Abstract
Neuroendocrine tumors (NETs) occur sporadically or as part of rare endocrine tumor syndromes (RETS) such as Multiple Endocrine Neoplasia 1 and Von-Hippel Landau syndromes. Due to their relative rarity and lack of model systems, NETs and RETs are difficult to study, hindering advancements in therapeutic development. Causal, or mechanistic mathematical modeling, is widely deployed in disease areas such as breast and prostate cancers, aiding understanding of observations as well as streamlining in vitro and in vivo modeling efforts. Mathematical modeling, while not yet widely utilized in NETs research, offers an opportunity to accelerate NET research and therapy development. To illustrate this, we highlight examples of how mathematical modeling associated with more common endocrine cancers has been successfully used in the preclinical, translational, and clinical settings. We also provide a scope of the limited work that has been done in NETs and map how these techniques can be utilized in NET research to address specific outstanding challenges in the field. Finally, we include practical details such as hardware and data requirements, present advantages and disadvantages of various mathematical modeling approaches, and discuss challenges of using mathematical modeling. Through a cross-disciplinary approach, we believe that many currently difficult problems can be made more tractable by applying mathematical modeling and that the field of rare diseases in endocrine oncology is well-poised to take advantage of these techniques.
Published Version
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