From Data to Insight - Applying Statistical and Machine Learning Methods to Real-World Data in Esophageal and Gastric Cancer

Aug 5, 2026

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.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages337

Reading Time14 h 9 min

AccessFree to download

General introduction

Part I: Epidemiology of esophageal and gastric cancer

Chapter 1

Chapter 2

Chapter 3

Part II: Health-Related Quality of Life

Chapter 4

Chapter 5

Part III: Clinical trials and real-world data

Chapter 6

Chapter 7

Part IV: Prediction of survival outcomes

Chapter 8

Chapter 9

Chapter 10

Chapter 11

General discussion

Summary

Nederlandse samenvatting

Dankwoord

About the author

UvA-DARE (Digital Academic Repository) From data to insight Applying statistical and machine learning methods to real-world data in esophageal and gastric cancer Kuijper, S.C. Publication date 2025 Document Version Final published version Link to publication Citation for published version (APA): Kuijper, 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] . General rights It 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) . Disclaimer/Complaints regulations If 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. UvA-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)) Download date:07 Jan 2026 From Data to Insight Applying Statistical and Machine Learning Methods to Real-World Data in Esophageal and Gastric Cancer Steven C. Kuijper From Data to Insight: Applying Statistical and Machine Learning Methods to Real-world Data in Esophageal and Gastric Cancer Steven C. Kuijper Colophon Cover art S.C. Kuijper Lay-out S.C. Kuijper Printed by Ridderprint, [www.ridderprint.nl](www.ridderprint.nl) ISBN 978-94-6522-661-3 Financial support for this thesis was kindly provided by the Cancer Center Amsterdam. Copyright © 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. From Data to Insight: Applying Statistical and Machine Learning Methods to Real-world Data in Esophageal and Gastric Cancer ACADEMISCH PROEFSCHRIFT ter verkrijging van de graad van doctor aan de Universiteit van Amsterdam op gezag van de Rector Magnificus [prof. dr. ir. P.P.C.C. Verbeek](prof. dr. ir. P.P.C.C. Verbeek) ten 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 door Steven Coenrad Kuijper geboren te Haarlem Promotiecommissie Promotor: Copromotor: Overige leden: prof. dr. H.W.M. van Laarhoven dr. R.H.A. Verhoeven prof. dr. A. Abu-Hanna prof. dr. J.J.G.H.M. Bergman prof. dr. V.M.H. Coupé prof. dr. E.M.A. Smetsdr. P. S.N. van Rossum dr. M.J. Bijlsma AMC-UvA IKNL AMC-UvA AMC-UvA Vrije Universiteit Amsterdam AMC-UvA Vrije Universiteit Amsterdam Organon Faculteit der Geneeskunde Table of contents General introduction 9 Part I: Epidemiology of esophageal and gastric cancer Chapter 1 21 Treatment and survival of patients with gastric and esophageal cancer in the Netherlands and Belgium: a population-based comparison Chapter 2 49 Trends in best-case, typical and worst-case survival scenarios of patients with non-metastatic esophagogastric cancer between 2006 and 2020: a population-based study Chapter 3 85 Conditional relative survival in non-metastatic esophagogastric cancer between 2006 and 2020: a population-based study Part II: Health-Related Quality of Life Chapter 4 109 Assessing real-world representativeness

FAQs

What is the main goal of the thesis?

To apply statistical and machine learning methods to real-world data in esophageal and gastric cancer to support epidemiology, prediction, and treatment-relevant insights.

Which aspects of the disease are addressed in the thesis?

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.

How does the thesis enhance survival prediction models?

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.

Aug 5, 2026

Upload by Theodora