ROC Analysis in Radiotherapy: A TCP Model-Based Test

Santos, Mairon Marques dos; Neves, Ubiraci Pereira da Costa

doi:10.4236/ojapps.2013.32025

Cited by 3 publications

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“…A medida que o número de experimentos cresce, P (t) tende a se aproximar da sua formulação contínua, tal qual descrevem as equações de campo médio [1,14,56]. A rigor, P (t) deve ser calculada para um número M muito grande de simulações [2]:…”

Section: A Tcp De Dawson-hillenunclassified

“…Zaider-Minerbo (ZM) e de Dawson-Hillen (DH). Os resultados mostram que, para tumores de baixa proliferação celular, o uso do modelo Poissoniano para indicação de protocolos de tratamento é tão ecaz quanto o método Monte Carlo ou o uso de modelos mais sosticados (ZM e DH).Na segunda parte da tese, propõe-se um teste estatístico[2], baseado em simulações Monte Carlo do modelo de TCP de DH, para se determinar a capacidade de previsão de erradicação de tumor (cura). Obtem-se a curva ROC do teste a partir das distribuições de probabilidade da fração de células tumorais remanescentes, nas condições de cura ou não-cura.…”

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Probabilidade de controle tumoral: modelos e estatísticas

Santos¹

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Radiobiological modeling allows one to predict the ecacy of radiotherapeutic treatments, specifying protocols and strategies to treat patients with cancer. Many mathematical models have been proposed to evaluate the Tumor Control Probability (TCP). In this thesis we rst present a study in colaboration with researchers at the University of Alberta [1], Canada, in which we compare the TCPs obtained by Monte Carlo simulations and from the Poissonian, Zaider-Minerbo (ZM) and Dawson-Hillen (DH) models. Results show that, for low proliferation tumors, the use of the Poissonian model for indicating the treatment protocol is as eective as the Monte Carlo method or more sosticated models (ZM and DH). in the second part of the thesis, we propose a statistical test [2] based on Monte Carlo simulations of the DH TCP model to determine the prediction capacity of tumor eradication (cure). We obtain the ROC curve of the test from the probability distributions of the remaining tumor cells for conditions of cure and non-cure. Results show that the method can also be applied to clinical data suggesting that the evaluation of the tumor size at the beginning of the radiotherapy leads to a short-term prognosis of the treatment. In the third part of the thesis, we study the surviving fraction (FS) of tumor cells as function of the radiation dose to which they are subjected. In the literature, this surviving fraction has been formulated by the Linear-Quadratic (LQ) model and, more recently, from the Tsallis non-extensive statistics [3]. We evaluate the behaviour of both formulations in terms of the FS ttings to experimental data in the ix x literature (related to cells cultivated in vitro for several tumoral tissues) so that we extend previous studies in the literature. The FS parameters for both formulations are obtained and the quality of the FS ttings to experimental data is compared using the reduced chi-square. Results show that in general both formulations lead to very good FS-curve ttings. Furthermore, we use the Tsallis non-extensive statistics to obtain the ZM TCP as function of the dose, expressing it analitically in terms of the Gamma function (for a dose prole typical of external beam radiation) and the Hipergeometric function (for a dose prole typical of brachitherapy). Finally, the curves of the corresponding TCPs are plotted using experimental data and then compared with TCPs obtained from the LQ model.

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Probabilidade de controle tumoral: modelos e estatísticas

Santos¹

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Simulator of radiation biological effects in tumor in order to determinate the tumor control probability

Frometa-Castillo¹,

Pyakuryal

Piseaux‐Aillon

2019

Informatics in Medicine Unlocked

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Computational simulator that calculates tumor control probability in a tumor heterogeneously irradiated for fractionated radiation oncology treatments

et al. 2021

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Although in the ionizing radiation field many concepts and processes are currently recognized as radiobiological, there are also probabilistic ones, and a probabilistic treatment makes a better understanding about them. The purpose of this study is to develop a new radiobiological simulator that calculates the tumor control probability (TCP) for a tumor heterogeneously irradiated from a fractioned treatment. The three possible types of cells and the results of interactions of ionizing radiation with each cell of a determined volume are analyzed. For an irradiated region with a dose per fraction d, the simulator determines the radiation biological effects using the cell kill ( K) and cell sub-lethal damage, volume, cell density, cell repair of damaged cells during the interfractions, and number of fractions. K is determined from its probabilistic complement, the cell survival ( S), described with the linear-quadratic (LQ) S(d) model as K = 1 − LQ S( d). TCP is calculated from computational simulations as in the ratio of simulations with K = 100% and their total. This application opens new avenues for theoretical and experimental investigations concerning simulations of radiation treatments, and methodologies for therapy optimizations. Our simulator represents a novel methodology as TCP is calculated without analytical formulas, but based on its own probabilistic definition.

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ROC Analysis in Radiotherapy: A TCP Model-Based Test

Cited by 3 publications

References 20 publications

Probabilidade de controle tumoral: modelos e estatísticas

Probabilidade de controle tumoral: modelos e estatísticas

Simulator of radiation biological effects in tumor in order to determinate the tumor control probability

Computational simulator that calculates tumor control probability in a tumor heterogeneously irradiated for fractionated radiation oncology treatments

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