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Annual Review of Biomedical Engineering

Volume 26, 2024

Patient-Specific, Mechanistic Models of Tumor Growth Incorporating Artificial Intelligence and Big Data

Abstract

Despite the remarkable advances in cancer diagnosis, treatment, and management 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 the integration of patient-specific information with 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. We begin this review with 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. We then present illustrative examples of mathematical models manifesting their utility and discuss the limitations of stand-alone mechanistic and data-driven models. We then discuss the potential of mechanistic models for not only predicting but also optimizing response to therapy on a patient-specific basis. We describe 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.

Keyword(s): artificial intelligence, cancer, computational oncology, machine learning, mechanistic models, medical imaging, microscopy, multiscale models, omics, tumor forecasting
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/content/journals/10.1146/annurev-bioeng-081623-025834
2024-07-03
2024-07-04
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/content/journals/10.1146/annurev-bioeng-081623-025834
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Patient-Specific, Mechanistic Models of Tumor Growth Incorporating Artificial Intelligence and Big Data
Annual Review of Biomedical Engineering 26, 529 (2024); https://doi.org/10.1146/annurev-bioeng-081623-025834
/content/journals/10.1146/annurev-bioeng-081623-025834
/content/journals/10.1146/annurev-bioeng-081623-025834
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