ArticlePloS one2018
Physiological random processes in precision cancer therapy.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Stochastic models for objects and images in oncology and virology: application to PI3K-Akt-mTOR signaling and COVID-19 disease.Journal of medical imaging (Bellingham, Wash.) · 2021Article
- Generating patient-specific virtual tumor populations with reaction-diffusion models and molecular imaging data.Mathematical biosciences and engineering : MBE · 2020Article
- Growth Rate and Acceleration Analysis of the COVID-19 Pandemic Reveals the Effect of Public Health Measures in Real Time.Frontiers in medicine · 2020Article
- A System Biology-Based Approach for Designing Combination Therapy in Cancer Precision Medicine.BioMed research international · 2020Article
- Is there a role for image science in the brave new world of artificial intelligence?Journal of medical imaging (Bellingham, Wash.) · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Many different physiological processes affect the growth of malignant lesions and their response to therapy. Each of these processes is spatially and genetically heterogeneous; dynamically evolving in time; controlled by many other physiological processes, and intrinsically random and unpredictable. The objective of this paper is to show that all of these properties of cancer physiology can be treated in a unified, mathematically rigorous way via the theory of random processes. We treat each physiological process as a random function of position and time within a tumor, defining the joint statistics of such functions via the infinite-dimensional characteristic functional. The theory is illustrated by analyzing several models of drug delivery and response of a tumor to therapy. To apply the methodology to precision cancer therapy, we use maximum-likelihood estimation with Emission Computed Tomography (ECT) data to estimate unknown patient-specific physiological parameters, ultimately demonstrating how to predict the probability of tumor control for an individual patient undergoing a proposed therapeutic regimen.
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Registered trials
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