• Benzekry, Sébastien

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 ofinformation, mathematical models able to produce predictive algorithms and simulations arerequired, with applications for diagnosis, prognosis, drug development or prediction of theresponse 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 orelectronichealthrecords),pharmacometrics,quantitativesystems 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’.

Topics

• impedance spectroscopy
• simulation
• machine learning