Development and Application of Models to Predict Cancer Incidence and Prevalence

Dear Colleagues,

Cancer is a major health issue in the 21st century. With the growing worldwide population and trend in life expectancy, knowledge on future healthcare needs in oncology can be a valuable tool for providing information on the patient-specific personal level as well as for policymaking and planning purposes. The aim of the development and application of models to predict cancer incidence and prevalence is to harness advanced statistical, mathematical, as well as computational methods to forecast the occurrence of various types of cancer within populations with increasing precision and confidence. Statistical and mathematical models serve as invaluable tools for healthcare professionals, researchers, and policymakers, providing reliable estimates on trends and offering insights into the impact of biological, medical, or environmental risk or protective factors. High data quality and the choice of adequate modelling techniques, as well as the dissemination of prediction models, increase impact. The development of prediction models should thus not be a task for its own purpose, but should be aligned with the needs of stakeholders, researchers, and practitioners.  

I am pleased to invite you to submit your work on methodological advances as well as current applications in cancer prediction models to this Special Issue of Cancers. The Special Issue will focus on original research articles on methodological advances and applications, as well as reviews. Research areas may include (but are not limited to) the following:

• Statistical and mathematical methods for the prediction of cancer incidence or prevalence.
• Practical application and implementation aspects of prediction models in cancer epidemiology and cancer prevention.
• Computational aspects of prediction (including software packages).
• Machine learning, deep learning, and advanced (big data) methodology (including the explainability of models).
• The influence of incomplete data on prediction (missing data problems).
• Model validation strategies.
• Review articles that illustrate good practices and pitfalls.
• The dissemination of prediction models and results.

I look forward to receiving your contributions.

Dr. Dennis Görlich
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• cancer incidence
• statistical prediction models
• advanced modelling techniques
• model validation
• risk factors
• explainability
• multimodal and/or multiscale models
• software