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This study evaluates Raman spectroscopy combined with machine learning to identify and characterize precancerous and cancerous cells early. Using Raman spectra from 457 mouse fibroblast cells across three measurement sites, dimensionality reduction and classification were performed with PCA and LDA. Results enabled separation of normal versus abnormal cells with a 93.1% success rate (93.7% normal vs precancerous; 80.2% precancerous vs cancerous) and showed no influence of measurement site on differentiation.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/early-detection-of-pre-cancerous-and-cancerous-cells-using-raman-spectroscopy-based-machine-learning/375588/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/early-detection-of-pre-cancerous-and-cancerous-cells-using-raman-spectroscopy-based-machine-learning/375588.png","ImageObject",300,407,{"name":92,"@type":93},"Eliana","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-25","2026-09-24",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",16,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is the main goal of this study?","Question",{"text":113,"@type":114},"To evaluate whether Raman spectroscopy-based machine learning can identify and characterize precancerous and cancerous cells for early detection.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How were the biological samples and data collected?",{"text":118,"@type":114},"Normal mouse primary fibroblast cells, a precancerous mouse fibroblast line (NIH/3T3), and fully malignant mouse fibroblasts were used. Raman spectra were measured from three sites for each of 457 cells.",{"name":120,"@type":111,"acceptedAnswer":121},"Which machine learning methods were used, and what classification performance was achieved?",{"text":122,"@type":114},"The spectra were analyzed using PCA and LDA. Normal versus abnormal cells were distinguished with 93.1% success (93.7% for normal vs precancerous; 80.2% for precancerous vs cancerous).","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},375588,1790271670,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":145,"read_time":146},4398048949847,"https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267","cells  \nArticle  \nEarly Detection of Pre-Cancerous and Cancerous Cells Using Raman Spectroscopy-Based Machine Learning  \nUraib Sharaha 1,2, Daniel Hania 3, Itshak Lapidot 4,5,†, Ahmad Salman 6, *,† and Mahmoud Huleihel 1, *,†  \nCitation: Sharaha, U.; Hania, D.; Lapidot, I.; Salman, A.; Huleihel, M. Early Detection of Pre-Cancerous and Cancerous Cells Using Raman Spectroscopy-Based Machine Learning. Cells 2023, 12, 1909 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)cells12141909  \nAcademic Editor: Jan G. Hengstler  \nReceived: 7 June 2023  \nRevised: 6 July 2023  \nAccepted: 17 July 2023  \nPublished: 21 July 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Microbiology, Immunology, and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel; [sharaha@post.bgu.ac.il](sharaha@post.bgu.ac.il)  \n2 Department of Biology, Science and Technology College, Hebron University, Hebron P760, Palestine  \n3 Department of Green Engineering, SCE—Shamoon College of Engineering, Beer-Sheva 84100, Israel; [dan308103@gmail.com](dan308103@gmail.com)  \n4 Department of Electrical and Electronics Engineering, ACLP-Afeka Center for Language Processing, Afeka Tel-Aviv Academic College of Engineering, Tel-Aviv 69107, Israel; [itshakl@afeka.ac.il](itshakl@afeka.ac.il)  \n5 Laboratoire Informatique d'Avignon (LIA), Avignon Universit², 339 Chemin des Meinajaries,  \n84000 Avignon, France  \n6 Department of Physics, SCE—Shamoon College of Engineering, Beer-Sheva 84100, Israel  \n* Correspondence: [ahmad@sce.ac.il](ahmad@sce.ac.il) (A.S.); [mahmoudh@bgu.ac.il](mahmoudh@bgu.ac.il) (M.H.); Tel.: +972-8-6475794 (A.S.);+972-8-6479867 (M.H.)  \n† These authors contributed equally to this work.  \nAbstract: Cancer is the most common and fatal disease around the globe, with an estimated 19 million newly diagnosed patients and approximately 10 million deaths annually. Patients with cancer struggle daily due to difficult treatments, pain, and financial and social difficulties. Detecting the disease in its early stages is critical in increasing the likelihood of recovery and reducing the financial burden on the patient and society. Currently used methods for the diagnosis of cancer are time-consuming, producing discomfort and anxiety for patients and significant medical waste. The main goal of this study is to evaluate the potential of Raman spectroscopy-based machine learning for the identification and characterization of precancerous and cancerous cells. As a representative model, normal mouse primary fibroblast cells (NFC) as healthy cells; a mouse fibroblast cell line (NIH/3T3), as precancerous cells; and fully malignant mouse fibroblasts (MBM-T) as cancerous cells were used. Raman spectra were measured from three different sites of each of the 457 investigated cells and analyzed by principal component analysis (PCA) and linear discriminant analysis (LDA) . Our results showed that it was possible to distinguish between the normal and abnormal (precancerous and cancerous) cells with a success rate of 93.1%; this value was 93.7% when distinguishing between normal and precancerous cells and 80.2% between precancerous and cancerous cells. Moreover, there was no influence of the measurement site on the differentiation between the different examined biological systems.  \nKeywords: cancer; Raman spectroscopy; machine learning; normal fibroblast cells (NFC); NIH/3T3; MBM (cancerous cells)  \n1. Introduction  \nCancer comprises a growing health, economic, and social issue [1], accounting for nearly 10 million deaths in 2020, or nearly one in six deaths. For many years, studies of the incide","cbCaivYAhz9Zm9nz","https://ap.wps.com/l/cbCaivYAhz9Zm9nz","pdf",2322723,17,"English","# Abstract\n# Introduction\n## Cancer burden and need for early detection\n## Study goal and approach\n# Materials and Methods\n# Results\n# Discussion\n# Conclusions","[{\"question\":\"What is the main goal of this study?\",\"answer\":\"To evaluate whether Raman spectroscopy-based machine learning can identify and characterize precancerous and cancerous cells for early detection.\"},{\"question\":\"How were the biological samples and data collected?\",\"answer\":\"Normal mouse primary fibroblast cells, a precancerous mouse fibroblast line (NIH/3T3), and fully malignant mouse fibroblasts were used. Raman spectra were measured from three sites for each of 457 cells.\"},{\"question\":\"Which machine learning methods were used, and what classification performance was achieved?\",\"answer\":\"The spectra were analyzed using PCA and LDA. Normal versus abnormal cells were distinguished with 93.1% success (93.7% for normal vs precancerous; 80.2% for precancerous vs cancerous).\"}]","Early Detection of Pre-Cancerous and Cancerous Cells Using Raman Spectroscopy-Based Machine Learning | PDF",1790215439,43]