[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-detail-290462-en":59,"doc-seo-290462-105":81},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":5,"data":60},{"doc_id":61,"user_id":62,"nickname":63,"user_avatar":64,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":66,"doc_content":67,"file_id":68,"file_url":69,"file_type":70,"file_size":71,"view_count":72,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":73,"language":74,"language_code":75,"site_id":76,"html_lang":75,"table_of_contents":77,"faqs":78,"seo_title":79,"seo_description":66,"update_tm":80,"read_time":39,"preview_page_count":4},290462,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908","Role of Machine Learning in Liquid Biopsy of Brain Tumours","Liquid biopsy offers minimally invasive, repeatable options for oncology, yet its neuro-oncology role remains limited. Tumour-derived materials including CTCs, TEPs, cfDNA, ctDNA, miRNA, extracellular vesicles and particles are obtained from CSF, blood (plasma/serum) or urine. Large, complex liquid-biopsy datasets are streamlined through machine learning models to support brain-tumour diagnosis, distinguish low- from high-grade glioma, and separate true progression from pseudo-progression. A literature review summarizes the emerging evidence and future potential.","1194  \nEVIDENCE BASED NEURO-ONCOLOGY  \nRole of Machine Learning in Liquid Biopsy of Brain Tumours  \nZanib Javed, Saqib Kamran Bakhshi, Saad Akhtar Khan, Muhammad Shahzad Shamim  \nAbstract  \nLiquid biopsy has multiple benefits and is used extensively in other fields of oncology, but its role in neuro-oncology has been limited so far. Multiple tumour-derived materials like circulating tumour cells (CTCs), tumour-educated platelets (TEPs), cell-free DNA (cfDNA), circulating tumour DNA (ctDNA), and miRNA are studied in CSF, blood (plasma, serum) or urine. Large and complex amounts of data from liquid biopsy can be simplified by machine learning using various algorithms. By using this technique, we can diagnose brain tumours and differentiate low versus highgrade glioma and true progression from pseudo-progression. The potential of liquid biopsy in brain tumours has not been extensively studied, but it has a bright future in the coming years. Here, we present a literature review on the role of machine learning in liquid biopsy of brain tumours.  \nDOI: [https://doi.org/10.47391/JPMA.24-46](https://doi.org/10.47391/JPMA.24-46)  \nIntroduction  \nMany brain tumours carry significant morbidity for sampling, with some even inaccessible for biopsy. Also, monitoring disease progression and differentiating pseudo-progression requires repeated tissue sampling, which is not feasible.1 The recent 2021 WHO CNS classification, classifies brain tumours based on molecular characteristics rather than histology. Various markers released by tumour tissue in body fluids can be sampled. Liquid biopsy has remarkable benefits, offering a minimally invasive, safer, faster, repeatable and cheaper way to diagnose and monitor malignant diseases. Samples from CSF, blood (plasma, serum), and urine were used for a liquid biopsy. The tumour-derived material used in the analysis includes circulating tumour cells (CTCs), tumour-educated platelets (TEPs), cell-free DNA (cfDNA), circulating tumour DNA (ctDNA), miRNA, extracellular vesicles and particles (EVPs), and serum-derived small extracellular vesicles (EVs) . One of the problems with liquid biopsy is the large amount of data and its complex nature, which can be solved with machine learning (ML) . ML algorithms can identify and analyze large complex data sets and build simple models.  \nSection of Neurosurgery, Department of Surgery, Aga Khan University, Karachi, Pakistan.  \nCorrespondence: Muhammad Shahzad Shamim.  \ne-mail: [shahzad.shamim@aku.edu](shahzad.shamim@aku.edu)[ ](shahzad.shamim@aku.edu)ORCID ID: 0000-0001-8305-8854  \nVarious machine learning algorithms used are random forest (RM), support vector machine (SVM), particle swarm optimization (PSO), principal component analysis (PCA), adaptive boosting (AdaBoost), partial least squaresdiscriminant analysis (PLS-DA), neural networks, decision trees (DT), gradient boosted decision trees, k-nearest neighbor (k-NN), and linear regression (LR) .  \nLiterature Search  \nMorokoff et al., profiled pre- and post-operative serum miRNA in 91 glioma patients and 17 healthy controls using a machine-learning random forest analysis.2 They identified a highly accurate 9-gene miRNA signature that could discriminate between glioma and healthy controls with 99.8% accuracy. Two miRNAs, miR-223 and miR-320e, best demonstrated dynamic changes that linked closely with tumour volume in low-grade glioma (LGG) and Glioblastoma, respectively. Most important, miRNA levels did not increase in two cases of pseudo-progression, implying that miRNA is reflective of the volume of glioma cells in the brain and is not influenced by other changes such as inflammation, necrosis or oedema on MRI that are associated with pseudo-progression.  \nMouliere et al., recruited 35 glioma patients (30 high grade gliomas (HGG), five LGG), 26 healthy volunteers and 27 patients with other pathologies of the central nervous system and studied cfDNA in CSF, urine and plasma using two sequencing-based approaches, pa","cbCaiknNlEPPRePo","https://ap.wps.com/l/cbCaiknNlEPPRePo","pdf",93837,13,3,"English","en",105,"# Abstract\n# Introduction\n# Literature Search","[{\"question\":\"Why is liquid biopsy particularly important in brain tumours?\",\"answer\":\"Many brain tumours are hard or unsafe to sample, and distinguishing pseudo-progression requires repeated tissue sampling. Liquid biopsy provides a minimally invasive, safer, faster, repeatable and cheaper alternative using body-fluid markers.\"},{\"question\":\"Which tumour-derived materials are used in liquid biopsy for brain tumours?\",\"answer\":\"The document lists circulating tumour cells (CTCs), tumour-educated platelets (TEPs), cell-free DNA (cfDNA), circulating tumour DNA (ctDNA), miRNA, extracellular vesicles and particles (EVPs), and serum-derived small extracellular vesicles (EVs).\"},{\"question\":\"How does machine learning improve analysis of liquid biopsy data?\",\"answer\":\"Machine learning algorithms can identify and analyze large complex datasets and build simpler models, enabling diagnostic classification, discrimination between tumour types, and differentiation between true progression and pseudo-progression.\"}]","Role of Machine Learning in Liquid Biopsy of Brain Tumours | PDF",1789641739,{"code":4,"msg":82,"data":83},"ok",{"site_id":76,"language":75,"slug":84,"title":65,"keywords":85,"description":66,"schema_data":86,"social_meta":139,"head_meta":141,"extra_data":143,"updated_unix":80},"role-of-machine-learning-in-liquid-biopsy-of-brain-tumours","",{"@graph":87,"@context":138},[88,101,121],{"@type":89,"itemListElement":90},"BreadcrumbList",[91,95,97,99],{"item":92,"name":93,"@type":94,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":96,"name":9,"@type":94,"position":14},"https://docshare.wps.com/document/",{"item":98,"name":40,"@type":94,"position":73},"https://docshare.wps.com/document/research-report/",{"item":100,"name":65,"@type":94,"position":19},"https://docshare.wps.com/document/role-of-machine-learning-in-liquid-biopsy-of-brain-tumours/290462/",{"url":100,"name":65,"@type":102,"image":103,"author":108,"headline":65,"publisher":110,"fileFormat":113,"inLanguage":75,"description":66,"dateModified":114,"datePublished":115,"encodingFormat":113,"isAccessibleForFree":116,"interactionStatistic":117},"DigitalDocument",{"url":104,"@type":105,"width":106,"height":107},"https://docshare.wps.com/thumbnails/role-of-machine-learning-in-liquid-biopsy-of-brain-tumours/290462.png","ImageObject",300,407,{"name":63,"@type":109},"Person",{"url":92,"name":111,"@type":112},"DocShare","Organization","application/pdf","2026-10-30","2026-09-17",true,{"@type":118,"interactionType":119,"userInteractionCount":72},"InteractionCounter",{"@type":120},"ViewAction",{"@type":122,"mainEntity":123},"FAQPage",[124,130,134],{"name":125,"@type":126,"acceptedAnswer":127},"Why is liquid biopsy particularly important in brain tumours?","Question",{"text":128,"@type":129},"Many brain tumours are hard or unsafe to sample, and distinguishing pseudo-progression requires repeated tissue sampling. Liquid biopsy provides a minimally invasive, safer, faster, repeatable and cheaper alternative using body-fluid markers.","Answer",{"name":131,"@type":126,"acceptedAnswer":132},"Which tumour-derived materials are used in liquid biopsy for brain tumours?",{"text":133,"@type":129},"The document lists circulating tumour cells (CTCs), tumour-educated platelets (TEPs), cell-free DNA (cfDNA), circulating tumour DNA (ctDNA), miRNA, extracellular vesicles and particles (EVPs), and serum-derived small extracellular vesicles (EVs).",{"name":135,"@type":126,"acceptedAnswer":136},"How does machine learning improve analysis of liquid biopsy data?",{"text":137,"@type":129},"Machine learning algorithms can identify and analyze large complex datasets and build simpler models, enabling diagnostic classification, discrimination between tumour types, and differentiation between true progression and pseudo-progression.","https://schema.org",{"og:url":100,"og:type":140,"og:title":65,"og:site_name":111,"og:description":66},"article",{"robots":142,"canonical":100},"index,follow",{"doc_id":61,"site_id":76}]