Paper Info
Title
Supermodeling In Predictive Diagnostics Of Cancer Under Treatment
Abstract
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.
Year
DOI
Venue
2021
10.1016/j.compbiomed.2021.104797
COMPUTERS IN BIOLOGY AND MEDICINE
Keywords
DocType
Volume
Cancer predictive system, Data assimilation, Supermodeling, Efficient tumor model, Anticancer therapy
Journal
137
ISSN
Citations 
PageRank 
0010-4825
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Witold Dzwinel113225.14
Adrian Kłusek200.34
Leszek Siwik36713.85