using the new technology (SRH images classified by the algorithm) in the operating room and conventional laboratory pathology (tissues processed, stained, and analyzed by a pathologist) to see if the new technology was as accurate as the conventional technology.
• Results. : The algorithm correctly diagnosed brain tumors 94.6% of the time, while conventional pathologist-based analysis had an overall accuracy rate of 93.9%.
• Conclusion : The researchers noted that the ability of the AI technology and pathologists to cross-check each other highlights the need for pathologists to work alongside the AI technology to interpret challenging cases and ensure the highest diagnostic accuracy possible.
• End Game : The extent of tumor removal can be determined during surgery as well as with a post-operative MRI scan that shows how complete the removal was. While removing as much tumor as possible during surgery can improve how long patients live, removing too much healthy brain tissue during surgery can have serious and harmful consequences for a patient, such as impaired motor function, memory loss, or vision problems.
• The Future : Before the new technology can be expanded to other centers and institutions, “robust testing with more patients and expanding the technology to include rare brain tumors are greatly needed,” Dr. Zaghloul said. The SRH imager is being used at several major cancer centers across the United States today. Both AI and SRH imaging are emerging technologies, so there will be challenges to integrating them into care, Dr. Orringer explained, such as financial or regulatory issues as well as clinician training.
Analysis based on research published at : https://www.cancer.gov/news-events/cancer-currents-blog/2020/artificial-intelligence-brain-tumor-diagnosis-surgery
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