Improved model prediction of glioma growth utilizing tissue-specific boundary effects

Jacobs, Joshua J.; Rockne, Russell C.; Hawkins-Daarud, Andrea; Jackson, Pamela; Johnston, Sandra K.; Kinahan, Paul E.; Swanson, Kristin R.

doi:10.1016/j.mbs.2019.04.004

Cited by 18 publications

(23 citation statements)

References 27 publications

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“…In temporal order the most common three-parameter growth models in avian research [33] are the Gompertz [34] model, the logistic model of Verhulst [35,36], the monomolecular model (bounded exponential growth) of Brody [37], the von Bertalanffy [38,39] model, and the more recent West model [40,41]. There are also simpler trend models, such as power-laws between size and age [42], and more complex models explaining growth in relation to food consumption [43] or describing spatial characteristics of growth by partial differential equations [44].…”

Section: Nonlinear Trend Modelsmentioning

confidence: 99%

Bertalanffy-Pütter models for avian growth

2021

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This paper explores the ratio of the mass in the inflection point over asymptotic mass for 81 nestlings of blue tits and great tits from an urban parkland in Warsaw, Poland (growth data from literature). We computed the ratios using the Bertalanffy-Pütter model, because this model was more flexible with respect to the ratios than the traditional models. For them, there were a-priori restrictions on the possible range of the ratios. (Further, as the Bertalanffy-Pütter model generalizes the traditional models, its fit to the data was necessarily better.) For six birds there was no inflection point (we set the ratio to 0), for 19 birds the ratio was between 0 and 0.368 (lowest ratio attainable for the Richards model), for 48 birds it was above 0.5 (fixed ratio of logistic growth), and for the remaining eight birds it was in between; the maximal observed ratio was 0.835. With these ratios we were able to detect small variations in avian growth due to slight differences in the environment: Our results indicate that blue tits grew more slowly (had a lower ratio) in the presence of light pollution and modified impervious substrate, a finding that would not have been possible had we used traditional growth curve analysis.

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Section: Nonlinear Trend Modelsmentioning

confidence: 99%

Bertalanffy-Pütter models for avian growth

2021

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“…In the typical timeline of fit and evaluation [14,17], described in figure 2 as the bidirectional scheme, the model is fitted on a tumor segmentation S 0 and then simulated from onset, through S 0 , to the point of evaluation S 2 . In other words, the prediction contains the behavior that it is fitted on.…”

Section: Experimental Designmentioning

confidence: 99%

“…where κ and τ are parameters to weigh the two components, I is the identity matrix, F (x) is the local Fractional Anisotropy (FA) and T is the normalized diffusion tensor [11]. The isotropic diffusion depends on the local tissue type [14], as defined by a separate parameter κ w and κ g for voxels in the white matter (W) and grey matter (G) respectively:…”

Section: Growth Modelsmentioning

confidence: 99%

“…Running a growth model from onset requires knowledge of the underlying healthy tissue. Removing pathology from an image is a research problem in itself, but commonly a registration approach with a healthy brain -often an atlas -is used [5,18,14]. We propose to not use an atlas but rather the contralateral side of the brain as a reference for healthy brain structure (similar to [6]).…”

Section: Healthy Brain Structurementioning

confidence: 99%

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Evaluating glioma growth predictions as a forward ranking problem

Garderen¹,

Voort²,

Wijnenga³

et al. 2021

Preprint

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The problem of tumor growth prediction is challenging, but promising results have been achieved with both model-driven and statistical methods. In this work, we present a framework for the evaluation of growth predictions that focuses on the spatial infiltration patterns, and specifically evaluating a prediction of future growth. We propose to frame the problem as a ranking problem rather than a segmentation problem. Using the average precision as a metric, we can evaluate the results with segmentations while using the full spatiotemporal prediction. Furthermore, by separating the model goodness-of-fit from future predictive performance, we show that in some cases, a better fit of model parameters does not guarantee a better the predictive power.

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“…To the best of our knowledge the estimation of the tumor shape over time via a sequential approach has not been addressed before. One can found simple trial-error strategies, focusing on the parameter estimation and not on the state -shape -estimation [18,19], variational approaches [17] or through approximate analytical solutions with a reduced model [20]. The goal of this paper is to present joint state-parameter estimation strategy for a hyperbolic-elliptic tumor growth model, to combine imaging data (MRI or CT scans) with the model to improve the predictive potential of the simulation and to quantify the norm of the estimation.…”

Section: Introductionmentioning

confidence: 99%

Joint State-Parameter Estimation for Tumor Growth Model

Collin¹,

Kritter²,

Poignard³

et al. 2021

SIAM J. Appl. Math.

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We present a shape-oriented data assimilation strategy suitable for front-tracking tumor growth problems. A general hyperbolic/elliptic tumor growth model is presented as well as the available observations corresponding to the location of the tumor front over time extracted from medical imaging as MRI or CT scans. We provide sufficient conditions allowing to design a state observer by proving the convergence of the observer model to the target solution, for exact parameters. In particular, the similarity measure chosen to compare observations and simulation of tumor contour is presented. A specific joint stateparameter correction with a Luenberger observer correcting the state and a reduced-order Kalman filter correcting the parameters is introduced and studied. We then illustrate and assess our proposed observer method with synthetic problems. Our numerical trials show that state estimation is very effective with the proposed Luenberger observer, but specific strategies are needed to accurately perform parameter estimation in a clinical context. We then propose strategies to deal with the fact that data is very sparse in time and that the initial distribution of the proliferation rate is unknown. The results on synthetic data are very promising and work is ongoing to apply our strategy on clinical cases.

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Improved model prediction of glioma growth utilizing tissue-specific boundary effects

Cited by 18 publications

References 27 publications

Bertalanffy-Pütter models for avian growth

Bertalanffy-Pütter models for avian growth

Evaluating glioma growth predictions as a forward ranking problem

Joint State-Parameter Estimation for Tumor Growth Model

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