A concept of a prognostic system for personalized anti-tumor therapy based on supermodeling

Dzwinel, Witold; Kłusek, Adrian; Paszyński, Maciej

doi:10.1016/j.procs.2017.05.013

Cited by 8 publications

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another possibility is to rank the models according to their performance and average their predictions [99,249]. In this case, the weight of each model can deal with data uncertainties and model inadequacies either following the frequentist approach, where the probability of an event can be predicted by observing a large dataset [203,250], or the Bayesian approach, which does not require a large dataset and where prior information on parameters guides the posterior distribution of model parameters [204].…”

Section: Limitations Of Model Selection Validation and Uncertainty Qu...mentioning

confidence: 99%

Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology

Lorenzo

Hormuth

et al. 2022

Biophysics Reviews

View full text Add to dashboard Cite

Digital twins employ mathematical and computational models to virtually represent a physical object (e.g., planes and human organs), predict the behavior of the object, and enable decision-making to optimize the future behavior of the object. While digital twins have been widely used in engineering for decades, their applications to oncology are only just emerging. Due to advances in experimental techniques quantitatively characterizing cancer, as well as advances in the mathematical and computational sciences, the notion of building and applying digital twins to understand tumor dynamics and personalize the care of cancer patients has been increasingly appreciated. In this review, we present the opportunities and challenges of applying digital twins in clinical oncology, with a particular focus on integrating medical imaging with mechanism-based, tissue-scale mathematical modeling. Specifically, we first introduce the general digital twin framework and then illustrate existing applications of image-guided digital twins in healthcare. Next, we detail both the imaging and modeling techniques that provide practical opportunities to build patient-specific digital twins for oncology. We then describe the current challenges and limitations in developing image-guided, mechanism-based digital twins for oncology along with potential solutions. We conclude by outlining five fundamental questions that can serve as a roadmap when designing and building a practical digital twin for oncology and attempt to provide answers for a specific application to brain cancer. We hope that this contribution provides motivation for the imaging science, oncology, and computational communities to develop practical digital twin technologies to improve the care of patients battling cancer.

show abstract

Section: Limitations Of Model Selection Validation and Uncertainty Qu...mentioning

confidence: 99%

Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology

Lorenzo

Hormuth

et al. 2022

Biophysics Reviews

View full text Add to dashboard Cite

show abstract

“…Due to multiscale cancer dynamics, which spans from molecular to tissue levels under continuous and unpredictable influence of the environment, the application of the most advanced multiscale tumor models (Deisboeck et al, 2011; Marias et al, 2011) to prognostic and personalized oncology is not realistic. Mainly, due to many principal conceptual and computational problems (Dzwinel et al, 2016a, 2017) including underfitting, ill-conditioning and computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Efficient model of tumor dynamics simulated in multi-GPU environment

Kłusek

Łoś

Paszyński

et al. 2018

The International Journal of High Performance Computing Applica

Self Cite

View full text Add to dashboard Cite

The application of computer simulation as a tool in predicting cancer dynamics (e.g. during anticancer therapy) requires tumor models, which are nontrivial and, simultaneously, not computationally demanding. To this end, both the level of details and computational efficiency of the model should be well balanced. The restrictions on computational time are forced by very demanding data assimilation process in the phase of parameters learning and their correction on the basis of incoming medical data. Herein we present a very efficient multi-GPU/CUDA implementation of three-dimensional (3-D) cancer model which allows for simulating both the growth and treatment phases of tumor dynamics. We demonstrate that the interaction between the tissue and the discrete network of blood vessels is a crucial component, which influences considerably the simulation time. Here we present a new solution which eliminates this flaw. We show also that the efficiency of our model does not depend on the complexity of tumor setup. As an example, we confront the growth of tumor in a simple and homogeneous environment with melanoma evolution, which proliferates in a complex environment of human skin. Consequently, the 3-D simulation of a tumor growth up to 1 cm in diameter requires an hour of computations on a midrange multi-GPU server.

show abstract