Radiation protraction schedules for low-grade gliomas: a comparison between different mathematical models

Budia, Irene; Álvarez-Arenas, Arturo; Woolley, Thomas E.; Calvo, Gabriel F.; Belmonte-Beitia, Juan

doi:10.1098/rsif.2019.0665

Cited by 8 publications

(10 citation statements)

References 58 publications

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“…Several aspects of DLGGs have already been the objects of models, from their origin [ 15 ] to their natural evolution [ 11 , 16 ], their response to treatments (in particular, with RT [ 17 , 18 , 19 , 20 , 21 ]), and their anaplastic transformation [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

The Effect of Radiotherapy on Diffuse Low-Grade Gliomas Evolution: Confronting Theory with Clinical Data

Adenis

Plaszczynski

Grammaticos

et al. 2021

JPM

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Diffuse low-grade gliomas are slowly growing tumors that always recur after treatment. In this paper, we revisit the modeling of the evolution of the tumor radius before and after the radiotherapy process and propose a novel model that is simple yet biologically motivated and that remedies some shortcomings of previously proposed ones. We confront this with clinical data consisting of time series of tumor radii from 43 patient records by using a stochastic optimization technique and obtain very good fits in all cases. Since our model describes the evolution of a tumor from the very first glioma cell, it gives access to the possible age of the tumor. Using the technique of profile likelihood to extract all of the information from the data, we build confidence intervals for the tumor birth age and confirm the fact that low-grade gliomas seem to appear in the late teenage years. Moreover, an approximate analytical expression of the temporal evolution of the tumor radius allows us to explain the correlations observed in the data.

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Section: Introductionmentioning

confidence: 99%

The Effect of Radiotherapy on Diffuse Low-Grade Gliomas Evolution: Confronting Theory with Clinical Data

Adenis

Plaszczynski

Grammaticos

et al. 2021

JPM

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“…Following the conventional RT administration schedule, consisting of 30 fractions of 1.8 Gy given from Monday to Friday for six consecutive weeks [ 21 ], we fixed the radiation dose

Gy. The parameters

(1.8 Gy) and

(1.8 Gy) and the initial populations

and

were fitted for each patient’s volumetric data using the Matlab (R2020b, The MathWorks, Inc., Natick, MA, USA) function fmincon.…”

Section: Materials and Methodsmentioning

confidence: 99%

“…A number of mathematical studies have constructed in-silico models to determine the optimal delivery scheme for either radiation therapy [ 16 , 17 , 18 , 19 , 20 , 21 ] or chemotherapy [ 22 , 23 , 24 , 25 , 26 , 27 ] alone. However, although mathematical models have potential for the study of optimal combination treatments [ 28 ], no studies have addressed computationally the question of what might be the best combination scheme of TMZ and RT for LGGs.…”

Section: Introductionmentioning

confidence: 99%

Optimal Combinations of Chemotherapy and Radiotherapy in Low-Grade Gliomas: A Mathematical Approach

Ayala-Hernández

Gallegos

Schucht³

et al. 2021

JPM

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Low-grade gliomas (LGGs) are brain tumors characterized by their slow growth and infiltrative nature. Treatment options for these tumors are surgery, radiation therapy and chemotherapy. The optimal use of radiation therapy and chemotherapy is still under study. In this paper, we construct a mathematical model of LGG response to combinations of chemotherapy, specifically to the alkylating agent temozolomide and radiation therapy. Patient-specific parameters were obtained from longitudinal imaging data of the response of real LGG patients. Computer simulations showed that concurrent cycles of radiation therapy and temozolomide could provide the best therapeutic efficacy in-silico for the patients included in the study. The patient cohort was extended computationally to a set of 3000 virtual patients. This virtual cohort was subject to an in-silico trial in which matching the doses of radiotherapy to those of temozolomide in the first five days of each cycle improved overall survival over concomitant radio-chemotherapy according to RTOG 0424. Thus, the proposed treatment schedule could be investigated in a clinical setting to improve combination treatments in LGGs with substantial survival benefits.

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“…In recent years, we [ 9 ] and others [ 10 , 11 , 12 , 13 ] have attempted to connect models capturing the dynamics of tumor growth with the efficacy of RT. Beyond characterizing response to RT, these mechanism-based and machine learning techniques can also be used to predict response [ 9 , 14 ], identify optimal treatment strategies [ 15 , 16 , 17 , 18 ], and evaluate alternative schedules based off different growth assumptions [ 19 ]. In the works of Rockne et al [ 13 ] and Prokopiou et al [ 12 ], the effects of RT were assumed to result in the immediate loss of tumor cells (or reduction in tumor volume) as estimated from the LQ model.…”

Section: Introductionmentioning

confidence: 99%

Towards an Image-Informed Mathematical Model of In Vivo Response to Fractionated Radiation Therapy

Hormuth

Jarrett

Davis

et al. 2021

Cancers

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Fractionated radiation therapy is central to the treatment of numerous malignancies, including high-grade gliomas where complete surgical resection is often impractical due to its highly invasive nature. Development of approaches to forecast response to fractionated radiation therapy may provide the ability to optimize or adapt treatment plans for radiotherapy. Towards this end, we have developed a family of 18 biologically-based mathematical models describing the response of both tumor and vasculature to fractionated radiation therapy. Importantly, these models can be personalized for individual tumors via quantitative imaging measurements. To evaluate this family of models, rats (n = 7) with U-87 glioblastomas were imaged with magnetic resonance imaging (MRI) before, during, and after treatment with fractionated radiotherapy (with doses of either 2 Gy/day or 4 Gy/day for up to 10 days). Estimates of tumor and blood volume fractions, provided by diffusion-weighted MRI and dynamic contrast-enhanced MRI, respectively, were used to calibrate tumor-specific model parameters. The Akaike Information Criterion was employed to select the most parsimonious model and determine an ensemble averaged model, and the resulting forecasts were evaluated at the global and local level. At the global level, the selected model’s forecast resulted in less than 16.2% error in tumor volume estimates. At the local (voxel) level, the median Pearson correlation coefficient across all prediction time points ranged from 0.57 to 0.87 for all animals. While the ensemble average forecast resulted in increased error (ranging from 4.0% to 1063%) in tumor volume predictions over the selected model, it increased the voxel wise correlation (by greater than 12.3%) for three of the animals. This study demonstrates the feasibility of calibrating a model of response by serial quantitative MRI data collected during fractionated radiotherapy to predict response at the conclusion of treatment.

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Radiation protraction schedules for low-grade gliomas: a comparison between different mathematical models

Cited by 8 publications

References 58 publications

The Effect of Radiotherapy on Diffuse Low-Grade Gliomas Evolution: Confronting Theory with Clinical Data

The Effect of Radiotherapy on Diffuse Low-Grade Gliomas Evolution: Confronting Theory with Clinical Data

Optimal Combinations of Chemotherapy and Radiotherapy in Low-Grade Gliomas: A Mathematical Approach

Towards an Image-Informed Mathematical Model of In Vivo Response to Fractionated Radiation Therapy

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