2023/08/28 by Guillermo Lorenzo, Syed Rakin Ahmed, Lorenzo, Guillermo +13 · 1 citation
Biochemistry, Genetics and Molecular Biology · Mathematics · #Artificial Intelligence (cs.AI) #Bioinformatics and Genomic Networks #Cancer Genomics and Diagnostics #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Physical sciences #Mathematical Biology Tumor Growth #Medical Physics (physics.med-ph) #Tissues and Organs (q-bio.TO)
paper · pdf · doi:10.48550/arxiv.2308.14925
openalex publication_date 2023/08/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30
Despite the remarkable advances in cancer diagnosis, treatment, and management that have occurred over the past decade, malignant tumors remain a major public health problem. Further progress in combating cancer may be enabled by personalizing the delivery of therapies according to the predicted response for each individual patient. The design of personalized therapies requires patient-specific information integrated into an appropriate mathematical model of tumor response. A fundamental barrier to realizing this paradigm is the current lack of a rigorous, yet practical, mathematical theory of tumor initiation, development, invasion, and response to therapy. In this review, we begin by providing an overview of different approaches to modeling tumor growth and treatment, including mechanistic as well as data-driven models based on ``big data" and artificial intelligence. Next, we present illustrative examples of mathematical models manifesting their utility and discussing the limitations of stand-alone mechanistic and data-driven models. We further discuss the potential of mechanistic models for not only predicting, but also optimizing response to therapy on a patient-specific basis. We then discuss current efforts and future possibilities to integrate mechanistic and data-driven models. We conclude by proposing five fundamental challenges that must be addressed to fully realize personalized care for cancer patients driven by computational models.