[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122798-en":3,"doc-seo-122798-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122798,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Rapid, label-free classification of glioblastoma differentiation status - combining confocal Raman spectroscopy and machine learning - preclinical study","Label-free identification of tumor cells using spectroscopic assays supports rapid clinical implementation, while machine learning improves processing and interpretation of large spectroscopy datasets from surgical samples. This preclinical study evaluates algorithms that combine confocal Raman spectroscopy to distinguish non-differentiated glioblastoma cells from isogenic differentiated phenotypes using ultra-rapid confocal measurements. Data from 1146 intracellular single-point measurements and clustered cell components predict tumor stem cell presence with 91.7% accuracy, and narrow selected Raman peaks. The approach addresses intra- and intertumoral heterogeneity despite intracellular noise limits for future translational evaluation.","Open Access Article . Published on 06 November 2023.  \nhicle is licensed under a Creative C mmons A 3 0 U d[ttr .](ttr .)ibution npor e nce.  \nAnalyst  \nPAPER  \nView Article Online View Journal | View Issue  \nCite this: Analyst, 2023, 148, 6109  \nReceived 30th July 2023,  \nAccepted 23rd October 2023 DOI: 10.1039/d3an01303k[rsc.li/analyst](rsc.li/analyst)  \nRapid, label-free classification of glioblastoma differentiation status combining confocal Raman spectroscopy and machine learning  \nLennard M. Wurm, a,b Björn Fischer,c,d Volker Neuschmelting,b  \nDavid Reinecke,  b Igor Fischer,a Roland S. Croner,e Roland Goldbrunner,b Michael C. Hacker,c Jakub Dybaś †f and Ulf D. Kahlert  *†e  \nLabel-free identification of tumor cells using spectroscopic assays has emerged as a technological innovation with a proven ability for rapid implementation in clinical care. Machine learning facilitates the optimization of processing and interpretation of extensive data, such as various spectroscopy data obtained from surgical samples. The here-described preclinical work investigates the potential of machine learning algorithms combining confocal Raman spectroscopy to distinguish non-differentiated glioblastoma cells and their respective isogenic differentiated phenotype by means of confocal ultra-rapid measurements. For this purpose, we measured and correlated modalities of 1146 intracellular single-point measurements and sustainingly clustered cell components to predict tumor stem cell existence. By further narrowing a few selected peaks, we found indicative evidence that using our computational imaging technology is a powerful approach to detect tumor stem cells in vitro with an accuracy of 91 .7% in distinct cell compartments, mainly because of greater lipid content and putative different protein structures. We also demonstrate that the presented technology can overcome intra-and intertumoral cellular heterogeneity of our disease models, verifying the elevated physiological relevance of our applied disease modeling technology despite intracellular noise limitations for future translational evaluation.  \nA Introduction  \nSurgical resection of the tumor is the most commonly applied treatment for malignant cancers and represents the therapy option leading to the best clinical outcome for most types of cancers when compared to non-surgical intervention plans.1 Moreover, recent predictions reveal a severe increase in demand for surgical treatments in future oncological care due to various socio-economic reasons.2 Technological innovations in surgical oncology, such as robotic-facilitated minimal inva-  \naDepartment of Neurosurgery, University Hospital Düsseldorf and Medical Faculty Heinrich-Heine University, Düsseldorf, Germany  \nbDepartment of Neurosurgery, University Hospital Cologne, Cologne, Germany cInstitute of Pharmaceutics and Biopharmaceutics, University of Düsseldorf, Düsseldorf, Germany  \ndFISCHER GmbH, Raman Spectroscopic Services, 40667 Meerbusch, Germany eClinic of General- Visceral-, Vascular and Transplantation Surgery,  \nDepartment of Molecular and Experimental Surgery, University Hospital Magdeburg and Medical Faculty Otto-von-Guericke University, Magdeburg, Germany. [E-mail: Ulf.Kahlert@med.ovgu.de](E-mail: Ulf.Kahlert@med.ovgu.de)  \nfJagiellonian Center for Experimental Therapeutics, Jagiellonian University, Krakow, Poland  \n†These authors contributed equally.  \nsive surgery or navigation-guided neurosurgery to improve resection outcomes while reducing intervention-associated morbidity and mortality, are of current clinical interest. Our preclinical basic science study strives to provide a tool for such innovations using state-of-the-art instrumental, computational, and disease-modeling technologies.  \nCancer-associated deaths are one of the leading global health problems affecting all levels of society, gender, and ethnicity.3 Over the last decades, research has revealed that the occurrence, progression, and regrowth of malignant cance","cbCaitFxIhsI3Wfq","https://ap.wps.com/l/cbCaitFxIhsI3Wfq","pdf",474635,1,11,"English","en",105,"# Introduction\n## Surgical treatment and unmet needs in glioblastoma\n## Cancer stem cells and limitations in clinical translation\n## Study rationale: detection of GSC at the cellular level","[{\"question\":\"What is the main goal of this study on glioblastoma?\",\"answer\":\"To rapidly classify glioblastoma differentiation status without labels by using confocal Raman spectroscopy combined with machine learning.\"},{\"question\":\"How does the study detect tumor stem cell presence?\",\"answer\":\"It correlates intracellular Raman measurements and clustered cell components, then predicts tumor stem cell existence using the trained computational model.\"},{\"question\":\"What accuracy and biological signals are reported for the Raman-based approach?\",\"answer\":\"Using selected Raman peaks, the method detects tumor stem cells in vitro with 91.7% accuracy across distinct cell compartments, linked mainly to lipid content and protein structure differences.\"}]","Rapid, label-free classification of glioblastoma differentiation status - combining confocal Raman spectroscopy and machine learning - preclinical study | PDF",1785812948,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"rapid-label-free-classification-of-glioblastoma-differentiation-status-combining-confocal-raman-spectroscopy-and-machine-learning-preclinical-study","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/rapid-label-free-classification-of-glioblastoma-differentiation-status-combining-confocal-raman-spectroscopy-and-machine-learning-preclinical-study/122798/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of this study on glioblastoma?","Question",{"text":75,"@type":76},"To rapidly classify glioblastoma differentiation status without labels by using confocal Raman spectroscopy combined with machine learning.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study detect tumor stem cell presence?",{"text":80,"@type":76},"It correlates intracellular Raman measurements and clustered cell components, then predicts tumor stem cell existence using the trained computational model.",{"name":82,"@type":73,"acceptedAnswer":83},"What accuracy and biological signals are reported for the Raman-based approach?",{"text":84,"@type":76},"Using selected Raman peaks, the method detects tumor stem cells in vitro with 91.7% accuracy across distinct cell compartments, linked mainly to lipid content and protein structure differences.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]