![\"Artificial](\"https:\/\/www.europesays.com\/ai\/wp-content\/uploads\/2026\/06\/GettyImages-1268208483-696x392.jpg\" "\\"1268208483\\"\/")
Credit: Rost-9D \/ iStock \/ Getty Images Plus<\/p>\n Credit: Rost-9D \/ iStock \/ Getty Images Plus<\/p>\n
Researchers at the German Cancer Research Center (DKFZ), Heidelberg University\u2019s Medical Faculty, and Heidelberg University Hospital have developed an AI system called Hetairos that can classify central nervous system (CNS) tumors<\/a> using routinely prepared and stained tissue sections. The research, published in Nature Cancer<\/a>, showed that the system can classify 102 molecular tumor subtypes of CNS cancers using digitized H&E slides, delivering diagnostic findings in minutes rather than the days or weeks often required for molecular testing.<\/p>\n \u201cThe study shows that artificial intelligence is capable of deriving molecular information directly from routine tissue sections and thus fundamentally changing cancer diagnostics,\u201d said lead author Darui Jin, PhD, a postdoctoral fellow at DKFZ.<\/p>\n CNS tumors encompass a broad range of diseases with substantial molecular and morphological diversity. DNA methylation profiling is currently the gold standard for classifying many brain tumors, but the tests require specialized labs employing expensive analytical tools and require sufficient amount of tumor material, which can be difficult to obtain in some instances. Results from these tests typically take about two weeks to return results. The reliance on molecular testing has also created barriers to diagnosis in some clinical settings.<\/p>\n \u201cFaster and more widely accessible methods are therefore needed,\u201d the researchers wrote. Existing alternatives such as nanopore sequencing also require specialized instruments and tissue preparation. Routine H&E histopathology remains the most widely available diagnostic material worldwide.<\/p>\n The development of Hetairos has been made possible from significant advances in computer vision and digital pathology. Prior research has established that AI algorithms can detect molecular features in tissue samples and classify tumors into molecularly defined categories. The researchers noted that previous research has shown that AI models can estimate methylation signals from standard H&E slides and predict specific molecular alterations, but \u201can artificial intelligence (AI)-based diagnostic solution for H&E slides that covers the entire spectrum of CNS tumors, as currently only possible with methylation testing, is still missing.\u201d<\/p>\n To develop Hetairos, the team trained and validated the system using more than 11,000 digitized tissue sections from 9,606 patients treated at 11 medical centers across four continents. Diagnoses used for training were primarily established through DNA methylation diagnostics. The resulting model was designed to classify tumors into 102 molecular subtypes that span nearly the entire WHO classification spectrum for CNS tumors.<\/p>\n An important element of the system is it can estimate the level of confidence of its predictions.<\/p>\n \u201cCrucial to Hetairos\u2019s applicability across cohorts are its realistic confidence estimates, which help judge its prediction accuracy,\u201d the researcher wrote. \u201cDepending on the cohort, Hetairos made high-confidence predictions in 50\u201370% of cases, and those predictions were found to be the correct diagnosis in nearly 90% of instances.\u201d<\/p>\n As it has become custom, the system was also tested directly against human experts. Five board-certified neuropathologists reviewed 210 cases using only tissue sections. Hetairos achieved an accuracy rate of 68%, compared with 30% for the specialists. When the three most likely diagnoses were considered, the AI tool was 84% accurate versus about 50% for the neuropathologists.<\/p>\n \u201cThe results show that modern AI systems are now capable of recognizing extremely subtle morphological patterns that are difficult even for experienced specialists to distinguish,\u201d said Felix Sahm, a project leader at DKFZ.<\/p>\n A prospective evaluation of Hetairos in clinical practice analyzed 210 tumor samples alongside routine diagnostics without, but its results weren\u2019t used to influence patient management. While complete molecular testing required an average of about 12 days, Hetairos generated results in approximately 12 minutes after digitized slides were available. Including slide preparation and scanning, findings could often be produced within 24 hours to two days.<\/p>\n This much speedier diagnosis could help clinicians initiate targeted treatments sooner and guide additional testing, but rather than replacing molecular testing, the developers envision Hetairos as a triage and decision-support tool.<\/p>\n \u201cWe developed Hetairos primarily as a tool to support diagnostics,\u201d Sahm said. \u201cIt is not intended to replace molecular analyses, but rather to specifically complement and accelerate them. The technology could make an important contribution, particularly in countries or regions with limited resources, as it is based on standard tissue sections used worldwide.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"Credit: Rost-9D \/ iStock \/ Getty Images Plus Researchers at the German Cancer Research Center (DKFZ), Heidelberg University\u2019s…\n","protected":false},"author":2,"featured_media":69953,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[24,25,14372,36976,38861,2748,2749,5813,7290,360,4792,38860],"class_list":["post-69952","post","type-post","status-publish","format-standard","has-post-thumbnail","category-ai","tag-ai","tag-artificial-intelligence","tag-diagnosis","tag-dna-methylation","tag-hetairos","tag-informatics","tag-news-features","tag-oncology","tag-pathology","tag-precision-medicine","tag-topics","tag-tumors"],"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/69952","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/comments?post=69952"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/69952\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/69953"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=69952"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=69952"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=69952"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}