Artificial Intelligence and Mechanistic Modeling for Clinical Decision Making in Oncology
BENZEKRY, Sébastien
Modélisation Mathématique pour l'Oncologie [MONC]
Institut de Mathématiques de Bordeaux [IMB]
Modélisation Mathématique pour l'Oncologie [MONC]
Institut de Mathématiques de Bordeaux [IMB]
BENZEKRY, Sébastien
Modélisation Mathématique pour l'Oncologie [MONC]
Institut de Mathématiques de Bordeaux [IMB]
< Reduce
Modélisation Mathématique pour l'Oncologie [MONC]
Institut de Mathématiques de Bordeaux [IMB]
Language
en
Article de revue
This item was published in
Clinical Pharmacology and Therapeutics. 2020-06-18
American Society for Clinical Pharmacology and Therapeutics
English Abstract
The amount of ‘big’ data generated in clinical oncology, whether from molecular, imaging, pharmacological or biological origin, brings novel challenges. To mine efficiently this source of information, mathematical models ...Read more >
The amount of ‘big’ data generated in clinical oncology, whether from molecular, imaging, pharmacological or biological origin, brings novel challenges. To mine efficiently this source of information, mathematical models able to produce predictive algorithms and simulations are required, with applications for diagnosis, prognosis, drug development or prediction of the response to therapy. Such mathematical and computational constructs can be subdivided into two broad classes: biologically agnostic, statistical models using artificial intelligence techniques, and physiologically-based, mechanistic models. In this review, recent advances in the applications of such methods in clinical oncology are outlined. These include machine learning applied to big data (omics, imaging or electronic health records), pharmacometrics, quantitative systems pharmacology, tumor size kinetics, and metastasis modeling. Focus is set on studies with high potential of clinical translation, as well as applied to cancer immunotherapy. Perspectives are given in terms of combinations of the two approaches: ‘mechanistic learning’.Read less <
Origin
Hal imported