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Patient-Specific, Mechanistic Models of Tumor Growth Incorporating Artificial Intelligence and Big Data
Guillermo Lorenzo1,2, Syed Rakin Ahmed3,4,5,6, David A Hormuth7,1
1Oden Institute for Computational Engineering and Sciences, University of Texas, Austin, Texas, USA.
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.
Insights
Personalized cancer therapy requires integrating patient data with mathematical models. Overcoming current limitations in tumor modeling theory is crucial for predicting and optimizing individual patient treatment responses.
Area of Science:
- Oncology
- Mathematical Biology
- Computational Science
Background:
- Malignant tumors remain a significant public health challenge despite advances in cancer care.
- Personalized therapy, tailored to individual patient responses, offers a promising avenue for improved cancer treatment outcomes.
Purpose of the Study:
- To review current mathematical modeling approaches for tumor growth and response to therapy.
- To highlight the potential and limitations of mechanistic and data-driven models in personalized cancer care.
- To identify key challenges in developing computational models for patient-specific cancer treatment.
Main Methods:
- Overview of mechanistic and data-driven (big data, artificial intelligence) tumor modeling techniques.
- Illustrative examples of mathematical models applied to tumor growth and treatment response.
- Discussion on integrating mechanistic and data-driven approaches for enhanced predictive and prescriptive capabilities.
Main Results:
- Both mechanistic and data-driven models have utility but also limitations when used independently.
- Mechanistic models show potential for predicting and optimizing patient-specific therapy responses.
- Integration of mechanistic and data-driven models is a key area for future development.
Conclusions:
- A robust mathematical theory for tumor initiation, development, invasion, and therapy response is currently lacking.
- Integrating diverse modeling approaches is essential for advancing personalized cancer care.
- Addressing five fundamental challenges is necessary to fully realize computational model-driven personalized cancer treatment.
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