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Open AccessFeature PaperReview

Review: Mathematical Modeling of Prostate Cancer and Clinical Application

1
School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ 85281, USA
2
Division of Hematology and Medical Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
3
Arizona Cancer Evolution Center, Arizona State University, Tempe, AZ 85287, USA
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(8), 2721; https://doi.org/10.3390/app10082721
Received: 15 February 2020 / Revised: 7 April 2020 / Accepted: 8 April 2020 / Published: 15 April 2020
(This article belongs to the Special Issue Dynamic Models of Biology and Medicine, Volume II)
We review and synthesize key findings and limitations of mathematical models for prostate cancer, both from theoretical work and data-validated approaches, especially concerning clinical applications. Our focus is on models of prostate cancer dynamics under treatment, particularly with a view toward optimizing hormone-based treatment schedules and estimating the onset of treatment resistance under various assumptions. Population models suggest that intermittent or adaptive therapy is more beneficial to delay cancer relapse as compared to the standard continuous therapy if treatment resistance comes at a competitive cost for cancer cells. Another consensus among existing work is that the standard biomarker for cancer growth, prostate-specific antigen, may not always correlate well with cancer progression. Instead, its doubling rate appears to be a better indicator of tumor growth. Much of the existing work utilizes simple ordinary differential equations due to difficulty in collecting spatial data and due to the early success of using prostate-specific antigen in mathematical modeling. However, a shift toward more complex and realistic models is taking place, which leaves many of the theoretical and mathematical questions unexplored. Furthermore, as adaptive therapy displays better potential than existing treatment protocols, an increasing number of studies incorporate this treatment into modeling efforts. Although existing modeling work has explored and yielded useful insights on the treatment of prostate cancer, the road to clinical application is still elusive. Among the pertinent issues needed to be addressed to bridge the gap from modeling work to clinical application are (1) real-time data validation and model identification, (2) sensitivity analysis and uncertainty quantification for model prediction, and (3) optimal treatment/schedule while considering drug properties, interactions, and toxicity. To address these issues, we suggest in-depth studies on various aspects of the parameters in dynamical models such as the evolution of parameters over time. We hope this review will assist future attempts at studying prostate cancer. View Full-Text
Keywords: prostate cancer; mathematical models; clinical application; mathematical oncology; parameter evolution; personalized medicine; adaptive therapy; optimal schedule; parameter identification; uncertainty quantification prostate cancer; mathematical models; clinical application; mathematical oncology; parameter evolution; personalized medicine; adaptive therapy; optimal schedule; parameter identification; uncertainty quantification
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Phan, T.; Crook, S.M.; Bryce, A.H.; Maley, C.C.; Kostelich, E.J.; Kuang, Y. Review: Mathematical Modeling of Prostate Cancer and Clinical Application. Appl. Sci. 2020, 10, 2721.

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