Supermodeling in predictive diagnostics of cancer under treatment

• Published in: Computers in biology and medicine 2021-10, Vol.137, p.104797-104797, Article 104797
• Main Authors: Dzwinel, Witold, Kłusek, Adrian, Siwik, Leszek
• Format: Article
• Language: English
• Subjects: Algorithms; Anticancer therapy; Cancer predictive system; Cancer therapies; Computer simulation; Data assimilation; Data collection; Decision making; Drug dosages; Efficient tumor model; Medical diagnosis; Oncology; Parameterization; Patients; Remission (Medicine); Supermodeling; Synchronism; Systematic errors
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2021.104797

Classical data assimilation (DA) techniques, synchronizing a computer model with observations, are highly demanding computationally, particularly, for complex over-parametrized cancer models. Consequently, current models are not sufficiently flexible to interactively explore various therapy strategies, and to become a key tool of predictive oncology. We show that, by using supermodeling, it is possible to develop a prediction/correction scheme that could attain the required time regimes and be directly used to support decision-making in anticancer therapies. A supermodel is an interconnected ensemble of individual models (sub-models); in this case, the variously parametrized baseline tumor models. The sub-model connection weights are trained from data, thereby incorporating the advantages of the individual models. Simultaneously, by optimizing the strengths of the connections, the sub-models tend to partially synchronize with one another. As a result, during the evolution of the supermodel, the systematic errors of the individual models partially cancel each other. We find that supermodeling allows for a radical increase in the accuracy and efficiency of data assimilation. We demonstrate that it can be considered as a meta-procedure for any classical parameter fitting algorithm, thus it represents the next – latent – level of abstraction of data assimilation. We conclude that supermodeling is a very promising paradigm that can considerably increase the quality of prognosis in predictive oncology. [Display omitted] •A supermodel is an ensemble of synchronized imperfect computer models of a complex system such as a tumor.•It allows for fast data assimilation and parameters fitting in an overparametrized tumor model.•The supermodel clearly improves the quality of the predictions.•It is an excellent computational framework for planning anti-cancer therapy.