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Colorectal cancer remains highly lethal despite advances in treatment, motivating more personalized and ethically relevant experimental models. The work outlines data preparation and cleaning, unsupervised learning to classify organoids by diagnosis, morphology and molecular consensus, and evaluation of drug tolerance by comparing organoid and tumour profiles across similar subgroups.",{"@graph":69,"@context":122},[70,84,105],{"@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":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/pronostic-of-colo-rectal-cancer-crc-using-machine-learning-models-on-organoids-derived-of-patient-poster/124705/",{"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/pronostic-of-colo-rectal-cancer-crc-using-machine-learning-models-on-organoids-derived-of-patient-poster/124705.png","ImageObject",300,407,{"name":92,"@type":93},"Lucas Martin","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-28","2026-08-05",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why are organoids used for CRC research instead of traditional models?","Question",{"text":112,"@type":113},"Organoids are 3D cultures derived from tumour epithelial cells and can better represent cellular diversity and biological stability. They also support personalised therapy exploration while reducing ethical concerns compared with some other approaches.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What is the main goal of the proposed machine learning model?",{"text":117,"@type":113},"To generate a machine learning model that predicts the development and response of CRC patients under different chemotherapy treatments, linking organoid behavior to patient outcomes.",{"name":119,"@type":110,"acceptedAnswer":120},"How does the methodology evaluate chemotherapy response?",{"text":121,"@type":113},"The poster describes collecting biobank tumour data and organoid data, classifying organoids by diagnosis and molecular consensus, and comparing drug tolerance and response patterns between organoids and similarly classified tissues.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},124705,1785894018,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":14,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":24},962084925502,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","CCT College Dublin  \nARC (Academic Research Collection)  \nICT  \n2023  \nPronostic of Colo-Rectal Cancer (CRC) Using Machine Learning Models on Organoids Derived of Patient  \nClaudia Andrea Leiva Acevedo  \nCCT College Dublin  \nFollow this and additional works at: [https://arc.cct.ie/ict](https://arc.cct.ie/ict)  \n Part of the Computer Engineering Commons, and the Medicine and Health Sciences Commons  \nRecommended Citation  \nLeiva Acevedo, Claudia Andrea, \"Pronostic of Colo-Rectal Cancer (CRC) Using Machine Learning Modelson Organoids Derived of Patient\" (2023) . ICT. 47.  \n[https://arc.cct.ie/ict/47](https://arc.cct.ie/ict/47)  \nThis Poster is brought to you for free and open access by ARC (Academic Research Collection) . It has been accepted for inclusion in ICT by an authorized administrator of ARC (Academic Research Collection) . For more information, please [contact](contact debora@cct.ie)[ debora@cct.ie](contact debora@cct.ie).  \nPronostic of Colo-Rectal Cancer (CRC) Using Machine Learning Models on Organoids Derived of Patient  \nAuthor Claudia Andrea Leiva Acevedo  \nSupervisor David McQuaid  \nT. Sato et al., 2009 (Organoid)  \nIntroduction Analysis  \nColorectal Cancer (CRC) is one of the most studied carcinomas due to its high incidence in the world. CRC is the second leading cause of death for women and third for men globally. Despite advances in treatment, the mortality rate remains high at 50% .  \nThere are different biological models to study molecular mechanisms through animals, cell lines and, more recently, organoid model. Organoid is a 3D cell culture, generally produced by epithelial cells derived from tumours which can grow under specific conditions of culture.  \nOrganoid offers the potential to further personalised therapies and also eradicating ethical issues. In comparison to other models, this 3D model offers multiple advantages, including greater diversity of cell cancer, biological stability, genetic modification, and extension of the culture.  \nIn recent years, machine learning models have been used to predict the treatment response of CRC using organoids and their tissue of origin. Considering all of this, evaluate the reaction of organoids and tissues further different treatments could help clarify the comprehension and treatment of future CRC patients.  \n\n| CRC Patient\u003Cbr>\u003Cbr>Biological Models |  | Cell lines\u003Cbr>\u003Cbr>\u003Cbr>Organoid | \u003Cbr>Animal Model\u003Cbr> |  |\n| --- | --- | --- | --- | --- |\n|  |  |  |  Personalised\u003Cbr>treatment |  |\n\nWhat is the percentage of similarity between the tumour and the organoid? Is it possible to find a pattern between people who do not respond to conventional chemotherapy and those who do?  \nResearch Objective  \nGenerate a new Machine Learning Model (ML) capable of predicting the development and response of CRC patients further different chemotherapy treatment.  \nSpecific Objectives  \n1. Prepare and clean data prior applying an unsupervised machine learning model.  \n2. Classify different organoids derived from CRC tissue and previous tissue stored based on diagnosis, morphological characteristics and molecular consensus of CRC.  \n3. Evaluate drug tolerance of organoid, comparing the tissue derived or/and tissue with the same or similar classification.  \nMethodology  \nBiobank  \nCollecting CRC tumour data from Biobank  \nEthical Approval  \nUsing ML classify tumours according to the molecular consensus  \nCollect organoid data from papers of the main research institutions  \nCompare organoid and tumour information  \nAnalyse the response of patients undergoing chemotherapy is similar to the organoid essays  \nThe ethic committee must approve the study. It will also monitor practices during the project develops. A critical step is to obtain informed consents from participants. The privacy, confidentiality and protection of the data will be the responsibility of the biobank and research team to provide the necessary data for the study. Organoids and tissues will be organised according to ","cbCaigOeOTvO3Ihm","https://ap.wps.com/l/cbCaigOeOTvO3Ihm","pdf",378770,"English","# Introduction\n## Biological models and rationale for organoids\n# Research Objective\n## Specific objectives\n# Methodology\n## Biobank and ethical approval\n## Data preparation, modelling and evaluation\n# Comments and Recommendations\n## Model accuracy, organoid biobanks, and future directions","[{\"question\":\"Why are organoids used for CRC research instead of traditional models?\",\"answer\":\"Organoids are 3D cultures derived from tumour epithelial cells and can better represent cellular diversity and biological stability. They also support personalised therapy exploration while reducing ethical concerns compared with some other approaches.\"},{\"question\":\"What is the main goal of the proposed machine learning model?\",\"answer\":\"To generate a machine learning model that predicts the development and response of CRC patients under different chemotherapy treatments, linking organoid behavior to patient outcomes.\"},{\"question\":\"How does the methodology evaluate chemotherapy response?\",\"answer\":\"The poster describes collecting biobank tumour data and organoid data, classifying organoids by diagnosis and molecular consensus, and comparing drug tolerance and response patterns between organoids and similarly classified tissues.\"}]","Pronostic of Colo-Rectal Cancer (CRC) Using Machine Learning Models on Organoids Derived of Patient - Poster | PDF"]