[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126411-en":3,"doc-seo-126411-105":31,"detail-sidebar-cat-0-en-105":93},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126411,962085571259,"Theodora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","From Data to Insight - Applying Statistical and Machine Learning Methods to Real-World Data in Esophageal and Gastric Cancer","This thesis develops and applies statistical and machine learning methods to real-world evidence in esophageal and gastric cancer. It covers epidemiology and survival through population-based comparisons and relative survival modeling, evaluates how well oncology registry cohorts represent real patients, and predicts health-related quality of life. The work also contrasts therapies using real-world matched comparisons and builds survival prediction approaches integrating clinical variables, radiomics, and tumor-derived circulating cell-free DNA. Results support more accurate decision making across diagnosis, treatment, and follow-up.","UvA-DARE (Digital Academic Repository)  \nFrom data to insight  \nApplying statistical and machine learning methods to real-world data in esophageal and gastric cancer  \nKuijper, S.C.  \nPublication date  \n2025  \nDocument Version  \nFinal published version  \nLink to publication  \nCitation for published version (APA):  \nKuijper, S. C. (2025) . From data to insight: Applying statistical and machine learning methods to real-world data in esophageal and gastric cancer. [Thesis, fully internal, Universiteit van Amsterdam] .  \nGeneral rights  \nIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons) .  \nDisclaimer/Complaints regulations  \nIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: [https://uba.uva.nl/en/contact](https://uba.uva.nl/en/contact), or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible.  \nUvA-DARE is a service provided by the library of the University of Amsterdam ( [http](https://dare. uva. nl)[s](https://dare. uva. nl)[://dare. uva. nl](https://dare. uva. nl))  \nDownload date:07 Jan 2026  \nFrom Data to Insight  \nApplying Statistical and Machine Learning Methods to Real-World Data in Esophageal and Gastric Cancer  \nSteven C. Kuijper  \nFrom Data to Insight: Applying Statistical and Machine Learning Methods to Real-world Data in Esophageal and  \nGastric Cancer  \nSteven C. Kuijper  \nColophon  \nCover art S.C. Kuijper  \nLay-out S.C. Kuijper  \nPrinted by Ridderprint, [www.ridderprint.nl](www.ridderprint.nl)  \nISBN 978-94-6522-661-3  \nFinancial support for this thesis was kindly provided by the Cancer Center Amsterdam.  \nCopyright © Steven C. Kuijper, IJmuiden, The Netherlands, 2025. All rights reserved. No part of this thesis may be reproduced, stored in a retrieval system, or transmitted in any form or by any means without prior permission in writing from the author.  \nFrom Data to Insight: Applying Statistical and Machine Learning Methods to Real-world Data in Esophageal and Gastric Cancer  \nACADEMISCH PROEFSCHRIFT  \nter verkrijging van de graad van doctor  \naan de Universiteit van Amsterdam  \nop gezag van de Rector Magnificus  \n[prof. dr. ir. P.P.C.C. Verbeek](prof. dr. ir. P.P.C.C. Verbeek)  \nten overstaan van een door het College voor Promoties ingestelde commissie, in het openbaar te verdedigen in de Agnietenkapel op dinsdag 14 oktober 2025, te 13.00 uur  \ndoor Steven Coenrad Kuijper  \ngeboren te Haarlem  \nPromotiecommissie  \nPromotor:  \nCopromotor:  \nOverige leden:  \nprof. dr. H.W.M. van Laarhoven dr. R.H.A. Verhoeven  \nprof. dr. A. Abu-Hanna  \nprof. dr. J.J.G.H.M. Bergman  \nprof. dr. V.M.H. Coupé prof. dr. E.M.A. Smetsdr. P. S.N. van Rossum  \ndr. M.J. Bijlsma  \nAMC-UvA  \nIKNL  \nAMC-UvA  \nAMC-UvA  \nVrije Universiteit Amsterdam AMC-UvA  \nVrije Universiteit Amsterdam Organon  \nFaculteit der Geneeskunde  \nTable of contents  \nGeneral introduction 9  \nPart I: Epidemiology of esophageal and gastric cancer  \nChapter 1 21  \nTreatment and survival of patients with gastric and esophageal cancer in the Netherlands and Belgium: a population-based comparison  \nChapter 2 49  \nTrends in best-case, typical and worst-case survival scenarios of patients with non-metastatic esophagogastric cancer between 2006 and 2020: a population-based study  \nChapter 3 85  \nConditional relative survival in non-metastatic esophagogastric cancer between 2006 and 2020: a population-based study  \nPart II: Health-Related Quality of Life  \nChapter 4 109  \nAssessing real-world representativeness","cbCaiqLMRKiWpsjE","https://ap.wps.com/l/cbCaiqLMRKiWpsjE","pdf",6152138,7,1,337,"English","en",105,"# General introduction\n# Part I: Epidemiology of esophageal and gastric cancer\n## Chapter 1\n## Chapter 2\n## Chapter 3\n# Part II: Health-Related Quality of Life\n## Chapter 4\n## Chapter 5\n# Part III: Clinical trials and real-world data\n## Chapter 6\n## Chapter 7\n# Part IV: Prediction of survival outcomes\n## Chapter 8\n## Chapter 9\n## Chapter 10\n## Chapter 11\n# General discussion\n# Summary\n# Nederlandse samenvatting\n# Dankwoord\n# About the author","[{\"question\":\"What is the main goal of the thesis?\",\"answer\":\"To apply statistical and machine learning methods to real-world data in esophageal and gastric cancer to support epidemiology, prediction, and treatment-relevant insights.\"},{\"question\":\"Which aspects of the disease are addressed in the thesis?\",\"answer\":\"The thesis covers survival and epidemiology, representativeness of registry cohorts, prediction of health-related quality of life, therapy comparisons using real-world data, and survival prediction outcomes.\"},{\"question\":\"How does the thesis enhance survival prediction models?\",\"answer\":\"By integrating clinical variables with advanced data sources, including radiomics and tumor-derived circulating cell-free DNA, to improve prediction of outcomes for resectable esophageal adenocarcinoma.\"}]","From Data to Insight - Applying Statistical and Machine Learning Methods to Real-World Data in Esophageal and Gastric Cancer | PDF",1785904917,849,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"from-data-to-insight-applying-statistical-and-machine-learning-methods-to-real-world-data-in-esophageal-and-gastric-cancer","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/from-data-to-insight-applying-statistical-and-machine-learning-methods-to-real-world-data-in-esophageal-and-gastric-cancer/126411/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What is the main goal of the thesis?","Question",{"text":77,"@type":78},"To apply statistical and machine learning methods to real-world data in esophageal and gastric cancer to support epidemiology, prediction, and treatment-relevant insights.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which aspects of the disease are addressed in the thesis?",{"text":82,"@type":78},"The thesis covers survival and epidemiology, representativeness of registry cohorts, prediction of health-related quality of life, therapy comparisons using real-world data, and survival prediction outcomes.",{"name":84,"@type":75,"acceptedAnswer":85},"How does the thesis enhance survival prediction models?",{"text":86,"@type":78},"By integrating clinical variables with advanced data sources, including radiomics and tumor-derived circulating cell-free DNA, to improve prediction of outcomes for resectable esophageal adenocarcinoma.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,117,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":113,"doc_module":4,"doc_module_name":47,"category_name":114,"show_sort_weight":115,"slug":116},6,"Technology",50,"technology",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":108,"slug":139},19,"General","general"]