MONC Team, Inria Bordeaux Sud-Ouest, Talence, France.
Institut de Mathématiques de Bordeaux, CNRS UMR 5251, Bordeaux University, Talence, France.
Clin Pharmacol Ther. 2020 Sep;108(3):471-486. doi: 10.1002/cpt.1951. Epub 2020 Aug 1.
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 and quantitative systems pharmacology, as well as tumor kinetics and metastasis modeling. Focus is set on studies with high potential of clinical translation, and particular attention is given to cancer immunotherapy. Perspectives are given in terms of combinations of the two approaches: "mechanistic learning."
临床肿瘤学中产生的“大数据”量，无论是来自分子、成像、药理学还是生物学来源，都带来了新的挑战。为了有效地挖掘这一信息来源，需要能够生成预测算法和模拟的数学模型，应用于诊断、预后、药物开发或治疗反应预测。这种数学和计算结构可以细分为两类：使用人工智能技术的与生物学无关的统计模型，以及基于生理学的机械模型。在这篇综述中，概述了这些方法在临床肿瘤学中的最新应用进展。这些方法包括应用于大数据（组学、成像或电子健康记录）的机器学习、药代动力学和定量系统药理学，以及肿瘤动力学和转移建模。重点放在具有高临床转化潜力的研究上，并特别关注癌症免疫治疗。从两种方法的组合的角度来看，给出了一些观点：“机械学习”。
