[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120234-en":3,"doc-seo-120234-105":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},120234,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Predictive Modeling in Cancer Cell Separation - A Machine Learning Approach in DLD Devices - Master of Science Thesis","Deterministic Lateral Displacement (DLD) devices enable label-free, size-based separation of particles and cells, offering strong value for cancer diagnostics by isolating circulating tumor cells (CTCs) from blood. The thesis targets the difficulty of detecting rare CTCs within abundant blood cells, using geometric parameters—row shift fraction, post size, and gap distance—to differentiate cells by physical properties. It further introduces machine learning models trained on datasets generated by validated numerical simulations to predict particle trajectories and improve separation efficiency, supporting automated DLD device design for scalable, high-throughput cancer cell separation.","PREDICTIVE MODELING IN CANCER CELL SEPARATION:  \nA MACHINE LEARNING APPROACH IN DLD DEVICES  \nBy  \nMD TANBIR SAROWAR  \nA thesis submitted in partial fulfillment of the requirements for the degree of  \nMASTER OF SCIENCE IN MECHANICAL ENGINEERING  \nWASHINGTON STATE UNIVERSITY School of Engineering and Computer Science, Vancouver  \nDECEMBER 2024  \n© Copyright by MD TANBIR SAROWAR, 2024 All Rights Reserved  \n© Copyright by MD TANBIR SAROWAR, 2024 All Rights Reserved  \nTo the Faculty of Washington State University:  \nThe members of the Committee appointed to examine the thesis of MD TANBIR SAROWAR find it satisfactory and recommend that it be accepted.  \nXiaolin Chen, Ph.D., Chair Jong-Hoon Kim, Ph.D.  \nHua Tan, Ph.D.  \nACKNOWLEDGMENT  \nI am deeply grateful to my graduate advisor, Dr. Xiaolin Chen, for her unwavering support, insightful guidance, and constant encouragement throughout my master’s program. Under her mentorship, I gained the courage to explore new ideas without fear of failure.  \nMy sincere thanks to Dr. Jong-Hoon Kim for serving as my committee member and for his inspirational teaching on MEMS course, which greatly contributed to my masters program and the completion of this thesis. I am also thankful to Dr. Hua Tan for his role as my committee member and for his valuable courses on Microfluidicsand CFD, which have been instrumental in my graduate research.  \nLastly, I extend my heartfelt thanks to my parents and my wonderful wife for their continuous support and tireless efforts in standing by me through the many challenges of this journey.  \nPREDICTIVE MODELING IN CANCER CELL SEPARATION:  \nA MACHINE LEARNING APPROACH IN DLD DEVICES  \nAbstract  \nby Md Tanbir Sarowar, M.S.  \nWashington State University  \nDecember 2024  \nChair: Xiaolin Chen  \nDeterministic Lateral Displacement (DLD) devices serve as a powerful tool in the field of microfluidics, enabling label-free, size-based separation of particles and cells. These devices offer significant potential for cancer diagnostics, specifically in isolating circulating tumor cells (CTCs) from blood samples to facilitate early detection and improve patient outcomes. Due to the challenge of identifying rare CTCs among the vastly larger population of blood cells, DLD technology optimizes separation through carefully designed geometric configurations, focusing on parameters such as row shift fraction, post size, and gap distance to effectively differentiate cancer cells based on their unique physical properties. This thesis explores how fine-tuning these parameters in DLD devices can lead to more precise and reliable isolation of lung cancer cells, supporting advancements in early cancer diagnostics.  \nIn addition to DLD design optimization, this study integrates machine learning models to enhance the process of parameter selection, reducing the reliance on exhaustive simulations and physical prototyping. A large dataset, generated through validated numerical models, underpins the training of various machine learning algorithms, including gradient boosting, k-nearest neighbors (kNN), random forest, and MLP regressor, each tailored to predict particle trajectories and improve separation efficiency. These models are not only instrumental in accurately predicting cell migration patterns within the DLD devices but also serve to identify optimal device configurations rapidly, thus enabling high-throughput and cost-effective cancer cell separation.  \nThe application of machine learning in this research extends beyond trajectory prediction; it systematically isolates crucial design parameters essential for advancing DLD technology in cancer research. By analyzing migration characteristics and predicting separation outcomes based on model input, the thesis provides a framework for automated DLD device design, offering a streamlined approach for efficient, scalable, and precise cancer cell separation. Ultimately, this predictive modeling approach, combining the strengths of machine lear","cbCaisfwPsJOZrOV","https://ap.wps.com/l/cbCaisfwPsJOZrOV","pdf",8416563,1,108,"English","en",105,"# Acknowledgment\n# Abstract\n# List of Tables\n# List of Figures\n# Chapter 1 - Introduction\n## Background\n## Circulating Tumor Cells\n## Circulating Tumor Cells Separation Methods\n## Research Motivation\n## Thesis Outline\n# Chapter 2 - Literature Review\n# Chapter 3 - Related Theory\n## Geometric Model\n## Theory\n## Particle Separation Principle in DLD Device\n## Mixed Mode of Particle Transport\n## Sidewall Effects\n## Factors Influencing Critical Diameter\n# Chapter 4 - Methodologies\n## Numerical Modeling\n## Cell Motion in the Channel\n## Volume Force due to Fluid-Cell Interaction\n## Cell-Obstacle Collision Modeling\n## Boundary Conditions","[{\"question\":\"What is the role of DLD devices in cancer cell separation?\",\"answer\":\"DLD devices provide label-free, size-based separation of particles and cells. In this thesis, they are used to isolate circulating tumor cells (CTCs) from blood samples for early cancer diagnostics.\"},{\"question\":\"Which DLD design parameters are optimized in the study?\",\"answer\":\"The work focuses on geometric parameters including row shift fraction, post size, and gap distance. These parameters are tuned to distinguish cancer cells based on their physical properties.\"},{\"question\":\"How does machine learning support the DLD device design process?\",\"answer\":\"Machine learning models are trained on datasets generated from validated numerical simulations. They predict particle trajectories and identify optimal device configurations more rapidly than relying solely on exhaustive simulations and physical prototyping.\"}]","Predictive Modeling in Cancer Cell Separation - A Machine Learning Approach in DLD Devices - Master of Science Thesis | PDF",1785728882,272,{"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},"predictive-modeling-in-cancer-cell-separation-a-machine-learning-approach-in-dld-devices-master-of-science-thesis","",{"@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/predictive-modeling-in-cancer-cell-separation-a-machine-learning-approach-in-dld-devices-master-of-science-thesis/120234/",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","2026-08-03",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 role of DLD devices in cancer cell separation?","Question",{"text":75,"@type":76},"DLD devices provide label-free, size-based separation of particles and cells. In this thesis, they are used to isolate circulating tumor cells (CTCs) from blood samples for early cancer diagnostics.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which DLD design parameters are optimized in the study?",{"text":80,"@type":76},"The work focuses on geometric parameters including row shift fraction, post size, and gap distance. These parameters are tuned to distinguish cancer cells based on their physical properties.",{"name":82,"@type":73,"acceptedAnswer":83},"How does machine learning support the DLD device design process?",{"text":84,"@type":76},"Machine learning models are trained on datasets generated from validated numerical simulations. They predict particle trajectories and identify optimal device configurations more rapidly than relying solely on exhaustive simulations and physical prototyping.","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"]