{"id":19750,"date":"2020-02-12T12:00:00","date_gmt":"2020-02-12T12:00:00","guid":{"rendered":"https:\/\/www.cancer.gov\/news-events\/cancer-currents-blog\/2020\/artificial-intelligence-brain-tumor-diagnosis-surgery"},"modified":"2024-11-03T15:16:50","modified_gmt":"2024-11-03T15:16:50","slug":"artificial-intelligence-expedites-brain-tumor-diagnosis-during-surgery","status":"publish","type":"post","link":"https:\/\/ogkologos.com\/en\/artificial-intelligence-expedites-brain-tumor-diagnosis-during-surgery\/","title":{"rendered":"Artificial Intelligence Expedites Brain Tumor Diagnosis during Surgery"},"content":{"rendered":"

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Researchers are using artificial intelligence to quickly analyze images of brain tumor biopsies produced by a technology called stimulated Raman histology (SRH).<\/p>\n

Credit: NYU Langone Health<\/p>\n<\/div>\n<\/figcaption><\/figure>\n<\/div>\n

For patients with a brain tumor, the first step in treatment is often surgery to remove as much of the mass as possible. 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.\u00a0<\/p>\n

However, such intraoperative pathology analysis takes time\u2014the sample must be processed, stained, and analyzed by a pathologist while the surgeon and patient wait for the results. Now, a new study shows that a process combining an advanced imaging technology and artificial intelligence (AI) can accurately diagnose brain tumors in fewer than 3 minutes<\/a> during surgery. The approach was also able to accurately distinguish tumor tissue from healthy tissue.<\/p>\n

The findings were published January 6 in Nature Medicine<\/em>.\u00a0<\/p>\n

\u201cThis technology is especially encouraging for patients with newly detected tumors and patients with [recurrent tumors] who are undergoing second or third surgeries,\u201d said Daniel Orringer, M.D., of NYU Langone Health, who helped lead the study.<\/p>\n

This study, the research team wrote, opens the door to \u201cprovid[ing] unparalleled access to intraoperative tissue diagnosis at the bedside during surgery\u201d while \u201creducing the risk of removing \u2026 normal tissue adjacent to a [tumor].\u201d<\/p>\n

Kareem Zaghloul, M.D., Ph.D., a neurosurgeon in NIH\u2019s Surgical Neurology Branch who was not involved in the research, said he is encouraged by the study\u2019s results. \u201cThis technology could help inform how aggressive or conservative surgery needs to be,\u201d Dr. Zaghloul\u00a0said.<\/p>\n

Applying Imaging and AI Technology<\/h2>\n

In the study, a research team led by Dr. Orringer and Todd Hollon, M.D., chief neurosurgery resident at the University of Michigan, wanted to test whether they could combine an imaging technology called stimulated Raman histology (SRH) with the predictive power of AI to improve current intraoperative pathology practice.\u00a0<\/p>\n

SRH, a specialized form of microscopy<\/a>, can be used to visualize fresh tissue samples directly in the operating room, even producing the same sort of \u201cstaining\u201d that pathologists apply to frozen tissue samples to analyze cellular structure. At the University of Michigan, surgical teams are already using an SRH system for some brain tumor and head and neck cancer procedures.\u00a0<\/p>\n

AI involves using powerful computers to perform tasks that are typically associated with human intelligence. A type of AI known as deep learning uses complex mathematical algorithms, sometimes called convolutional neural networks, to extract features from data that it is then \u201ctrained\u201d on.\u00a0<\/p>\n

This training allows the algorithm to recognize patterns and perform tasks such as analyzing images. In medicine, for example, such algorithms are being studied to see if they can help assess mammograms<\/a>, detect precancerous tissue in the cervix<\/a>, or detect cancerous moles more accurately<\/a>.<\/p>\n

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, they used more than 2.5 million tumor tissue images from 415 patients. The\u00a0images covered\u00a0three nontumor-tissue classifications, including healthy grey or white matter,\u00a0and the 10 most common brain tumor types, which account for more than 90% of all brain tumor diagnoses in the United States.<\/p>\n

\u201cA major initial challenge was determining the ideal size and resolution of images to train the algorithm,\u201d 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\u2019t be analyzed by AI).<\/p>\n

Testing in a Clinical Trial<\/h2>\n

To explore the clinical value of the SRH\u2012AI technology for diagnosing brain tumors, the researchers enrolled nearly 280 patients in a clinical trial, all of whom agreed to allow surgeons to collect additional tumor tissue beyond what would usually be collected but in a way that would not pose additional risks.<\/p>\n

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.\u00a0<\/p>\n

That proved to be the case. The algorithm correctly diagnosed brain tumors 94.6% of the time, while conventional pathologist-based analysis had an overall accuracy rate of 93.9%.\u00a0<\/p>\n

In the instances where tissue specimens were incorrectly classified by the algorithm, a pathologist had made the correct diagnosis. And in the instances where pathologists incorrectly classified a sample, the algorithm had made the correct diagnosis. \u00a0<\/p>\n

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.<\/p>\n

Improving Surgical Accuracy<\/h2>\n

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<\/a>, 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.\u00a0<\/p>\n

To address this, the researchers also looked at the ability of the new technology to distinguish tumor tissue from healthy brain tissue while the patient is still in surgery.\u00a0<\/p>\n

Because tumor cells can sometimes infiltrate healthy tissues, it can be difficult to identify the border between tumor and healthy tissue with the naked eye during surgery. By breaking up each specimen image into smaller \u201cpatches,\u201d the AI technology allows surgeons to quickly and clearly identify areas containing tumor or healthy tissue.<\/p>\n

Using SRH\u2012AI technology, Dr. Orringer said, \u201cwe can \u2026 visualize tumor [cells] that would otherwise be invisible during surgery.\u201d<\/p>\n

And since \u201ca patient\u2019s prognosis is dependent on the extent of resection,\u201d said Dr. Zaghloul, having better information on the brain\u2012tumor margin \u201ccould result in better treatment outcomes for patients and fewer surgery-related complications due to healthy tissue being preserved.\u201d\u00a0<\/p>\n

The Future of AI in Brain Tumors<\/h2>\n

Before the new technology can be expanded to other centers and institutions, \u201crobust testing with more patients and expanding the technology to include rare brain tumors are greatly needed,\u201d Dr. Zaghloul said. \u00a0<\/p>\n

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.<\/p>\n

Even so, Dr. Orringer is hopeful that use of the SRH\u2012AI technology will expand in the future, including at centers with limited pathology resources<\/a> and\u00a0for potential use in a number of different cancer types.<\/p>\n

Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

February 12, 2020, by NCI Staff Researchers are using artificial intelligence to quickly analyze images of brain tumor biopsies produced by a technology called stimulated Raman histology (SRH). Credit: NYU Langone Health For patients with a brain tumor, the first step in treatment is often surgery to remove as much of the mass as possible. […]<\/p>\n","protected":false},"author":2,"featured_media":19751,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"footnotes":""},"categories":[5137],"tags":[],"class_list":["post-19750","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-world-news"],"_links":{"self":[{"href":"https:\/\/ogkologos.com\/en\/wp-json\/wp\/v2\/posts\/19750","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ogkologos.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ogkologos.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ogkologos.com\/en\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/ogkologos.com\/en\/wp-json\/wp\/v2\/comments?post=19750"}],"version-history":[{"count":1,"href":"https:\/\/ogkologos.com\/en\/wp-json\/wp\/v2\/posts\/19750\/revisions"}],"predecessor-version":[{"id":45685,"href":"https:\/\/ogkologos.com\/en\/wp-json\/wp\/v2\/posts\/19750\/revisions\/45685"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/ogkologos.com\/en\/wp-json\/wp\/v2\/media\/19751"}],"wp:attachment":[{"href":"https:\/\/ogkologos.com\/en\/wp-json\/wp\/v2\/media?parent=19750"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ogkologos.com\/en\/wp-json\/wp\/v2\/categories?post=19750"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ogkologos.com\/en\/wp-json\/wp\/v2\/tags?post=19750"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}