Ruben Van Dijck

Mr. Ruben Van Dijck

KULeuven
orcid

Research Focus & Metrics

Academic Career Length

10Years (2017-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
26 June 2025 in Blood
Verified
Journal Article
Open access
JBJuan Carlos Hernández Boluda
Juan Carlos Hernández Boluda
AOAdrián Mosquera Orgueira
Adrián Mosquera Orgueira
LGLuuk Gras
Luuk Gras
LKLinda Koster
Linda Koster
JTJoe Tuffnell
Joe Tuffnell
DMDonal P. McLornan
Donal P. McLornan
With the incorporation of effective therapies for myelofibrosis (MF), accurately predicting outcomes after allogeneic hematopoietic cell transplantation (allo-HCT) is crucial for determining the optimal timing for this procedure. Using data from 5183 patients with MF who underwent first allo-HCT between 2005 and 2020 at European Society for Blood and Marrow Transplantation centers, we examined different machine learning (ML) models to predict overall survival after transplant. The cohort was divided into a training set (75%) and a test set (25%) for model validation. A random survival forests (RSF) model was developed based on 10 variables: patient age, comorbidity index, performance status, blood blasts, hemoglobin, leukocytes, platelets, donor type, conditioning intensity, and graft-versus-host disease prophylaxis. Its performance was compared with a 4-level Cox regression–based score and other ML-based models derived from the same data set, and with the Center for International Blood and Marrow Transplant Research score. The RSF outperformed all comparators, achieving better concordance indices across both primary and postessential thrombocythemia/polycythemia vera MF subgroups. The robustness and generalizability of the RSF model was confirmed by Akaike information criterion and time-dependent receiver operating characteristic area under the curve metrics in both sets. Although all models were prognostic for nonrelapse mortality, the RSF provided better curve separation, effectively identifying a high-risk group comprising 25% of patients. In conclusion, ML enhances risk stratification in patients with MF undergoing allo-HCT, paving the way for personalized medicine. A web application (https://gemfin.click/ebmt) based on the RSF model offers a practical tool to identify patients at high risk for poor transplantation outcomes, supporting informed treatment decisions and advancing individualized care.

16 April 2021 in Cancers
Verified
Journal Article
Open access
PDF file
RDRuben Van Dijck
Ruben Van Dijck
JDJeanette K. Doorduijn
Jeanette K. Doorduijn
JBJacoline Bromberg
Jacoline Bromberg
Primary central nervous system lymphoma (PCNSL) is a type of non-Hodgkin lymphoma limited to the central nervous system. It has a poor prognosis. Consensus has been reached on the treatment of newly diagnosed patients with high-dose methotrexate-based chemotherapy, but whether the addition of the monoclonal anti-CD20 antibody rituximab improves survival, as it does in systemic B-cell non-Hodgkin lymphoma, remains disputed. In this review, we reflect on the available evidence of the use of rituximab in PCNSL. Whether rituximab has any beneficial effect remains uncertain.

13 April 2023 in New England Journal of Medicine
Verified
Journal Article
Closed access
RDRuben Van Dijck
Ruben Van Dijck
ASAlice M. Goncalves Silva
Alice M. Goncalves Silva
ARAnita Rijneveld
Anita Rijneveld

6 June 2021 in eJHaem
Verified
Journal Article
Open access
PDF file
RDRuben Van Dijck
Ruben Van Dijck
MLMandy N. Lauw
Mandy N. Lauw
MSMaurice Swinkels
Maurice Swinkels
HRHenk Russcher
Henk Russcher
AJA.J. Gerard Jansen
A.J. Gerard Jansen

5 August 2017 in Annals of Hematology
Verified
Journal Article
Closed access
PDF file
RDRuben Van Dijck
Ruben Van Dijck
AJAnn Janssens
Ann Janssens
DDDaan Dierickx
Daan Dierickx
MDMichel Delforge
Michel Delforge
OGOlivier Gheysens
Olivier Gheysens
GVGregor Verhoef
Gregor Verhoef

29 May 2025 in American Journal of Hematology
Verified
Journal Article
Closed access
RDRuben Van Dijck
Ruben Van Dijck
ZEZoran Erjavec
Zoran Erjavec
JSJohn‐John B. Schnog
John‐John B. Schnog