Non-standard radiotherapy fractionations delay the time to malignant transformation of low-grade gliomas

International audienceGrade II gliomas are slowly growing primary brain tumors that affect mostly young patients. Cytotoxic therapies (radiotherapy and/or chemotherapy) are used initially only for patients having a bad prognosis. These therapies are planned following the " maximum dose in minimum time " principle, i. e. the same schedule used for high-grade brain tumors in spite of their very different behavior. These tumors transform after a variable time into high-grade gliomas, which significantly decreases the patient's life expectancy. In this paper we study mathematical models describing the growth of grade II gliomas in response to radiotherapy. We find that protracted metronomic fractionations, i.e. therapeutical schedules enlarging the time interval between low-dose radiotherapy fractions, may lead to a better tumor control without an increase in toxicity. Other non-standard fractionations such as protracted or hypoprotracted schemes may also be beneficial. The potential survival improvement depends on the tumor's proliferation rate and can be even of the order of years. A conservative metronomic scheme, still being a suboptimal treatment, delays the time to malignant progression by at least one year when compared to the standard scheme

Optimized radiotherapy protocols delay the malignant transformation of low-grade gliomas in-silico

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The optimal protocol delays substantially the MT considering the optimal time between fractions Δ_opt for <i>U</i>₀ = 0.3, <i>U</i>_* = 0.5 and <i>ρ</i> ∈ [0.002, 0.01].

<p>(a) Δ_opt and TMT obtained with the optimal protocol. (b) TMT for both the optimal (black curve) and the standard protocols (dark gray curve). The light gray curve represents the differences between their TMTs. The later provides a quantification of the benefit obtained from the optimal fractionation over the standard one.</p

Dependence of the optimal Δ, (Δ_opt) and TMT on the initial and critical tumor cell densities for <i>ρ</i> = 0.005 day⁻¹.

<p>(a) Δ_opt as a function of <i>U</i>₀ and <i>U</i>_*. (b) TMT computed using the optimal Δ_opt(<i>U</i>₀, <i>U</i>_*). The insets show the curves for <i>U</i>₀ = 0.3.</p

Dependence of the TMT and Δ_opt on <i>U</i>_* for <i>U</i>₀ = 0.3.

<p>(a) <i>ρ</i> = 0.01 day⁻¹, (b) <i>ρ</i> = 0.005 day⁻¹. In both cases, the optimal fractionation for each parameter set was used.</p

Tumor amplitude evolution for eight virtual tumors under the effect of the optimal radiation treatment.

<p>(a) <i>ρ</i> = 0.01 day⁻¹, <i>U</i>_* = 0.6. (b) <i>ρ</i> = 0.005 day⁻¹, <i>U</i>_* = 0.6. (c) <i>ρ</i> = 0.01 day⁻¹, <i>U</i>₀ = 0.3. (d) <i>ρ</i> = 0.005 day⁻¹, <i>U</i>₀ = 0.3. (a-b) Show the comparison between two simulations with <i>U</i>₀ = 0.15 and <i>U</i>₀ = 0.3 under optimal therapies. (c-d) Show the comparison between two simulations with <i>U</i>_* = 0.5 and <i>U</i>_* = 0.65 under optimal therapies.</p

The increase of celularity may lead to the malignant transformation of LGGs.

<p>Left and right images are immunohistochemical staining for Hematoxilyn and Eosin for LGG and HGG biopsies respectively.</p

Results for the TMT under different fractionation schemes.

<p>Stars and circles indicate the location of the standard and optimal treatments respectively on the (Dose, Δ) plane and their associated TMT. (a) <i>ρ</i> = 0.01 day⁻¹. Optimal fractionation is <i>d</i>_opt = 0.5 Gy every 6 days (Δ_opt = 6 days), and TMT = 2.9 years. (b) <i>ρ</i> = 0.005 day⁻¹. Optimal fractionation is <i>d</i>_opt = 0.5 Gy and Δ_opt = 16 days TMT = 5.7 years.</p

Comparison of four different fractionation schemes: Optimal fractionation (black line), best protracted scheme obtained for <i>d</i> = 1.8 Gy (grey line), best hypoprotracted treatment obtained with <i>d</i> = 3.2 Gy (light grey) and standard fractionation (dashed line).

<p>In all cases the range 0.002 < <i>ρ</i> < 0.01 was studied. Pannel (a) shows the TMT as a function of <i>ρ</i> and (b) the value of Δ used for each of the schemes.</p

Correction to: Morphological MRI-based features provide pretreatment survival prediction in glioblastoma.

The original version of this article, published on 15 October 2018, unfortunately contained a mistake. The following correction has therefore been made in the original: The name of Mariano Amo-Salas and the affiliation of Ismael Herruzo were presented incorrectly