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

Journal: Annual review of biomedical engineering
PMID: 38594947

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.

Authors

  • Guillermo Lorenzo
    Oden Institute for Computational Engineering and Sciences, University of Texas, Austin, Texas, USA.
  • Syed Rakin Ahmed
    From the Athinoula A. Martinos Center for Biomedical Imaging (M.G., K.C., J.B.P., K.V.H., S.R.A., P.S., J.K.C.) and Department of Radiology (J.K.C.), Massachusetts General Brigham, 13th St, Building 149, Room 2301, Charlestown, MA 02129; Case Western School of Medicine, Cleveland, Ohio (M.G.); Harvard-MIT Division of Health Sciences and Technology, Cambridge, Mass (J.B.P., K.V.H.); Harvard Graduate Program in Biophysics, Harvard Medical School, Harvard University, Cambridge, Mass (S.R.A.); Geisel School of Medicine at Dartmouth, Dartmouth College, Hanover, NH (S.R.A.); and Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Tex (C.D.F.).
  • David A Hormuth
    Livestrong Cancer Institutes, University of Texas, Austin, Texas, USA.
  • Brenna Vaughn
    Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA; email: hectorgomez@purdue.edu.
  • Jayashree Kalpathy-Cramer
    Department of Radiology, MGH/Harvard Medical School, Charlestown, Massachusetts.
  • Luis Solorio
    Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA; email: hectorgomez@purdue.edu.
  • Thomas E Yankeelov
    Department of Biomedical Engineering, The University of Texas at Austin, TX 78712, USA.
  • Hector Gomez
    School of Mechanical Engineering and Purdue Center for Cancer Research, Purdue University, West Lafayette, Indiana, USA.

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