Personalized Prediction Model-Based Treatment Decisions in Cancer Patients
A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Methodology, Drug and Device Discovery".
Deadline for manuscript submissions: 20 July 2025 | Viewed by 70
Special Issue Editor
Special Issue Information
Dear Colleagues,
Cancer is a very heterogeneous disease, where patients, depending on their characteristics, can respond differently to treatment, thereby complicating clinical decision making. Clinical prediction models that combine patient, disease, and even treatment characteristics may facilitate personalized decision making.
Aim and scope: This Special Issue will focus on research on the use of clinical prediction models to facilitate personalized treatment decisions in cancer patients.
History: Typically, clinical prediction research is focused on identifying the combination of predictors that best estimates individual risks of having (diagnosis) or developing (prognosis) a disease outcome. However, when treatment decisions are made based on these models, or when there are changes in predictor values for two alternative treatments, causality and associated issues, e.g., confounding, come into play.
Cutting-edge research: Personalized prediction model-based treatment decisions is a research field that operates at the intersection of prediction modelling and causal inference. More research is needed on how we can estimate, assess, and value potential treatment effects on the level of the individual patient using these (causal?) prediction models.
What kind of paper we are looking for: We are looking for applied examples of and methodological research on the use of clinical prediction models to facilitate personalized treatment decisions in cancer patients.
Dr. Ewoud Schuit
Guest Editor
Manuscript Submission Information
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Keywords
- prediction model
- oncology
- personalized medicine
- precision medicine
- causal inference
- clinical decision making
- heterogeneous treatment effects
- individualized treatment effects
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